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					 AN OVERVIEW OF SIGMA-DELTA CONVERTERS

G. S. VISWESWARAN
PROFESSOR
ELECTRICAL ENGINEERING DEPARTMENT
INDIAN INSTITUTE OF TECHNOLOGY, DELHI
NEW DELHI 110 016
Email: gswaran@ee.iitd.ac.in
Telephone: (011) 2659 1077; (011) 2685 2525
                                              1
     DOMAIN OF CONVERTERS



Sigma Delta

     Successive Approx

              Subranging/Pipelined


                         Flash



Signal bandwidth converted

                                     2
               PCM NYQUIST RATE A/D CONVERTERS




E[n] is a sample sequence of a random process
uncorrelated with the sequence x[n].
The probability density of the error process is uniform
over the range of quantization error i.e over /2
The error is a white noise process

                                                   3
              PCM NYQUIST RATE A/D CONVERTERS


The variance of the noise power for a quantization
level  is given by

                                         2        2
                          2
                     2      1  2V      1  2V 
                    Se      N              
                         12 12  2  1 
                                       12  2N 


 This gives us an SNR

                            2
                          Sx             2
                                         Sx 
            SNR  10 log  2   10 log  2   4.77  6.02N (dB)
                         S            V 
                          e               


                                                                    4
               PCM NYQUIST RATE A/D CONVERTERS


In a Nyquist converter, the maximum signal to noise
ratio that can be obtained for a sinusoidal input with a
peak voltage of V is given by:


                 SNR  6.02N  1.76dB

 Every additional bit  6dB of SNR.

eg. Digital audio with signal bandwidth = 20kHz.
    If desired resolution = 18 bits
                         SNR  110dB.
                                                     5
              PCM NYQUIST RATE A/D CONVERTERS


What is the problem with getting 18 bits of resolution ?

1.     Nyquist rate converters essentially obtain output
by comparing the input voltage to various reference
levels. These reference levels are obtained by a
process of reference division; using resistors or
capacitors. Any mismatch in the resistors/capacitors
results in loss of accuracy.
2.   For an „N‟ bit converter, the required matching of
elements is at least 1 part in 2N. Matching of
components to > 10 bits (or > 0.1 %) is difficult.
3.    Nyquist rate converters require a sharp cutoff
anti-aliasing filter.
                                                     6
                OVERSAMPLED PCM CONVERTERS




Oversampled converters attempt to use relatively
imprecise analog components with additional digital
signal processing circuits to achieve high resolution.
This is done using
     Oversampling - the sampling frequency is much
higher than the signal frequency
                                                     7
OVERSAMPLED PCM CONVERTERS




                             8
                OVERSAMPLED PCM CONVERTERS


Noise spectrum when sampled at fS >> 2fB


      Assume     quantization   noise    is    uniformly
distributed, white and uncorrelated with the signal.
      Noise power folds back to –fS/2 to fS/2, 
oversampled converters have lower noise power within
the signal band.
      Out of band noise can be removed by a digital
filter following the PCM converter.


                                                     9
                   OVERSAMPLED PCM CONVERTERS



We define Power Spectral Density of the output
random Process is given by

                            2                             2
      Pxy  Px (f) Hx (f)       and Pey  Pe (f) He (f)

For an oversampled PCM converter |Hx(f)| = |He(f)| = 1.
White noise assumption states that Pe(f) = Se2(f)/fs
which implies Pey(f) = Sey2(f)/fs. Thus the in band noise
power is given by
                  f               f
                                                    2  2f 
                   B               B
           2                                             B
          Sey      Pey (f) df  2  Pey (f) df  Se  f 
                                                          
                  fB               0                  S 
                                                               10
                          OVERSAMPLED PCM CONVERTERS



  We now see that the SNR ratio for this converter is

              S2                  2                2
SNR  10 log  x   10 log 
                                 Sx     
                                           10 log  Sx   4.77  6.02N  3.02OSR (dB)
              S2           S2 2f f             V2 
               ey          e B S                    


   The spectrum of the (over) sampled signal can
   represented as follows:




                                                                                           11
                OVERSAMPLED PCM CONVERTERS




      “16-bit resolution digital audio” Oversampled 8-
bit converter to be used. To get an SNR = 110dB with
fB = 20kHz, we need fS  2.64GHz.
This is still not good enough since the sampling
frequency is too high. Further improvement can be
obtained if noise shaping is used.




