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Relationship Between in-situ Information and ex-situ Metrology in Metal Etch Processes Jill Card, An Cao, Wai Chan, Bill Martin, Yi-Min Lai IBEX Process Technology, A division of NeuMath, Inc Outline ● Background What we want for APC The current situation in IC fabrication ● Project Overview Product design Data collection Model structure ● Results Background Ideal Semiconductor Fabrication: Processes running on target Continuous process monitoring and control at the tool level Impending scrap events immediately detected and prevented Advanced Fault Detection Reliable Root Cause Analysis Heads-up for tool failures Pinpoint problems and advise maintenance actions High Yield by coordinating different steps and processes Current Fabrication Situation 5 Chart “Violates” 6 Lot Goes on Hold 4 Yellow Light On Tool SPC Chart Data to Process MT Takes Action 2 Lot Moves to Measurement Tool Delay! 1 3 Lot is Measured Lot is Processed Production line may be running for 5 lots with scraps before scraps are detected – at a cost of $$$ per lot. Solution? 5 Chart “Violates” Tool SPC Chart In-situ data is readily ex-situ data enhances available, no delays the model NN Model Predicted ex-situ 2 Lot Moves to Meas. Tool 1 Lot is 3 Lot is Measured Processed The Proposal Suppose We can build a map between in-situ information and ex-situ metrology, then we can use in-situ data to predict the wafer quality directly, thereby avoiding the metrology delay. Direct benefits Real time monitoring of wafer quality Predictions available for every single wafer Avoid delay in detection of major scrap events Take advantage of increasing availability of in-situ data, e.g. sensor data. Potentially reduce ex-situ measurement cost Experiments We seek answers to these questions: Can we accurately predict ex-situ information using in-situ results? If yes, is there a relationship that can be easily interpreted? Data Collection ● Production data from Metal Etch process 4 months of data, total = 30K records. About 1.3K records have ex-situ information collected. ● Modeling one critical etch step ● Inputs includes feed-forward metrology information from the previous steps. Neural Network (NN) Models • Neural Network modeling was chosen because the relationship between in-situ and ex-situ metrology is hard to formulate mathematically. • NN learns the rules from the dataset itself, no prior knowledge is required. • IBEX Dynamic Neural Controller [commercial software package] was used. • Separate neural network models are built for each ex-situ metrology measurement. Model Inputs vs. Outputs TCP RF Forward Power Bias RF Forward Power Temperature Upper Sense Temperature Bottom Electric Sense Temprature Turbo Manifold Sense Tempature Vat Valve Sense Chamber Pressure Chamber Clamp Pressure Chamber ESC Voltage TCP RF Reference Power TCP Match Tune Cap TCP Match Phase Error TCP Line Impedance Outputs TCP Match Load Cap FICD_mean Bias Match Load Cap FICD_std Bias Match Tune Cap Bias Match Peak Voltage FICD_slope Bias RF Ref Power DefectDensity Chamber Ref Manometer Pressure Chamber Pressure Valve Angle Chamber Clamp Flow Chamber End Point Channel A Chamber End Point Channel B Chamber ESC Current Leak DICD_Mean DICD_Std Inter Layer Dielectric Deposition Post CMP Thickness Post CMP Thickness Nonuniformity Percent Open Area DFT_DICD Results We sought to answer these questions: 1. Can we predict ex-situ information with in- situ results, accurately? Yes! 2. If yes, is there an easily-determined relationship? Model Accuracy Outputs Accuracy Records used FICD_mean 0.53 1254 FICD_std 0.92 1254 FICD_slope 0.93 1225 DefectDensity 0.80 99 Note: Prior metrology is important! Prediction Fitting Curve FICD_Mean 0.70 0.65 0.60 0.55 0.50 0.45 0.40 0.35 0.30 12/25/03 01/14/04 02/03/04 02/23/04 03/14/04 04/03/04 04/23/04 05/13/04 Measured Predicted Accuracy = 0.53, r2=0.95 Accuracy Depends on Limits Setting Recipe 13 0.57 0.55 0.53 0.51 0.49 0.47 0 50 100 150 200 250 300 Measured Predicted Target_Lower_Limit