Nano electronic device

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                                         CHAPTER 1

                                   1.1 INTRODUCTION

       Fast paced progress on devising and assembling nanoelectronic devices suggests that it
will be possible to manufacture large scale computation nanofabrics within 10-15 years. Aside
from greatly increasing concerns with complexity and scalability, nanoelectronic fabrics share
a characteristic that will impact the entire design hierarchy. Irrespective of the winning
technologies, e.g., semiconductor nanowires and or carbon nanotubes, it is widely recognized
that devices and interconnect at the nanoscale will exhibit fault densities much higher than
state of the art silicon technology. Indeed, they will have a density of defects which is much
higher than current silicon technologies are likely to be much more susceptible to transient
faults, sometimes referred to as soft faults. The increases are, in part, due to the physical
dimensions being considered. Indeed, from a materials perspective, decreasing the size of
structures increases the ratio of surface area to volume, making imperfections on surfaces or
materials’ boundaries more critical to proper function of nanoscale interconnects and devices.
Furthermore, at such reduced scales, the discrete nature of atomic matter and charge becomes
significant. Namely, a single charge or defect may significantly impact the structural stability
of a nanodevice as well as its sensitivity to the electrostatic environment. These observations
point to a reliability problem that is intrinsic to nanoscale regimes, and is thus here to stay.

     Overcoming this problem will require work at many levels, including devising more
reliable devices, interconnects, manufacturing processes, and materials. At the same time, one
needs to start devising design principles, abstractions, and tools to enable system level
designers to address the projected increases in faults. These will not only be pivotal to
conclusively demonstrating the viability of nanotechnologies, but also critical to moving them
from labs to production. Current design methodologies and tools take high reliability for
granted, and thus, their direct application to designing nanosystems would lead to exceedingly
low yields. A paradigm shift in design methods and tools is thus required, placing defect and
fault tolerance at the forefront, and recognizing these as inextricably tied to system
performance. Moreover given the substantial degree of uncertainty inherent to nanoscale

technologies, design will have to be framed in a probabilistic setting, if effective optimization
of performance, yield, and other key figures of merit is to be achieved. It has been
demonstrated that nanoelectronics technology is well suited to building reconfigurable
computational fabrics. This is significant because reconfigurability provides a powerful tool to
circumvent the uncertainty associated with distributions of defects. However, designing
complex systems to be instantiated through reconfiguration poses a major scalability challenge
defect mapping and configuration must be performed on a per chip basis. This paper proposes
a novel probabilistic design paradigm targeting reconfigurable architected nanofabrics and
shows that it lays a promising foundation towards comprehensively addressing, at the system
level, the density and reliability challenges posed by emerging nanotechnologies. Indeed, first
it provides a hierarchy of design abstractions aimed at ensuring scalability, not only during a
nanosystem’s synthesis, but also in the defect mapping and configuration phases. Scalability
must be jointly addressed across these phases this is one of the innovative aspects of our
approach. Second, the proposed hierarchy is based on abstractions that enable an effective
integration of fault tolerance and defect avoidance techniques in design methodologies. We
will show that this is the key to enabling the design of robust nanosystems. A third innovation
embodied in our approach is that it provides an adequate probabilistic framework in which to
consider critical system level design trade offs between performance, yield and per chip costs.
(Margarida Jacome, et al., 2004).


       Nanoelectronics refer to the use of nanotechnology on electronic components,
especially transistors. Although the term nanotechnology is generally defined as utilizing
technology less than 100 nm in size, nanoelectronics often refer to transistor devices that are
so small that inter-atomic interactions and quantum mechanical properties need to be studied
extensively. As a result, present transistors do not fall under this category, even though these
devices are manufactured with 45 nm, 32 nm, or 22 nm technology. Nanoelectronics are
sometimes considered as disruptive technology because present candidates are significantly
different   from traditional transistors. Some of these candidates include: hybrid
molecular/semiconductor electronics, one dimensional nanotubes, nanowires, or advanced
molecular electronics.
                                                                                                3 Transistor

       A transistor is a semiconductor device used to amplify and switch electronic signals
and power. It is composed of a semiconductor material with at least three terminals for
connection to an external circuit. A voltage or current applied to one pair of the transistor's
terminals changes the current flowing through another pair of terminals. Because the
controlled output power can be much more than the controlling input power, a transistor can
amplify a signal. Today, some transistors are packaged individually, but many more are found
embedded in integrated circuits. Carbon Nanotube

       Carbon nanotubes are allotropes of carbon with a cylindrical nanostructure. Nanotubes
have been constructed with length to diameter ratio of up to 132,000,000:1, significantly larger
than for any other material. These cylindrical carbon molecules have unusual properties,
which are valuable for nanotechnology, electronics, optics and other fields of materials science
and technology. In particular, owing to their extraordinary thermal conductivity and
mechanical and electrical properties, carbon nanotubes find applications as additives to
various structural materials. For instance, in baseball bats, car parts and even golf clubs, where
nanotubes form only a tiny portion of the material.

       Nanotubes are members of the fullerene structural family, which also includes the
spherical buckyballs, and the ends of a nanotube may be capped with a hemisphere of the
buckyball structure. Their name is derived from their long, hollow structure with the walls
formed by one atom thick sheets of carbon, called graphene. These sheets are rolled at specific
and discrete angles and the combination of the rolling angle and radius decides the nanotube
properties; for example, whether the individual nanotube shell is a metal or semiconductor.
Nanotubes are categorized as single walled nanotubes and multi walled nanotubes. Individual
nanotubes naturally align themselves into ropes held together by Vander Waals forces, more
specifically, pi stacking. Applied quantum chemistry, specifically, orbital hybridization best
describes chemical bonding in nanotubes. The chemical bonding of nanotubes is composed
entirely of sp2 bonds, similar to those of graphite. These bonds, which are stronger than the
sp3 bonds found in alkanes, provide nanotubules with their unique strength.
                                                                                             4 Nanowire

       A nanowire is a nanostructure, with the diameter of the order of a nanometer
(10−9 meters). Alternatively, nanowires can be defined as structures that have a thickness or
diameter constrained to tens of nanometers or less and an unconstrained length. At these
scales, quantum mechanical effects are important which coined the term quantum wires. Many
different types of nanowires exist that includes metallic (e.g., Ni, Pt, Au), semiconducting
(e.g., Si, InP, GaN, etc.), and insulating (e.g., SiO2, TiO2). Molecular nanowires are composed
of repeating molecular units either organic (e.g. DNA) or inorganic (e.g. Mo6S9-xIx). The
nanowires could be used, in the near future, to link tiny components into extremely small
circuits. Using nanotechnology, such components could be created out of chemical

               Fig.1. Nanowire used in a Transistor (

1.1.2 Nanoelectronic architectures

       Nanoelectronics offers the potential for much denser circuitry than is possible with
current CMOS technology, but presents a number of challenges that must be addressed if we
are to successfully exploit it. At the size scales we are considering (< 15 nm), conventional
lithography will lack the resolution necessary to create the individual devices normally
combined to create larger circuits. Sensitivity to noise, subatomic particles, and even quantum
uncertainty at this scale will increase the rate of transient faults to the point that redundancy
and fault tolerance will become necessary for logic functions as well as memory. And
regardless of fabrication technique, nanoscale circuits will likely contain defects so numerous
that it would be uneconomical to simply discard circuits containing a single defect some form
of defect tolerance will be necessary to achieve acceptable yields. These challenges have led
us to focus on configurable crossbar architectures where each cross point within a crossbar can
be independently configured to activate an electronic device, such as a resistor, diode, or
transistor. Crossbars are one of the easiest structures to build using nanoimprint lithography.
Their high degree of redundancy offers a simple strategy for defect tolerance. Their regular
structure makes them easy to analyze for devising a strategy for fault tolerance.
Configurability offers the potential for using a single nanoelectronic fabric for a large number
of applications, much like field programmable gate arrays, reducing design costs. The
architectures we describe here are necessarily speculative to varying degrees since many of the
crossbar devices we hypothesize are either not yet functional in the laboratory, or are
functional but incompletely characterized. Configurable nanoscale transistor crossbars, for
example, do not yet exist; consequently we use simplified, idealized models for them.
(Snider, et al., 2005) Photolithography