                                                     12
                   NOISE SHAPED OVERSAMPLED
                           PCM CONVERTERS


We see that for an A/D converter the output is given in
general by Y(z) = X(z)Hx(z) + E(z)He(z)
We have seen OS PCM converter using | Hx(z)| = | He(z)| = 1.
We can however realize another converter using | Hx(z)| = 1
but choose He(z) to shape the noise spectrum to improve the
noise performance. Noise shaping or modulation further
attenuates noise in the signal band to other frequencies.
The modulator output can be low pass filtered to attenuate
the out of band noise and finally down sampled to get
Nyquist rate samples.

                                                       13
OVERSAMPLED NOISE SHAPING




                            14
                 NOISE SHAPED OVERSAMPLED
                        PCM CONVERTERS

     Noise is high pass filtered to get additional
resolution
     Simplest z- domain high pass filter: 1 –z-1 We
want an output Y(z) that contains the sun of the input
and quantzation noise that is high pass filtered. i.e.
                  Y(z) = X(z) + (1-z-1)E(z)
                           or
                       = z-1X(z) + (1- z-1)E(z)



                                                    15
                    NOISE SHAPED OVERSAMPLED
                         PCM CONVERTERS




            1
         1  z 1

        Analog                         Digital



One possibility is to first integrate the analog input,
quantize it and then high pass filter it.


                                                     16
                 FIRST ORDER  MODULATION


The naïve system proposed has its own problems. The
first problem is that since it is an open loop system,
the integrator will saturate. It also requires matching
between analog and digital portions of the circuit.
         Y(z) = z-1X(z) + (1 – z-1) E(z)
        Y(z)      z 1
                         X(z)  E(z)
       1z  1
                 1z   1


                 z 1       z 1
       Y(z) 1       1 
                                     X(z)  E(z)
             1z  1z
                                   1


                                  z 1
       Y(z)  (X(z)  Y(z))               E(z)
                               1z     1
                                                    17
FIRST ORDER  MODULATION




                            18
                FIRST ORDER  MODULATION



Linearized „z‟ domain model gives
      Hx(z) = STF = z-1
      He(z) = NTF = 1-z-1
 Assuming that the quantization noise is uncorrelated
with the signal,
      Sxy(f) = Sx(f)Hx(f) 2
      Sey(f) = Se(f)He(f) 2



                                                  19
                 FIRST ORDER  MODULATION


If fB<< fS

                         1 2 2 f2
               Sey (f)       4
                         f 12
                          s      f2
                                  s

 Thus we obtain the Noise Power as

                        fB
             Pnoise   Sey (f)df
                        fB

                      ( 22 ) 1
             Pnoise 
                         36 OSR3
                                             20
Taking OSR to be of the form 2r we can obtain the
SNR as

                        2
                      Sx          2 
         SNR  10 log 2   10 log   9.03r (dB)
                     S            3 
                      e           




                                                      21
                     FIRST ORDER  MODULATION




Noise power coming out of First Order Modulator for an OSR of 128.
                                                              22
                 FIRST ORDER  MODULATION




Before we proceed to implement the transfer function
we need to look in to certain realizatios in the sampled
data domain. As the word  implies there is an
integration involved. In the continuous domain, this
requires resistance and capacitance.
As a designer we have the Capacity to Design but not
the Resistance.




                                                     23
                   SWITCHED CAPACITOR CIRCUITS
               DOYEN OF SAMPLED DATA DESIGNS

Sampled Signals:




                                       1  jst
         xs (t)  x(t)  (t  kTs )       e
                      k             Ts k  

 This gives a z transform
                             
                   Xs (z)   x(kTs )z k
                          k  
                                                   24
          Realizing resistors for Sampled Data Circuits




                  i1               i2


 The average value of current i1 or i2 is given by

               1 T /2        1 T /2     1
          i1       i1 dt       dq1  C (V  V2 )
                                             1
               T 0           T 0        T

This emulates a resistance of value R = T/C = 1/fC

                                                       25
OTHER REALIZATIONS OF R




                          26
SWITCHED CAP INTEGRATORS




                           27
                     SWITCHED CAP INTEGRATORS


During 1

      VC (nT  T / 2)  V (nT  T / 2)  V (nT)
                         1                1
            s

 During 2

        Cs V (nT )   CF (Vo ((n  1)T )  Vo (nT )
            1
  Using z transforms, this reduces to

        Vo (z)           (Cs / CF ) z 1
                H(z)                     H1 (z) z 1
        V (z)
         1                 (1  z 1 )
                                                          28
                    SWITCHED CAP INTEGRATORS



If  << 1/T, and using z = exp(jT) we get H(ejT) as


                 j T       Cs 1     1 1
           H(e          )        
                           CFT j    j RCF

This circuit is then an integrator with a delay using
the transformation s = (z-1)/T and is called the
Forward Euler Integrator.