       The processes used in traditional top down manufacturing at the nanoscale influence all
levels of the design process. In particular, regular layouts are preferred. For example, as
feature sizes become smaller than the illumination wavelength used in photolithography,
resolution enhancement techniques (RETs) must be used to ensure printability. RETs are
already restricting layouts that are manufacturable. Chemical Mechanical Polishing (CMP) is

another example of a process/layout interaction. To maintain a uniform thickness of the wafer,
CMP requires a uniform layout density. The complexity of generating a reliable mask set
which produces reliable chips also limits the ability to create arbitrary patterns of wires. There
is ongoing research into solving these problems; for example, restricted design rules and
process aware routing aim to reduce the impact of RET layout. Dummy features added to
each layer help to make irregular layouts more uniform for CMP. However, as scaling
continues, designs with regular layouts will be favored. One possible solution to these issues
as well as the high cost of mask sets is to share some or all the masks between designs.
Structured ASICs and FPGAs are two possible approaches. A Structured ASIC is based on a
prefabricated set of logic blocks which can be customized by each user. The customization
requires significantly fewer masks than a traditional ASIC. Furthermore, the prefabricated
layers are generally the ones which have the highest critical dimensions, removing the most
expensive masks from the customization process. SRAM based FPGAs are even more regular
and all customization occurs post manufacturing completely eliminating the need for custom
masks. Irregular layouts are a natural byproduct of ASIC or custom design. Making layouts
more regular generally requires more area and hurts performance. However, at the nanoscale
this tradeoff may be worthwhile. Molecular Scale Electronics

       Layout restrictions are even more onerous in bottom up manufacturing processes such
as those used for MSE. The key to understanding the restrictions imposed by MSE is that,
unlike traditional semiconductor manufacturing, the manufacture and assembly of the devices
occur in separate steps. First, the devices are created and then they are assembled into a
system. Due to the scale of the individual devices, the assembly techniques favor regular
patterns. We divide the MSE assembly methods into three broad categories: Probe based
methods, nanoimprint techniques, and self assembly based approaches. Probe based methods
use the tip of a scanning tunneling microscope (STM) or an atomic force microscope (AFM)
to write on a surface. One of the most promising of these methods is dip pen lithography
(DPL), which uses an AFM tip to deliver molecules to a surface. This technique can create
arbitrary patterns, but is limited by the write time of the probe. Parallel versions of DPL have

been demonstrated, but either the line widths increase or the individual probes are not
individually addressable. In the latter case, complex patterns may be generated, but each
pattern must be replicated by all the probes. Nanoimprint techniques use a master to stamp a
pattern directly into the substrate. E beam lithography or DPL would be used to create a
master with irregular patterns. However, issues relating to lift off, master creation, and contact
printing may, for irregular patterns, limit the pitch to 60nm printing by super lattice nanowire
pattern transfer (SNAP), can create parallel arrays of wires with a pitch of <20nm. The SNAP
master is formed by layering through molecular beam epitaxy (MBE) materials of different
hardness. The composite is cleaved and the cleaved edge is used as the master. The softer of
the two materials is partially etched, leaving a parallel set of voids on the cleaved edge. Metal
can be placed in the voids and then contact printed onto a surface. The resulting wires have a
very large aspect ratio. There are a multitude of self assembly based approaches, which
include flow based alignment, electrofield alignment, self assembled mono layers, Langmuir
Blodgett (LB) films, etc. All of these approaches create highly regular assemblies. For
example, 2D meshes of nanowires have been made by combining self assembly and
photolithography. The nanowires are placed in a fluid which is compressed, which in turn
causes the wires to align their long axis. The nanowires are then transferred to another surface.
A second set of aligned nanowires is placed orthogonally on top of the first. Photolithography
is then used to create individual meshes from the two layers. A more deterministic approach to
self assembly uses the hybridization of DNA to guide the assembly process. DNA directed
synthesis proceeds by attaching an unpaired DNA strand to a device and the complementary
strand to another device. The two stands will hybridize assembling the two devices. This
method has been used to join nanoscale devices as well as micronscale devices. A more
complex process based on DNA crossover, has been used to attach a single wall carbon
nanotube FET to a particular location and then use the DNA as a template for the creation of
metallic contacts. Another approach uses the DNA to create a template on which to assemble
other structures. A final example programs self assembly patterns through the use of DNA
tiles. Each tile has multiple single stranded ends that lie in the plane and act as programmable
binding sites. The tiles are designed so that they self assemble into a desired shape, e.g., a
demultiplexor or a 2D mesh. Additionally, the tiles can be functionalized with molecules in
the third dimension allowing them to serve as a scaffold onto which additional structures can

be placed. The MSE fabrication and assembly primitives will most readily create highly
regular structures. Clearly, post fabrication customization is required to implement custom
circuits. By lucky coincidence, a two terminal programmable non volatile switch can be
implemented at a single crossing of two wires. Furthermore, the state of the switch can be
programmed using the same wires as carry the signal during execution of the circuit. Thus, the
area overhead associated with solid state reconfigurable fabrics is not present in their
molecular cousins. Self Assembled NanoStructure

       Although more than one of these possible new devices may prove to be controllable
enough to be used in a new class of electronics, we still must consider Moore's second law,
which deals with the ever increasing cost of an integrated circuit fabrication facility.
Manufacturing facilities currently cost several billion (109) U.S. dollars, and their cost is
projected to continue doubling with almost every generation of smaller devices. The main
costs of the facility are the expensive lithography needed to form ever smaller features and the
requirement for ever cleaner environments to prevent defects. Even one defect in a critical
part of the circuit can destroy an entire chip. To reduce the need for fine lithography, self
assembly techniques are being explored. The basic idea of self assembly in this context is to
use natural forces to form a device feature, although its position may be determined by coarser
lithography. Thus, the less expensive equipment associated with a previous technology
generation may be used, along with self assembly techniques, to fabricate circuits for the next
generation of devices. One technique of forming such nanostructures relies on the stress
resulting from the lattice mismatch of a substrate and a deposited layer. As the layer is
deposited, the atoms in the layer register with the lattice of the underlying material, even
though the intrinsic lattice constant of the depositing material is different. The resulting stress
tends to deform the depositing material, eventually forming three dimensional structures. If
the lattice mismatch is the same in two orthogonal directions, zero dimensional nano islands
form. An example of a nanoisland formed by the deposition of Ge on Si with its 4% lattice
mismatch ( al., 2001).