                                                        29
                SWITCHED CAP INTEGRATORS




This is another integrator that gives a non inverting
integration at the output and uses the transformation
s = (1-z-1)/T and is called the Backward Euler
Integrator.
                                                 30
                 SWITCHED CAP INTEGRATORS




The sampling capacitor Cs is now effectively Cs + CP,
thus making the realized resistance R = T/(Cs + CP),
different from the intended value --- needs
correction,    look   for     parasitic  insensitive
configuration.


                                                   31
SWITCHED CAP INTEGRATORS




                           32
                     SWITCHED CAP INTEGRATORS




At 1 Cs gets charged to Vin(nT) and
During 2    Cs (V (nT ))  CF (Vo ((n  1)T )  Vo (nT )
                  in
 Giving us
                Vo (z)          (Cs / CF ) z 1
                        H(z) 
                V (z)
                 1                (1  z 1 )
                                                        33
                  SWITCHED CAP INTEGRATORS




This configuration gives

              Vo (z)           (Cs / CF )
                      H(z) 
              V (z)
               1               (1  z 1 )
                                             34
           BACK TO SIGMA DELTA CONVERTERS




Implementation Imperfection in the first order sigma-
delta modulator


§     Finite op-amp gain
§     Capacitance mismatch
§     Incomplete settling




                                                        35
FINITE OPAMP GAIN




                    36
FINITE OPAMP GAIN




                    37
                                  FINITE OPAMP GAIN


Using charge conservations at the nth clock cycle, we have:
CSVI[n]- CSVd[n] = CF [Vo[n]+ Vd[n] – Vo[n-1] - Vd[n-1]]
           CS                    CS
  V [n] 
    o         V[n]  V [n  1] 
               i      o             V [n]  V [n]  V [n  1]
           CF                    CF d        d       d

  Using Vo[n] = Avd[n] and writing in z domain
                                 CS 1
                                     z
    V (z)
     o                           CF                                        gz 1
                                                                   
    Vin (z)                                                            1  z 1
                                             1               
                                        1                  
                   1     C                 A
              1     1  S   1                            
              
                  A    CF   
                                                       z 1   
                                        1    C
                                           1  S
                                                     
                                                               
                                      1            
                                        A    CF               
                                                              

                                               1
                                           1
                              1                A
   for   CS  CF ,      g          ;    
                                2              2
                             1             1                                       38
                                A              A
                                  FINITE OPAMP GAIN


Output of the modulator is now given by
                         H(z)              E(z)
           Y (z)              X (z) 
                       1  H(z)         (1  H(z))

                      gz 1                         1   z 1
          STF                        and NTF 
                                 1
                  1  (g  )z                    1  (g  )z 1


where NTF denotes the noise transfer function and
STF denotes the signal transfer function,
 NTF „0‟ is shifted away from DC. Neglecting the
effect of the pole in the NTF,

                                                                    39
                            FINITE OPAMP GAIN


                                                  2 f
        | NTF |2  | 1   z 1 |2  e j ,  
                                 z
                                                    fs
                   = 1 +2-2 cos 
                              2
For small        cos   1 
                              2
  Noise power at the output is then
              f                                     f
         1 B 2         2     1 B 2
Pnoise      12 (1  ) df      12  2 df
         f f
          s                   f f
                               s
             B                    B

         1  2        2  2 42 1
             (1  ) 
        OSR 12           12 3 OSR3
                                                         40
                   FINITE OPAMP GAIN

                           2
                     1
                    1            CS
            2        A
     (1  )  1             for    1
                      2           CF
                1 
                     A
                     2
              1 
                    1
             A  
                 2   A2
             1  
                A

        1  2            2 42 1
P se 
 noi          (1  )2 
       OSR 12            12 3 OSR3
        1  2 1    2 4 2 1
                
       OSR 12 A2   12 3 OSR3
                                           41
   EFFECT OF FINITE BANDWIDTH




                  CS
Vo                CF
   
Vi      1     CS  S  CF  CS 
     1   1                 
     
        A
               CF  u  CF 
                               

                                      42
               EFFECT OF FINITE BANDWIDTH


                   CS
             Vo    CF                C
                        Vo (t)  Vi S (1  e ut )
             Vi 1  s                CF
                    u

Larger feedback factor  lower gain  faster setting
 Settling determines maximum clock frequency
             eg: CS = CF = 1pF            = 0.5
 Assume u = 100 MHz
 If we want setting to 1% error, time required 
 14.6ns  clock frequency = 34MHz.

                                                         43
         TIME DOMAIN BEHAVIOUR




Y[n] = Y [n-1] + (X[n-1] – V[n-1])
if     Y[n]  0
       Y[n] = 1.0
else   Y[n] = - 1.0
                                     44
                        TIME DOMAIN BEHAVIOUR



  For example, for a DC input = , the time domain output
  for the first six clock cycles is given by:
                           Y[n]    V[n]
                    0      0.0
                    1      0.33    1

                    2      -0.33   -1

                    3      1       1

                    4      0.33    1

                    5      -0.33   -1

                    6      1       1



It can be seen that the average value of the output is 1/3

                                                      45
               TIME DOMAIN BEHAVIOUR (Non Linear)



§ Quantization error spectrum is not white; successive
output levels may be correlated.
§ Limit cycle oscillations that lead to tones in the output
eg. DC input X[n] = x
For a limit cycle of period T;
             V[n] = V[n+T]
            Y[n] = Y[n+T]
Since the input is DC, the input to the integrator will
also be periodic.
                                                          46
             TIME DOMAIN BEHAVIOUR (Non Linear)



Now Y[n] – Y[n-1] = X – V[n-1].
Write this equation for „T‟ time instances and add; we
get
                          T      T
             Y[T]  Y[0]   X   V[n  1]
                          i1   i1

                              1 T           P N
 but Y[T] = Y [0]         X   V[n  1]      V
                              T i1          T 




                                                      47
             PATTERN NOISE IN  MODULATOR


It should be clear that the  MODULATOR is
expected to give out the output equal to the DC input.
Only limited no. of levels are allowed to the output ,
therefore output has to toggle from one level to
another in order to keep average output equal to the
DC input.
   For eg. Input=0.5 Levels allowed are 0 and 1
Then the output will toggle between 0 and 1. If
average is taken then the value of output of SDM is
0.5.
Therefore the output is oscillating with a frequency
half of that of fs. That means in frequency domain
the output will have tones at fs/2 and fs.        48
                PATTERN NOISE IN  MODULATOR

Similarly for dc level of 1/256, the output will have, one
one and 255 zeroes in 256 clocks (fs) this means the
output will oscillate at a frequency of (fs/256). Hence it
will have tones lying at multiples of this frequency. As the
dc level comes closer to zero the tonal frequency
decreases. The tones are completely harmless till they are
out of the signal bandwidth.
The thing to note over here is that these tones represent
noise as the information or signal is at 0 frequency rest of
the frequency components are noise. This effect is very
much prominent in I order modulators. Another important
fact is that the amplitudes of the tones decrease as they
come closer to the signal bandwidth. It is always better to
analyze them by using simulations.                     49
               PATTERN NOISE IN  MODULATOR




The question to be asked is why are this tones dangerous
in the signal bandwidth? The answer to this question lies
in the fact that all the analysis made earlier on was
based on the white noise approximation and the problem
with the tones is that they are much above the expected
noise floor. Hence the true signal to noise ratio is much
lesser than what was expected from the analysis.




                                                     50
               PATTERN NOISE IN  MODULATOR


It‟s generally said that the pattern noise is visible only
for slow moving inputs (not just DC). To understand
this more clearly assume the input signal is a sinusoid
with an input frequency of fm. If fm is a factor of fs
then every time a new period of the sine wave starts
the SDM will generate the same output as it generated
in the earlier period. This means the output will also be
changing with a frequency of fm. Hence the output will
have tones at the harmonics of the input sinusoidal
signal. If fm is very small then some of these harmonics
will lie in signal bandwidth and the SNR will be lesser
than expected.
                                                      51
              PATTERN NOISE IN  MODULATOR


Pattern Noise Reduces Effective Bits.