1.1.3 Defect Tolerance
          Although fine nanowires and nanoislands can be formed by self assembly, a significant
number of the structures formed by any thermodynamically controlled fabrication process will
be defective. The effect of defects can be minimized by basing much of the circuit on a simple
and dense cross bar array architecture, which can be made tolerant of defects. The cross bar
array essentially contains two orthogonal sets of wires separated by a layer that forms the
devices. Wherever two wires intersect, a device is automatically formed. A nonlinear element
e.g., a diode, contained within the device allows efficient programming of individual devices.
However, the device must have a well defined threshold to allow switching of only the desired
element, without affecting other devices in the same row or column of the array. Because the
cross bar array is very regular, its repetitive structure allows it to be configured to avoid
random defects. The defects are located by initially testing the array, and then the array is
programmed to avoid the defects and to accomplish the desired electronic task. Thus, an
architecture based on cross bar arrays can be tolerant of defects by its simple structure and
configurability. The defect tolerant architecture can be used even with conventional devices to
reduce the need for cleaner environments as device features become smaller, and the principle
has been demonstrated (Kamins, et al., 2001) using conventional field programmable gate
arrays. Defect Tolerant Architectures

          For nanoscale crossbars, the main type of defect is that introduced during manufacture
rather than during operation. This is reasonable for plausible technologies, which involve high
temperatures during manufacture, and hence a relative ease of introducing defects, but low
temperature during operation, with much less chance o creating new defects. In this situation,
an appropriate system architecture requires a compiler to arrange for desired circuit behaviors
by using only correctly functioning junctions within crossbars, as determined by a testing
phase after manufacture. This approach of avoiding known defects yields a defect tolerant
architecture. It contrasts with methods dealing with defect induced faults that appear during
operation, perhaps intermittently, e.g. using majority votes from replicated hardware. For
simplicity, we restrict our attention here to defects leading to unconfigurable junctions, rather
than defects the short out a junction or adjacent wires, or that unexpectedly cause a break in a

wire. In this scenario, we can test the circuits to determine which junctions are defective, and
then use the remaining ones to implement the circuit. That is, a compiler takes the required
logic function and a table of defects to find a way to implement that function on the defective
crossbar fabric. This leads to the central question addressed in this section given a defect rate
and a certain size crossbar. Thus, a related problem is the computational difficulty for the
compiler to identify an implementation, or conclude that no implementation is possible.
Decreasing the allowable defect rate for a nanoelectronic fabric will generally require more
difficult and costly manufacturing. Increasing the allowable defect rate will make it less likely
that a desired circuit can be implemented, and can also result in longer run times for the
compiler to identify a way to implement the circuit while avoiding defects. A further aspect of
this problem is that logic functions can often be written in different but logically equivalent

The ripple carry implementation (top) translates directly to a diode crossbar implementation
(bottom) using feedback from some of the outputs to the inputs (gray lines). Regenerative
buffers (left-pointing triangles) between stages regenerate signals degraded by diode and
resistor voltage drops. The input wire marked A0 gives the complement of input bit A0, and
similarly for the other inputs. Note that the carry bit between successive stages of the crossbar
implementation must be presented in both original and complemented forms, Thus, an
architecture based on cross bar arrays can be tolerant of defects by its simple structure and
configurability. The defect tolerant architecture can be used even with conventional devices to
reduce the need for cleaner environments as device features become smaller, and the principle
has been demonstrated (Kamins, et al., 2001) using conventional field programmable gate
arrays. (Snider, Hogg , et al, 2005)

 Figure 2: A 3 bit adder which adds two 3 bit numbers (denoted as the bitsA2A1A0 and
          B2B1B0) to produce a 4 bit sum (S3S2S1S0). (Snider, Hogg , et al, 2005) Fault tolerance

       We distinguish faults, transient perturbations of the circuit that could cause it to
function incorrectly from defects, permanent flaws in the nanofabric. Faults, as we have
defined them, are also referred to as single event upsets (SEUs) or soft errors. They can be
caused by subatomic particles striking a conductor within a circuit, briefly altering the charge,
and hence the voltage, in that conductor. Nanoelectronic structures will be more susceptible to
faults than conventional CMOS since

       (1) Fewer electrons will be used to represent logic states in nanologic circuits,
       increasing their sensitivity to changes in charge.

       (2) Devices will likely have a much greater variability in device parameters,
       reducing voltage safety margins.

       (3) Quantum probabilities will become more visible at the nanoscale, so that devices
       may not behave as reliably or predictably as one would like. (G. Snider, et al., 2005) Von Neumann Multiplexing Based Defect-Tolerance

       The multiplexing based fault tolerance scheme replaces a single logic device by a
multiplexed unit with N copies of every input and output of the device. The N devices in the
multiplexing unit process the copies of the inputs in parallel to give N outputs. Each element
of the output set will be identical and equal to the original output of the logic device, if all the
copies of the inputs and devices are reliable. However, if the inputs or devices are in error, the
outputs will not be identical .The basic design of a von Neumann multiplexing technique
consists of two stages: the executive stage, which performs the basic function of the
processing unit to be replaced, and the restorative stage, which reduces the degradation in the
executive stage caused by erroneous inputs and faulty devices. In the case of the executive
stage for a MAJ multiplexing system the majority gate, and its inputs and outputs are
duplicated N times. The unit U represents a random permutation of the input signals, that is,
for each input set to the N copies of the majority gate, three input signals are randomly chosen
from the three separate input bundles respectively. Also, the restorative stage which is made
using the same technique as the executive stage, duplicating the outputs of the executive stage
to use as its inputs. Note that applying this approach only once will invert the result, therefore
two steps are required. To give a more effective restoration mechanism this stage can be
iterated (Debayan Bhaduri, et al., 2004).

Figure.3. Majority gate implemented with four NAND gates (Debayan Bhaduri, et al.,
2004). R fold modular redundancy

         The concept of TMR is to have three units working in parallel, and to compare their
outputs with a majority gate. Then TMR can provide an assemblage that behaves like one of
its constituent components, but with an improved probability of working. The trade off is that
instead of n devices, at least 3n devices plus a majority gate are needed to make this new unit.
RMR is a generalization of TMR where instead of three we have R units working in parallel.
In our analysis we have assumed a chip with N devices, with the probability pf of an
individual device failing. The probability P fail of a complete chip failing during the working
lifetime is minimized for some optimum cluster size (Nc), under the condition N cpf        1. A
cluster represents the unit which is replicated R times; outputs from each of the units are
compared in a majority gate and the output is determined. It would be a more realistic
approach to fix the logical depth of the system (to say D = 10) and then have determined
values for Nc for each R. However, in the proposed model we calculate the maximum
theoretical improvement offered by this technique. Imperfect majority gates have B outputs
and mB devices. First we assume that a module of Nc devices works only if every single
device in the module works


Now the probability that a module fails, if          1, is           =1–            ≈       .A
group consisting of R modules and a majority gate works correctly when at least (R +1)/2
modules work correctly and when the majority gate also works. The number of devices in a

group is RNc + mB, so the total number of groups is Ngroups = N/ (RNc + mB). The chip
fails if any of the groups fail, hence the probability that the whole chip with N devices fails is

        Here we assume that the errors in each module are uncorrelated, i.e. that common
mode or common cause failures are not present in the redundant system. The equation
        /dNc =0 gives the optimum module size (Nc) for a given pf , which is substituted in
yielding the minimum chip failure probability.

Figure 4. The basic elements of fault tolerant techniques: (a) RMR, (b) CTMR (Debayan
Bhaduri, et al., 2004). Cascaded triple modular redundancy

        The TMR process can be repeated by combining three of the TMR units with another
majority gate to form a second order TMR unit with even higher reliability, a technique called
CTMR. If all three of the units work independently, then the probability of the assemblage,
three units plus majority gate working,     ,is given by


                                                 is given by equation (4).

The performance with additional stages of TMR is obtained by repeated application of this

Figure. 5. The probability of obtaining a defective (TMR/CTMR) (Debayan Bhaduri, et
al., 2004).

        Figure 5 demonstrates the effectiveness of the CTMR technique. It shows that there is
no advantage in using CTMR for units containing a small number of devices, when the
majority gates are made from the same devices as the units that they are monitoring. However,
at least in principle, improvement is possible for units with large Nc.

There are three regions in each set of curves:

        (a) Ncpf > ln 2, where redundancy affords no advantage;

        (b) 10−3<Ncpf < ln 2, where redundancy is most effective; and

        (c) Ncpf < 10−3, where only first order redundancy offers an advantage.

In case (b), the effectiveness of redundancy scales as a power law with the order of CTMR.
The failure probability is for case (c) the effectiveness of redundancy depends on the ratio
mB/Nc.), it can be shown that in region (c) the failure probabilities are
                                                                                               16 NAND multiplexing

        Fifty years ago, von Neumann was the first person to consider the use of redundant
components to overcome the effects of defective devices. He described the now well known
technique of multiple redundancy, but he also described another method, which we have
called NAND multiplexing for brevity.Von Neumann showed that this method could in
principle enable a circuit to work, even if the individual devices had a failure rate of ∼0.01.
The problem with this technique was that it required enormous levels of redundancy. Although
this seems very unrealistic, very high levels of redundancy may be needed for nanocomputers,
since it may be very hard to make huge numbers of nanoscale devices with good reliability.
We have examined whether this technique can be used with smaller levels of redundancy, and
we have also examined its performance for different circuit sizes.

        In essence the basic technique of multiplexing is similar to RMR, but instead of having
a majority gate to decide on the proper output, the output is carried on a bundle of wires, e.g.
for a single bit output one would have R wires in a bundle which carries the output to the next
stage. Therefore, in this method, processing units of any size are replaced by multiplexed units
containing Nbundle number of lines for every single input and output. Essentially, a multiplex
unit consists of two stages. The first, the executive stage, performs the basic function of the
processing unit in parallel. The second, the restorative stage, reduces the degradation caused
by the executive stage and thus acts as a nonlinear amplifier of the output. An example for the
executive stage given in figure 6 is a simple NAND (two input gate), but it could be a unit
with an arbitrary number of gates. Now the signals to and from units are not carried in single
lines but in bundles (Nbundle = 4 in the example in figure 3) and the unit is replicated the
appropriate number of times. If the inputs and processing units are perfectly reliable then the
lines comprising each output should be identically stimulated (1) or unstimulated (0).
However, due to errors in the input data as well as errors occurring in the processing of the
inputs from faulty devices, not all of the output lines in each output will be identically
stimulated. Thus, for multiplexed networks, the final outputs are considered to be 1 if more
than (1 − ) Nbundle lines are stimulated and 0 if less than Nbundle lines are stimulated, where
is a critical level that is pre defined (0 <   < 0.5). Stimulation levels in between are considered
to be undecided.

Figure 6. The basic elements of NAND multiplexing (after von Neumann) fault tolerant
techniques. The specific logic gates shown are only for the purpose of illustration. (Nikoli,
et al., 2002)

Figure 7. The basic structure for the reconfiguration technique theory. The specific logic
gates shown are only for the purpose of illustration. (Nikoli, et al., 2002)

        For the sake of simplifying calculations, von Neumann assumed the most basic logic
gate in a chip to be the NAND gate. This is a universal logic gate and can be used to build the
basic logic gates NOT and NOR. Thus, a NAND network equivalent can replace any
conventional architecture. The use of only NAND gates in extremely large scale integration
(XLSI) architectures may also be justified in that construction is simplified through the use of
identical repeating subunits. (Nikoli, et al., 2002) Successful fault tolerance precedents

        One of the most referenced examples related to fault tolerance of systems with a
medium/high ratio of defective components is the reconfigurable architecture of TERAMAC

designed by HP researchers. In this example, a computer is organized from a reconfigurable
structure made up of around 8 million components The system showed to be efficient to
tolerate 10% of defective programmable cells. The principle of tolerance was based on
reconfigurability; first a whole testing phase determined the set of defective components, later
the systems were reconfigured using healthy components only, avoiding the use of defective
ones. This is one of the principles of the reconfiguration technique, test find replace. The same
technique is used in reconfigurable memories, which integrate sparse components, or even in
multi core systems where in every startup the systems test the cores and cancel the defective or
marginal ones.    In spite of the apparent success of this technique, it cannot be used in
computers that exhibit transient errors as are those caused by noise, the unavoidable source.
The test of the whole systems takes time decades higher than the period of appearance of
transient faults. Another drawback is that the method is not efficient if defect rate is very high,
which is the case, we expect in nanodevice architectures. In the scenario of nanosystems, we
will find a significant number of defects at any level, section or subsection.

1.1.4 Other fault tolerant techniques adequate for transient errors

       Hardware fault tolerant structures implemented through the use of massive redundancy
have been reported previously. The more relevant are: NAND multiplexing, N modular
redundancy (NMR) and averaging cells. NAND MULTIPLEXING technique

       It was introduced by Von Neumann in 1956. The design is based on the
implementation of a functional gate into three stages: executive stage, restorative stage and
output stage. In each one of these stages the redundancy level is given by N (with typical
values of 100 or 1000). Figure 6, shows the results of this technique for three different levels
of redundancy (N= 10, 100 and 1000). Observe that for high values of N the error probability
is limited even for a significant individual gate error rate. However, many publications have
shown that this technique has a limitation, because the system is not able to tolerate global
errors when the individual error rate is higher than 1% approximately.
                                                                                               19 The NMR or TMR technique

        NMR and TMR are based on the use of N tuple or Triple modular redundancy of the
basic system. The system evaluates answers from the N or 3 modules and assumes as good the
one voted in majority. The system is simple and efficient but only for very low error rates as
shown in figure 8.

Figure.8. Error tolerance caused by TMR. The vertical axis is the error rate of the TMR
system and the horizontal the error probability of each one of the three components.
(Nikoli, et al., 2002)

1.1.5 Basic Concept: Reliability, Entropy, and Model of Computation

        It relates information theoretic entropy and thermal entropy of computation in a way so
as to connect reliability to entropy. It has been shown that the thermo dynamic limit of
computation is KbT ln 2, where KbT is the thermal energy and is expressed in normalized
units relative to the logic state energy (Kb is the Boltzmann constant and T is the temperature
in Kelvin). What this means is that the minimum entropy loss due to irreversible computation
amounts to thermal energy that is proportional to this value. If we consider energy levels close
to these thermal limits, the reliability of computation is likely to be affected, and if we can
keep our systems far from the temperature values that might bring the systems close to this
amount of entropy loss, the reliability is likely to improve. This idea is also taken into account

by and a Gibbs distribution based technique is used to characterize the computations by
Boolean gates and networks. NANOLAB automates this non discrete model of computation.
This automation entails parameterized library functions to compute the output distributions
given the input distributions for all the generic logic gates. These can be used to calculate the
probability of the state configurations and entropy values at the primary outputs of any
arbitrary Boolean network.

Figure.9. Generic Triple Modular Redundancy Configuration. (Debayan Bhaduri, et al.,

         The usefulness of such a library and methodology is illustrated by modeling and
computing various reliability figures of merit for interesting defect tolerant architectures. One
of the architectures shown as an illustration is a Triple modular redundancy (TMR)
configuration where three NAND gates work in parallel, and their outputs are compared with a
majority gate. The TMR configuration is shown in Figure 10 and the units in the figure are
NAND gates.

Figure 10. The probability of the TMR output for different values of KbT. (Debayan
Bhaduri, et al., 2004).

The probability of the state configurations of the output of the TMR configuration are plotted
in Figure 10 for different values of KbT. The results obtained by our methodology are
consistent with our analysis. The probability p(z) of the TMR output z for the different state
configurations of z are outlined in Figure 10. As it can be observed, the probability at z = 0 for
different values of KbT is very high and decreases as z approaches 1 in all the cases. This is
because the three NAND gates in the TMR configuration shown in Figure 10 have input
probability distributions where p (inputs=0) = 0.1 and p(inputs=1) = 0.9. Therefore, the output
of the majority logic gate z in Figure 1 has a high probability of being at 0. Also, it is observed
that as the KbT values increase, the likelihood of the occurrence of intermediate values for the
state configurations of the output goes up to the extent that the probability of occurrence of
these values is almost the same as z being logic low or high. (Debayan Bhaduri, et al., 2004).
                                                                                               22 PTM Theory
       In this section, we describe the PTM algebra and some key operations to manipulate
PTMs and compute reliability. First we discuss the basic operations needed to describe circuits
and to compute circuit PTMs from gate PTMs. Next, we define additional operations to extract
reliability information, eliminate variables and handle fanout efficiently. Finally we discuss
how PTMs capture signal correlation and a wide variety of errors. PTM Algebra
       Consider a circuit C with n inputs and m outputs. We order the inputs for purposes of
PTM representation and label them in 0 ... inn−1; similarly, the m outputs are labeled
out 0 ... outm−1. The circuit C can be represented by a 2n×2m PTM M. The rows of M are
indexed by an n bit vector whose values range from 000 ... 0 to 111 ... 1. The row indices
correspond to truth assignments of the circuit’s inputs. Therefore, if i = i0i1 ... in is an n bit
vector, then row M(i) gives the output probability distribution for n input values in0 = i0,
Therefore, each entry in M gives the conditional probability that a certain output combination
occurs given a certain input combination.