 The frequency domain output of the SDM shows
 tones and a noise floor. Consider them this noise to
 be made of two components 1. Tones 2. Random
 noise. Therefore in time domain these tones will give
 rise to impulses (if a large number of tones exist in
 the signal bandwidth). Since there is random noise,
 the impulse train will have a slightly varying
 magnitude but the frequency of repetition will be
 equal to the fundamental frequency. When these
 impulses are of the order of 2 or 3 LSBs. This
 means ENOB is lesser then was expected.
                                                     52
                  SECOND ORDER  MODULATOR




The 2nd order modulator has one delaying and one
non-delaying integrator. Note that the last loop with
the quantizer must have one unit of delay for
stability. The z-domain transfer function of the
second order modulator is given by:
 Y(z) = z-1X(z) +(1-z-1)2 E(z)
        NTF = (1-z-1)2
                                                   53
                   SECOND ORDER  MODULATOR



We can calculate the in band noise power of a second
order  modulator to obtain
                             2    4            5
                            1 
                Pnoise            
                         12 5  OSR 
 Giving us a noise figure of

                        2
                      Sx          4 
         SNR  10 log 2   10 log      15.05r (dB)
                     S            5 
                      e              



                                                          54
SECOND ORDER  MODULATOR




                            55
                   INTEGRATOR OVERLOAD




In second order modulator with a single delaying
integrator, simulations show that the maximum
outputs of the two integrators increase as the signal
level increase. Very often, they are several times the
full scale analog input range. The following table
contains data from simulations. The output levels
indicated are the maximum levels at the output of the
two integrators.



                                                    56
                        INTEGRATOR OVERLOAD

           Input        Ist    integrator   2nd   integrator
           level (dB)   output level        level



           -40          0.33                2.62
           -20          0.96                2.77
           -13.9        0.99                2.8
           -10.45       1.09                3.03
           -7.95        1.22                3.51
           -6.02        1.33                3.99
           -4.43        1.37                4.08
           -3.09        1.49                5.38
           -1.9         1.43                5.21




It is seen that the levels increase as the input value
increases. This reduces the dynamic range of the
modulation since the integrations will now saturate.
The 2nd order modulator can be modified as follows:

                                                               57
                       INTEGRATOR OVERLOAD




The linearized transfer function is
Y(z) = X (z) . z-2 + (1 – z-1)2E(z)
The signal levels at the output of the integrators are
now the following


                                                    58
                               INTEGRATOR OVERLOAD

                  Input        Ist    integrator   2nd   integrator
                  level (dB)   output level        level



                  -40          0.33                2.62
                  -20          0.96                2.77
                  -13.9        0.99                2.8
                  -10.45       1.09                3.03
                  -7.95        1.22                3.51
                  -6.02        1.33                3.99
                  -4.43        1.37                4.08
                  -3.09        1.49                5.38
                  -1.9         1.43                5.21




      The signal levels at the first integrator output is reduced.
However the second integrator output levels are still high.
§    The SNR in the two cases remains the same.
§      The circuit specifications are now more relaxed since there
are two units of delay in the loop.                             59
                     INTEGRATOR OVERLOAD


We need to reduce the output levels in the second
integrator. For this we need to alter the gain just before
the second integrator. Let us see the effect of altering
this gain.




                                                      60
                INTEGRATOR OVERLOAD

                kz1
[X(z)  Y(z)]         1
                            E(z)  Y(z)
                1z
         kz1               1  (1k)z 1 
 X(z)         E(z)  Y(z)              
       1z 1               1z    1    
                                         
             X(z)kz1                E(z)(1  z 1 )
 Y(z)                    1
                                
           1  (1  k )z            1  (1  k)z 1

                 Clock          Output
                 cycle



                 2              1
                 3              1
                 4              -1
                 5              1
                 6              1
                 7              1
                 8              -1

                                                       61
                    INTEGRATOR OVERLOAD



Therefore, even though the linearized transfer
function has changed, there is no change in the actual
output. This is because we have a two level quantizer,
the output of which depends only on the polarity and
not the magnitude of the input. The quantizer
effectively acts as an AGC and makes the overall gain 1.
The second integrator gain can be adjust to reduce
the integrator output levels. Typically it is made less
than one. For a gain of ½, the integrator output levels
are the following

                                                     62
             INTEGRATOR OVERLOAD



Signal       Ist    integrator   2nd   integrator
level (dB)   output level        output level