1.1.6 A MATLAB Based Tool
       In this section, we discuss how information theoretic entropy coupled with thermal
entropy can be used as a metric for reliability evaluation. We also present the automation
methodology for the NANOLAB tool with a detailed example not only provides a different
non discrete model of computation, in fact, it relates information theoretic entropy and thermal
entropy of computation in a way so as to connect reliability to entropy. It has been shown that
the thermodynamic limit of computation is KT ln2, K is the Boltzmann constant and T is the
temperature in Kelvin, and is expressed in normalized units relative to the logic energy. The
logic energy at a particular node of a Markov network depends only on its neighborhood.
What the thermodynamic limit of computation means is that the minimum entropy loss due to
irreversible computation amounts to thermal energy that is proportional to this value. Due to
the reduced operating voltage levels in nanoarchitectures, the entropy loss can become
significant and reliability may suffer when the computation is carried out close to this thermal
limit of computation. However, as we show by considering various defect tolerant

architectural techniques, that based on the usage of different configuration parameters for
TMR, CTMR etc, the effects of carrying out computation within close thermal limits can be
reduced substantially. NANOLAB consists of a library of functions implemented in
MATLAB. The library consists of functions based on this probabilistic non discrete model of
computation, and can handle discrete energy distributions at the inputs and interconnects of
any specified architectural configuration. We have also developed libraries that can compute
energy distribution at the outputs given continuous distributions at the inputs and
interconnects, introducing the notion of signal noise. Therefore, this tool supports the
modeling of both discrete and continuous energy distributions. The library functions work for
any generic one, two and three input logic gates and can be extended to handle n input logic
gates. The inputs of these gates are assumed to be independent of each other. These functions
are also parameterized and take in as inputs the logic compatibility function and the initial
energy distribution for the inputs of a gate. If the input distribution is discrete, the energy
distribution at the output of a gate is computed by marginalizing over the set of possible values
of the inputs. The output probability distributions are returned as vectors by these functions
and indicate the probability of the output node being at different energy levels between 0 and
1. These probabilities are also calculated over different values of KT so as to analyze thermal
effects on the node. The Belief Propagation algorithm is used to propagate these probability
values to the next node of the network to perform the next marginalization process. The tool
can also calculate entropy values at different nodes of the logic network. It also verifies that
for each logical component of a Boolean network, the valid states have an energy level less
than the invalid states as shown theoretically in. Our tool also consists of a library of functions
that can model noise either as uniform or Gaussian distributions or combinations of these,
depending on the user specifications. The probability of the energy levels at the output of a
gate is calculated by similar marginalizing techniques. Arbitrary Boolean networks in any
redundancy based defect tolerant architectural configuration can be analyzed by writing
simple MATLAB scripts that use the NANOLAB library functions. Also, generic fault
tolerant architectures like TMR, CTMR are being converted into library functions such that
these can be utilized in larger Boolean networks where more than one kind of defect tolerant
scheme may be used for higher reliability of computation.

                             1.2 LITERATURE REVIEW

       Nanotechnology is a field of science and technology that controls matter on a scale that
lies between 1-100 nanometers. Miniaturization is the key to increase performance in
electronics. Atomically Precise Manufacturing is the ability to manufacture materials and
structures at the atomic or molecular size scale. This technology integrates the knowledge and
low manufacturing cost of chemistry with the knowledge and flexibility of engineering. It is
apparent to achieve the highest degree of precision. Embedded systems are increasingly
becoming connected through wireless networking. These devices now form the basis of many
of today’s consumer products including cell phones and video game controllers.

       Nanoelectronics offers the potential for much denser circuitry than is possible with
current CMOS technology, but presents a number of challenges that must be addressed if we
are to successfully exploit it. At the size scale we are considering (< 15 nm), conventional
lithography will lack the resolution necessary to create the individual device normally
combined to create larger circuits. Sensitivity to noise, subatomic particles, and even quantum
uncertainty at this scale will increase the rate of transient faults to the point that redundancy
and fault tolerance will become necessary for logic functions as well as memory. And,
regardless of fabrication technique, nanoscale circuits will likely contain defect so numerous
that it would be uneconomical to simply discard circuits containing a single defect some form
of defect tolerance will be necessary to achieve acceptable yields. Recent years have seen a
dramatic improvement in the size and speed of electronic devices; the exponential pace of
microelectronics is well known.

       Although current trends may continue for some time, inevitable road blocks loom.
Whether or not one can predict with confidence how long he exponential path can be
extended, it makes sense to now explore more radical technologies that could leapfrog
conventional CMOS and enable scaling to continue unhindered down to molecular sizes. It is
helpful to appreciate that current MOS transistors are direct descendants of the
electromechanical switches first used by Zuse to code digital information in an electronic
form. Representing binary information by turning on or off a current switch has been one of

the most fruitful ideas in the history of technology. This paradigm does however have serious
drawbacks as device sizes are reduced. The interconnect problem is one. One needs to
distribute signals over large distances, which involves charging long lines. Remarkable
complexity attends the routing of signals on multiple levels. At the other end, as transistors
become smaller, the quantization of charge both in the channel and in the doping layer
becomes significant. When reduced to nanometer scales, current switches may not be the best
way to code information. (lent, et al., 2005).

       Today´s most advanced integrated technology shows a miniaturizing level
characterized by a manufacturing critical dimension of 32 nm and decreasing. Following the
scaling down of components predicted by Gordon Moore. We have seen a continuous
miniaturization of the device sizes that in the last decade was located around the 350 nm
generation. This last decade, from 350 to 90 nm nodes, the generations have been called the
Pre Giga transistor era. From 90 to 22 nm (a predicted feasible technology today) is called
Giga transistor era and from 22 nm to the ultimate feasible size (presumably 12or 6 nm) is
called the Tera transistor era. (Carmen García, et al., 2004).

       Physicists and chemists are starting to demonstrate a variety of sub 10nm electron
devices which can be fabricated bottomup without relying on nanoscale patterning, for
example by self assembly of pre synthesized molecules from solution. Simultaneously,
scientists and engineers are beginning to demonstrate a number of techniques for building and
engineering wires with nanometer scale widths. These devices are emerging just as we are
encountering increasing challenges in the control, variability, and reliability as we scale our
traditional, top down CMOS fabrication techniques. These encouraging developments have
stimulated intense work on architectures for future nanoelectronic circuits. Some of these
architectures are based on the use of active nanodevices alone. However, these proposals must
either deal with of signal restoration and device isolation challenges in two terminal devices or
the self assembly challenges of molecular three terminal nanodevices (e.g., transistors). This is
why one of the most promising solutions is to combine nanoscale devices and nanowires with
larger CMOS components. The CMOS subsystem can also serve as a reliable medium
connecting the nanoscale circuit blocks and providing I/O functions. This paper gives a brief

review of such hybrid CMOS/ nanowire/ nanodevice integrated circuits. We start with a
discussion of possible active and passive components of such circuits in order to show that
they create a new set of constraints and issues to address at circuit design, essentially requiring
a completely new design mindset. We then discuss several hybrid circuit architectures as
examples of how these issues could be addressed .Together, these new issues and architectures
present a new set of challenges for design automation (Dehon, Likharev et al., 2004).