-40          0.83                0.655
-20          0.96                0.69
-13.9        0.99                0.7
-10.45       1.09                0.75
-7.95        1.22                0.87
-6.02        1.33                0.99
-4.43        1.37                1.02
-3.09        1.49                1.34
-1.9         1.43                1.3




                                                    63
                         CIRCUIT NOISE


The sizes of the input capacitors should be chosen both
on the basis of slow rate as well as thermal noise
considerations. Thermal noise is basically introduced by
non-zero resistance of the sampling switches.
The baseband component of this noise is approximately
proportional to (kT/C)(1/OSR) where „C‟ is the sampling
capacitor. If the OSR = 256, C = 1pF , the noise power
will be 1.625 x 10-11 Joules. The total quantization noise
power in baseband at this OSR, with quantizer levels = 
1 is 5.9 x 10-12 Joules. Choose larger capacitance.



                                                     64
                       SAMPLING JITTER




Sampling Clock Jitter results in non uniform sampling,
increasing total noise power in the quantizer output.
For a sinusoidal input with amplitude A and frequency fx


                                      dX
                  X( t  )  X( t)  .
                                       dt
                   .2f .A cos( 2f t)
                         x           x




                                                     65
                      SAMPLING JITTER



 If the jitter is assumed to be an uncorrelated
 Gaussain random process („white‟), with standard
 deviation t, the average power of this error signal is

                        A2
                   P     (2f )2
                               x
                        2
Since this is assumed to be white, the total error power
in baseband is

                          2 (2f )2
                                 x
                     P 
                          8 OSR


                                                     66
            IMPLEMENTATION IMPERFECTIONS


Supposing the two integrators have the
following transfer functions
                    g1                 g2z 1
                               and
                          1
                1  1z              1  2z 1

                        g1         g z 1
      (X(z)  Y(z))             Y 2 1  E(z)  Y(z)
      
                    1  1z 1     1  2z
                                   
                                          g1g2z 1
       STF 
                1  (1  a2  g2  g1g2 )z 1  1 ( a2  g2 )z 2
                                (1  1z 1 )(1  2z 1 )
      & NTF 
                1  (1  a2  g2  g1g2 )z 1  1 ( a2  g2 )z 2


                                                                      67
                 IMPLEMENTATION IMPERFECTIONS



Assume A1=A2 (the two opamp have the gain). Generally
we can neglect the effect of the denominator and
obtain NTF = (1 – z-1)2
                 2            1 4           2f
          | NTF | | 1  z     | | j ,  
                                  z e        fs
                   (1  ) 4  2(1  )2 2  22

(1-)4 is the unshaped noise, 2(1-)2  2 is the 1st order
shaped noise and 2 4 is the 2nd order shaped noise.
To make sure we get second shaped, we need A  OSR.


                                                       68
      IMPLEMENTATION IMPERFECTIONS


 Attenuation       Maximum        SNR (OSR =
                  Integrator     256) Input = -
                 Output levels        20dB

0.9            5.98, 9.9         70


0.8            3.56, 4.26        83.15


0.7            2.05, 1.86        84.73


0.6            1.29, 0.9         83.59


0.5            0.962, 0.693      86.69


0.4            0.693, 0.625      86.71


0.3            0.510, 0.589      85.93


0.2            0.34, 0.562       77.45

                                                  69
 AD Converter
SIGNAL OUTPUTS OF  MODULATOR




                             71
                        D/A CONVERTER



The sigma Delta D/A converter has a similar topology
to the A/D converter. Here the input digital signal
first goes through an interpolation filter, where it is
upsampled and low pass filtered. After this it is fed
to the modulator. The output of the modulator is a
single bit signal, that comes at rate much higher than
the Nyquist rate. The output of the modulator is
ample and held and low pass filtered to give the analog
output.



                                                    72
 D/A CONVERTER




                   73
                          D/A CONVERTER




The input to the modulator is a 12 bit signal that is
upsampled. The clock rate is much higher than the
Nyquist rate. The modulator is a second order modulator
and the topology is the same as the A/D converter. All
numbers are in the 2‟s complement form. A one bit
quantizer in this case, would simple keep the MSB and
throw out all the other bits. The D/D converter converts
the one bit quantized output to 14 bit positive or negative
                                                      74
number as shown.
AT LAST




          75

				
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