       The first ACS architecture is called the MT ACS architecture. The MT ACS
architecture is an extension of the conventional ACS architecture and contains a
multi threaded processor, memory, I/O devices and a reconfigurable coprocessor connected to
the bus. For a given application, a part of the application will be executed on the processor
and the remaining part will be implemented on the reconfigurable coprocessor. For the
portions of the applications mapped on the reconfigurable hardware, CED, fault location and
recovery techniques described in can be used. Multi threading can be used to implement fault
tolerance in the processor. Fault tolerance is accomplished by using multiple threads of
computations and algorithms. With the MT ACS architecture, recovery from permanent faults
in the processor requires board swapping or replacement of the microprocessor chip. Thus, the
MT ACS architecture needs human intervention for recovery and may not be suitable for
unmanned applications. (Subhasish Mitra et al., 2000).

       The paper which follows is based on notes taken by R.S. Pierce on five lectures given
by the author at the California Institute of Technology in January 1952. They have been
revised by the author, but they reflect, apart from stylistic changes, the lectures as they were
delivered. The author intends to prepare an expanded version for publication, and the present
write up, which is imperfect in various ways, does therefore not represent the final and
complete publication. The neurological connections may then also be explored somewhat
further. The present write up is nevertheless presented in this form because the held is in a
state of rapid mux, and therefore for ideas that bear on it an exposition without too much delay
seems desirable. The analytical table of contents which precedes this will give a reasonably
close orientation about the contents indeed the title should be fairly self explanatory. The
subject-matter is the role of error in logics, or in the physical implementation: of logics in
automata synthesis. Error is viewed, therefore, not as an extraneous and misdirected or

misdirecting accident, but as an essential part of the process under consideration its
importance in the synthesis of automata being fully comparable to that one of the factor which
is normally considerd, the intended and correct logical structure (J. von Neumann, et al., 1952)

       New technologies for building nanometer scale devices are expected to provide the
means for constructing much denser logic and thinner wires. These technologies provide a
mechanism for the construction of a useful Avogadro computer that makes efficient use of
extremely large number of small devices computing in parallel in the presence of defects and
uncertainty. But the economic fabrication of complete circuits at the nanometer level remains
challenging because of the difficulty of connecting nanodevices to one another. Also, the
shrinking feature size will make the defect density control very expensive; in the near future
we will be unable to manufacture large, defect free integrated circuits. Thus designing reliable
system architectures that can work around these problems at run time becomes important.
Also, at the nanometer scale, the focus of micro architecture will move from processing to
communication. General computer architectures till date have been based on principles that
differentiate between memory and processing and rely on communication over buses.
Nanoelectronics promises to change these basic principles. Processing will be cheap and
copious, interconnection expensive and prevalent. This will tend to move computer
architecture in the direction of locally connected, redundant and reconfigurable hardware
meshes that merge processing and memory. At the same time, due to fundamental limitations
at the nanoscale, micro architects will be presented with new design challenges. For instance,
the methodology of using global interconnections. And assuming error free computing may no
longer be possible. Due to the small feature size, there will be an extremely large number of
nanodevices at a designer’s disposal. This will lead to redundancy based defect tolerant
architectures, and thus some conventional techniques such as TMR, CTMR and multistage
CTMR may be implemented to obtain high reliability. However, too much redundancy does
not necessarily lead to higher reliability, since the defects affect the redundant parts as well.
As a result, in depth analysis is required to find the optimal redundancy level for each specific

       Exhaustive analysis of the NAND multiplexing technique in shows that the arbitrary
augmentation of unreliable devices could result in the decrease of the reliability of an

architecture. This meansthat for each specific architecture and a given failure distribution of
devices, once an optimal redundancy level is reached, any increase or decrease in the number
of devices may lead to less reliable computation. Also note that redundancy may also be
applied at different levels of granularity, such as gate level, logic block level, functional unit
level etc. Our work shows that deciding the correct granularity level

       For a specific Boolean network is crucial in achieving an optimal redundancy level.
We have developed an automated tool for evaluating reliability of various alternative defect
tolerant architectures for any arbitrary Boolean network. This probabilistic model checking
based tool named NANOPRISM can automatically evaluate reliability in terms of redundancy
and granularity levels and most importantly show the trade offs and saturation points. By
saturation point we mean the granularity based redundancy vs. reliability reaches a plateau
meaning that there cannot be any more improvements in reliability by increasing redundancy
or granularity levels (Debayan, et al., 2004).

1.2.4 Defect tolerant computing

       In 1952, von Neumann studied the problem of constructing reliable computation from
unreliable devices, introducing a redundancy technique called NAND multiplexing. He
showed that, if the failure probabilities of the gates are sufficiently small and failures are
independent, then computations may be done with a high probability of correctness. Later, it
was shown that a logarithmic redundancy is necessary for some Boolean function
computations, and sufficient for all Boolean functions. Pippenger showed that von Neumann’s
construction works only when the probability of failure per gate is strictly less than 1/2, and
that computation in the presence of noise, requires more layers of redundancy, NAND
multiplexing was compared to other techniques for fault tolerance and theoretical calculations
showed that the redundancy level must be quite high to obtain acceptable levels of reliability.
Formally, a defect tolerant architecture is one which uses techniques to mitigate the effects of
defects in the devices that make up the architecture, and guarantees a given level of reliability.
(Gethin Norman, et al., 2005).

       New technologies for building nanometer scale devices are expected to provide the
means for constructing much denser logic and thinner wires. But the economic fabrication of

complete circuits at the nanometer level remains challenging because of the difficulty of
connecting nanodevices to one another. Also, the shrinking feature size will make the defect
density control very expensive; in the near future we will be unable to manufacture large,
defect-free integrated circuits. Thus designing reliable system architectures that can work
around these problems at run-time becomes important. Due to the small feature size, there will
be an extremely large number of nanodevices at a designer’s disposal. This will lead to
redundancy based defect-tolerant architectures, and thus some conventional techniques such as
Triple Modular Redundancy (TMR), Cascaded Triple Modular Redundancy (CTMR) and
multistage iterations of these may be implemented to obtain high reliability. We have
developed tools to evaluate such reliability redundancy trade offs of different redundant
architectural configurations for arbitrary Boolean networks. In particular, we describe a
MATLAB based tool called nanolab, and a probabilistic model checking based tool named

       Recently, a probabilistic model of computation based on Markov Random Fields
(MRF) has been proposed in that introduces a new information encoding and computation
scheme where signals are interpreted to be logic low or high over a continuous energy
distribution. The inputs and outputs of the gates in a combinational block are realized as nodes
of a Markov network and the logic function for each gate is interpreted by a Gibbs distribution
based transformation. Nanolab automates this probabilistic design levels. It consists of
libraries built on prism for different redundancy based defect tolerant architectural
configurations. These libraries also support modeling of redundancy at different levels of
granularity, such as gate level, logic block level, logic function level, unit level etc (Debayan
Bhaduri, et al., 2004).

       As CMOS is approaching its physical limit, nanoscale devices such as Resonant
Tunneling Diodes, Quantum dot Cellular Arrays and molecular electronics have been
proposed due to their drastically improved operating speeds, low power consumption and
device densities reaching 1012device / cm2. Consequently, defect and fault tolerance has
assumed paramount importance as a design objective in the emerging nanoelectronic
environment. Related research in defect tolerance in the nanoelectronic systems includes
system level approaches and logic level solutions. These approaches reconfigure the redundant

hardware to bypass the permanent faulty units. To deal with transient faults and provide fault
tolerance capability, the N Module Redundancy and NAND multiplexing can be applied at the
logic gate level. These simple hardware redundancy based schemes at the logic and lower
levels require an immense number of redundancies to tolerate the large failure rates in the
emerging nanotechnologies. research approaches in architectural level fault tolerance schemes
for processors based on CMOS technology mainly deal with a low and relatively fixed fault
rate and are not applicable to the nanoelectronic environment.

       Essentially, computational models for computing system architectures based on
nanoelectronic devices should satisfy two core requirements. First, correctness of
computations is a fundamental requirement. The overall system should operate reliably even
though the underlying nanotechnology is unreliable. A second requirement is to implement a
high performance system. The large number of computation units should be used to
dramatically speed up system performance. In doing so, several unique challenges need to be
addressed including (i) How to translate the speed up afforded by nanoelectronic devices into
high performance at the system level and (ii) How to organize the abundant computational
resources to trade off fault tolerance against system performance in the presence of high and
time varying failure rates. In this paper, we propose a fault tolerance computational model for
the nanoelectronic processors. Essentially, the correctness of every instruction is conrmed by
multiple execution instances through hardware redundancy complemented by time redundancy
approach. To achieve system performance, multiple computation branches can proceed in
parallel in a speculative manner. Hardware resource growth in the speculative computations is
controlled so that performance boost does not occur at hardware expense. An instance of the
proposed computation model can be found in. We set up an experimental framework to
validate the effectiveness of the proposed approach and investigate the variations of the
algorithm under different parameters.

       Experimental data confirm that, both in terms of hardware and in terms of latency
aspects, the proposed computation model can provide fault tolerance for pipelined instructions
even under a high and variable fault rate and can achieve high system performance with low
hardware overhead. The experimental results further show that, for different fault rate ranges,
a solution quite close to the optimal can be achieved through the proper selection of

parameters in the proposed computation model. The paper is organized as follows. A
description of the proposed fault tolerance computation model, with the hardware allocation
algorithm for a pipelined architecture provided. Experimental setup, results and analysis are
provided (Wenjing Rao, et al., 1999).

                                        CHAPTER 2

                               2.1 AIM AND OBJECTIVES

The main aim of this work is

    To evaluate the reliability of defect tolerance in nanoelectronic devices.

The objectives of the work includes

                     Design of a nanoelectronic device
                     Analysis for error detection
                     Correction by using simulation techniques.
                     Simulating the device using MATLAB



   Hazard calculation

   Entropy calculation

Redundancy calculation

Defect tolerance calculation

                          2.3 MATERIALS AND METHODS

2.3.1 Granularity and Measure of Redundancy

       Redundancy based defect-tolerance can be implemented for Boolean networks at
different levels of granularity. For a specific logic circuit, all the gates could be replicated as a
particular CTMR configuration and the overall architecture can be some other CTMR
configuration. For example, each gate in a logic circuit could be a kth order CTMR
configuration and the overall circuit could be a nth order configuration where k 6= n. The
different levels of granularity at which redundancy can be applied are gate level, logic block
level, logic function level etc. We discuss some interesting results later that show us that
reliability is indeed dependent on the granularity level at which redundancy is injected in a
specific Boolean network.

2.3.2 Modeling Single gate TMR, CTMR
       We explain the PRISM model construction of a single gate TMR, CTMR and
multistage iterations of these. The first approach is directly modeling the system as given in
Figure 1. For each redundant unit and the majority voting logic, construct separate PRISM
modules and combine these modules through synchronous parallel composition. However,
such an approach leads to the well know state space explosion problem. At the same time, we
observe that the actual values of the inputs and outputs of each logic block is not important,
instead one needs to keep track of only the total number of stimulated (and non stimulated)
inputs and outputs. Furthermore, to compute these values, without having to store all the
outputs of the units, we replace the set of replicated units working in parallel with ones
working in sequence. This folds space into time, or in other words reuse the same logic unit
over time rather than making redundancy over space. This approach does not influence the
performance of the system since each unit works independently and the probability of each
gate failing is also independent. Also, different orders of CTMR configurations are built
incrementally from the models of the previous iterations. In this case too, two approaches
seem to emerge. One of the approaches is incorporating PRISM modules of the previous
CTMR iteration as the redundant functional units for the current order and adding a majority

voting logic. This causes the model to grow exponentially as the higher orders of
configuration are reached. The other approach is to use already calculated probability values
(probability of being a logic low or high) at the output of the last CTMR configuration as the
output probability distributions of the three redundant functional units of the current order of a
CTMR configuration. Let us consider a TMR configuration of a NAND gate. For a given
input probability distribution and failure distribution of the NAND gates, the PRISM model
checks whether the output of the configuration is in an invalid state and computes the
probability of being in that state. This gives the output distribution of the TMR including the
invalid state configurations. A CTMR configuration uses three TMR logic units and a majority
voter. The probability distribution obtained for the TMR block can be used directly in this
configuration, thus reducing the state space. The NANOPRISM libraries are parameterized
and different input and error probabilities can be plugged in to check for reliability measures.

2.3.3 Tools Involved

                      Nanoprism
                      DTMC based libraries
                      Digital Electronics Tools MATLAB
       MATLAB is a high level technical computing language and interactive environment
for algorithm development, data visualization, data analysis, and numeric computation. Using
the MATLAB product, we can solve technical computing problems faster than with traditional
programming languages, such as C, C++, and FORTRAN. We can use MATLAB in a wide
range of applications, including signal and image processing, communications, control design,
test and measurement, financial modeling and analysis, and computational biology. Add on
toolboxes, collections of special purpose MATLAB functions, available separately extend the
MATLAB environment to solve particular classes of problems in these application areas.

       MATLAB provides a number of features for documenting and sharing our work. We
can integrate our MATLAB code with other languages and applications, and distribute our
MATLAB algorithms and applications.

                                          CHAPTER 3

                         3.1 RESULTS AND DISCUSSION

3.1.1 Reliability and Bit transfer of a traditional NAND Gate and Nano NAND Gate

       The bit transfer analysis was performed for that of a traditional NAND gate and nano
NAND gate. The output probability distribution of a NAND gate is asymmetrical. At the end
of the analysis, it was observed that the performance of a nano NAND gate was superior to the
traditional NAND gate. The elapsed time for the traditional NAND gate is 0.000102 seconds
and 0.000035 seconds for nano NAND gate.

       Figure 11. Bit transfer of a traditional NAND Gate and Nano NAND Gate

       It is found that the traditional NAND gate took 8.4 seconds to transfer an eight bit data,
whereas it was only 2.4 seconds with the nano NAND gate. With the nano NAND gate, data
transfer is faster and hence the Reliability of Defect Tolerant is high.

Figures 11 shows the entropy curves at the outputs for a single NAND gate and a CTMR
configuration at different KbT values. The output probability distribution of a NAND gate is
asymmetrical. This should be expected since only one input combination produces a logic 0 at
the output.

Figure 12. Entropy of a CTMR NAND configuration when inputs are uniformly distributed

Figure 12 shows the entropy curves for different orders of a NAND CTMR configuration at
different KbT values. The inputs in this case are uniformly distributed. It can be observed that

the 0th order CTMR has higher entropy value less logic margin than the 1st order CTMR at
lower KbT values and in both cases the entropy values almost converge at KbT = 0.5. At this
point logic margins at the output of the CTMR become insignificant and there is total disorder.
Computation becomes unpredictable. The entropy shoots up at a KbT value of 0.25 indicating
that the optimal redundancy level for a NAND CTMR configuration is the 1st order. This
aptly shows that every architecture has an optimal redundancy level.

Figure 13. Entropy of a CTMR NAND configuration when inputs are logic high with 0.8

Figure 13 indicates the entropy values when the inputsare non-uniformly distributed with a
probability of 0.8 to be logic high. In this case too, it is observed that the 1st order CTMR is

the optimal redundancy level for the configuration. The entropy values values are higher than
for all values of KbT. This is because the input distribution being at a higher probability of
being a logic high implies that the output of the NAND gate will be a 0 and only one input
combination can cause this to happen i.e. when both inputs are high. Thus, the logic margins
are a bit reduced and the entropy has higher values than the previous experiment.

                             Figure 14: Entropy of NAND Gate

Figure 14 indicates the entropy values when the inputs are non-uniformly distributed with a
probability of 0.8 to be logic high. In this case too, it is observed that the 1st order CTMR is
the optimal redundancy level for the configuration. The entropy values are higher than Figure
12 for all values of KbT. This is because the input distribution being at a higher probability of
being a logic high implies that the output of the NAND gate will be a 0 and only one input
combination can cause this to happen i.e. when both inputs are high. Thus, the logic margins
are a bit reduced and the entropy has higher values than the previous experiment.

                                    3.2 CONCLUSION

       In this work, two automation methodologies that can expedite reliability evaluation of
defect tolerant architectures for nanodevices is simulated. In particular, nanolab that is built on
MATLAB, and a probabilistic model checking based tool called nanoprism is made use of.
These tools can determine optimal redundancy and granularity levels for any specific logic
network, given the failure distribution of the devices and the desired reliability of the system.
These can also be used to inject signal noise at inputs and interconnects and create practical
situations the circuits are subjected to during normal operation. nanolab applies an approach to
reliability analysis that is based on Markov Random Fields as the probabilistic model of
computation. Whereas, nanoprism applies probabilistic model checking techniques to
calculate the likelihood of occurrence of probabilistic transient defects in devices and
interconnections. Thus, there is an inherent difference in the model of computation between
these two approaches.

                                    3.2 REFERENCES

1. Andr´e DeHon, Konstantin K. Likharev (2004) “Hybrid CMOS/Nanoelectronic Digital
   Circuits, Devices, Architectures, and Design Automation.” Journal of nanotechnology .Vol
   54, pp. 152-158
2. Andr´e DeHon (2008). “Defect and fault tolerance” journal of Electrical and system
   engineering. Vol. 37, pp. 830-852.
3. Carmen García, Francesc Moll, Antonio Rubio. (2004) ‘Electronic System Design
   Paradigms In The Technologies Of The Year 2020. Journal of Faulty of
   Telecommunication. Journal of IEEE Vol. 85, pp. 541-557
4. Craig S. Lent and P. Douglas Tougaw. (2008) ‘A Device Architecture for Computing
   with Quantum Dots’. Vol 54, pp. 152-158
5. Debayan Bhaduri, Sandeep K. Shukla,Paul S . Graham, Maya Gokhale,, (2005),
   “Reliability/Redundancy Trade off Evaluation for Multiplexed Architectures Used to
   Implement Quantum Dot Based Computing.’ journal of Fermat lab Vol. 121, pp. 1-16.
6. Debayan Bhaduri, Sandeep Shukla, (2003) “NANOPRISM: A Tool for Evaluating
   Granularity vs. Reliability Trade offs in Nano Architectures”. journal of Fermat lab Vol.
   54, pp. 52-59
7. Debayan Bhaduri, Sandeep Shukla, (2005), “Comparing Reliability Redundancy Trade
   offs for Two Von Neumann Multiplexing Architectures”. Vol. 54, pp. 15-19
8. Debayan Bhaduri, Sandeep Shukla, Heather Quinn, (2004), “Reliability Driven
   Probabilistic Design Paradigm for Transient Error Tolerant Architectures on Nanofabrics”.
   Vol. 54, pp. 25-29
9. Debayan Bhaduri, Sandeep K. Shukla, (2008) “Probabilistic Analysis of Self Assembled
   Molecular Networks” Vol. 5, pp. 123-151
10. Debayan Bhaduri, Sandeep Shukla, (2004), ‘Tools and Techniques for Evaluating
   Reliability of Defect Tolerant       Nano Architectures’ journal of Fermat lab. Vol. 96,
   pp. 40-46
11. Debayan Bhaduri, Sandeep Shukla, (2004) ‘Reliability Analysis of Nano Architectures in
   the Presence of Thermal Perturbations and Signal Noise.’ journal of Fermat lab Vol. 51,
   pp. 152-158

12. Gethin Norman, David Parker, Marta Kwiatkowska, (2005) ‘Evaluating the Reliability of
   Defect Tolerant Architectures for Nanotechnology with Probabilistic Model Checking’.
   Vol. 78, pp. 15-19
13. James R. Heath; Philip J. Kuekes; Gregory S. Snider; R. Stanley Williams, (1998) ‘A
   Defect Tolerant Computer Architecture: Opportunities for Nanotechnology.’ Journal of
   Jstore. Vol. 280, pp. 1716-1721
14. Kamins and R. Stanley Williams, (2001). ‘Trends in Nanotechnology: Self Assembly and
   Defect Tolerance’. Journal of Nanotechnology. Vol. 5, pp. 111-118
15. Marta Kwiatkowska, David Parker, Yi Zhang. (2003) ‘Dual Processor Parallelisation of
   Symbolic Probabilistic Model Checking’. In: School of Computer Science, University of
   Birmingham, Edgbaston, Birmingham. Vol. 14, pp. 152-171
16. Marta Kwiatkowska, Gethin Norman, David Parker, (2004), ‘Probabilistic Symbolic
   Model Checking with PRISM: A Hybrid Approach’. Journal of Vol. 71, pp. 1501-1511
17. Margarida Jacome, Chen He, Gustavo de Veciana, and Stephen Bijansky, (2004), “Defect
   Tolerant Probabilistic Design Paradigm for Nanotechnologies. Journal of Jstore”.Journal
   of Computer Engineering. Vol. 19, pp. 15-25
18. Marta Kwiatkowska, Gethin Norman, David Parker, (2004), ‘Probabilistic Symbolic
   Model Checking with PRISM: A Hybrid Approach’ Vol. 18, pp. 251-271
19. Mike Butts. (2002) “Survey of Nanoscale Digital System Technology”. Journal of Nano
   circuits Vol. 12, pp. 1-6
20. Nikoli´c, A Sadek and M Forshaw, (2002) “Fault tolerant techniques for Nanocomputers”.
   Journal of Nanotechnology. Vol. 13, pp. 357-362
21. Sandeep K. Shukla, Ramesh Kam, Seth Copen Goldstein, Forrest Brewer, (2004) ‘Nano,
   Quantum, and Molecular Computing: Are we ready for the Validation and Test
   Challenges.” Journal of Quantum computation. Vol. 6, pp. 3-7
22. Smitha krishnasawamy, George (2008) “probabilistic Transfer Matrices in Symbolic
   Reliability Analysis of logic Circuits.” Journal of Design Automation of Electronic
   Systems, Vol. 13, pp. 8.1-8.35.
23. Snider, P. Kuekes, T. Hogg, R. Stanley Williams, (2005) ‘Nanoelectronic architectures’. .
   Journal of Materials Science and Processing. Vol. 80, pp. 1183-1195.

24. Stan Z. Li, (1995) ‘Markov Random Field Models for pose Estimation in object
   Recognition’. Vol.17, pp. 151-158
25. Tian Ban, Lirida Naviner, (2011) ‘Progressive module redundancy for fault-tolerant
   designs in nanoelectronics’. Journal of Jstore. Vol. 41, pp. 14-29
26. WeiLu and Charles M. Lieber, (2007) ‘Nanoelectronics from the bottom up’.
27. Wenjing Rao, Alex Orailoglu,Ramesh Karri, (2005), ‘Architectural Level Fault Tolerant
   Computation in Nanoelectronic Processors.’Journal of Nanotechnology . Vol. 11, pp. 1-8

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