Target_Limit Target_Upper_Limit Soft_Upper_Limit Accuracy = 0.95 Accuracy for A Different Recipe FICD_Mean - Recipe 9 0.58 0.53 0.48 0.43 0.38 0 10 20 30 40 50 Measured Predicted Target_Lower_Limit Target_Limit Target_Upper_Limit Soft_Upper_Limit Accuracy = 0.61 Prediction Fitting Curve FICD_Slope 90 89 88 87 86 85 84 83 12/25/03 01/14/04 02/03/04 02/23/04 03/14/04 04/03/04 04/23/04 05/13/04 Measured Predicted Accuracy = 0.93 Prediction Fitting Curve FICD_STD 0.030 0.025 0.020 0.015 0.010 0.005 0.000 12/25/03 01/14/04 02/03/04 02/23/04 03/14/04 04/03/04 04/23/04 05/13/04 Measured Predicted Accuracy = 0.92 Prediction Fitting Curve DefectDensity 7 6 5 4 3 2 1 0 12/25/03 01/14/04 02/03/04 02/23/04 03/14/04 04/03/04 04/23/04 05/13/04 Measured Predicted Accuracy = 0.80. Limited number of observed records may affect the model accuracy. Sensitivity Analysis We sought to answer these questions: Can we predict ex-situ information with in-situ results, accurately? Yes! We successfully predicted ex-situ metrology from the in-situ metrology with reasonable accuracy (ranging from 0.5 to 0.9) If yes, is there an easily-determined relationship? No. It requires Sensitivity Analysis. Sensitivity Analysis Recipe 1 Bias Match Voltage Complicated relationship. DICD Mean FICD depends on multiple inputs Temp Turbo Manifold Sensor Temp Turbo Manifold Sensor Sensitivity Analysis Recipe 2 Sensitivity is also recipe DICD Mean dependent Temp Turbo Manifold Sensor Temp Turbo Manifold Sensor Sensitivity Analysis Recipe 2 Other ex-situ metrologies show similar complicated sensitivity curves. An example, FICD Slope, is shown. Sensitivity of ex-situ metrology Ex-situ metrology depends on complicated interactions among the trace inputs and the feed forward metrology. Recipe-dependence Non-linear sensitivity curves Possible dependence on tool health situation Sensitivity changes over time This demands an intelligent algorithm for better interpretation. Output Dependency on Inputs Variables FICD_mean FICD_std FICD_slope DefectDensity TCP RF Forward Power Bias RF Forward Power Temperature Upper Sense X Temperature Bottom Electric Sense X Temprature Turbo Manifold Sense X X X X Tempature Vat Valve Sense X Chamber Pressure X Chamber Clamp Pressure Chamber ESC Voltage X TCP RF Reference Power TCP Match Tune Cap X TCP Match Phase Error X TCP Line Impedance TCP Match Load Cap X Bias Match Load Cap X Bias Match Tune Cap X Bias Match Peak Voltage X X X X Bias RF Ref Power X X Chamber Ref Manometer Pressure X Chamber Pressure Valve Angle Chamber Clamp Flow Chamber End Point Channel A X Chamber End Point Channel B X Chamber ESC Current Leak X X DICD_Mean X X X X DICD_Std X Inter Layer Dielectric Deposition X X Post CMP Thickness X X Post CMP Thickness Nonuniformity X X Percent Open Area X X DFT_DICD X X X Summary ● Our previous work** shows comprehensive root cause analysis through neural model of all metrology outputs (in-situ and ex-situ) and controllable variable inputs. ● Recommends corrective action Wafer to Wafer maintenance actions setpointed recipe parameters. ** Card, et. al. Fab Process And Equipment Performance Improvement After An Advanced Process Controller Installation, AEC/APC-Europe 2004 Recommended optimal Repair or Recipe adjustment Gas flow Etch Rate Pressure Uniformity Ex-situ Temp Selectivity Particles Conditioning Run Wet Clean Replace MFC Valve Angle Replace Quartz He Clamp Flow Replace Chuck In-situ Wafer Area Pres. HGS Replace vat valve Next Steps ● By prediction of ex-situ measures with precision, DNC can provide root cause analysis for tool health and process health without reliance on ex-situ measures. ● Addition of more complex sensors (RF probe, OES) may well add the remaining information content to complete ex-situ characterization Recommended optimal Repair or Recipe adjustment Gas flow Valve Angle Pressure He Clamp Flow Temp Wafer Area Pres. Conditioning Run Wet Clean In-situ Replace MFC Replace Quartz OES Replace Chuck RF Probe HGS Replace vat valve Conclusion Accurate predictions of ex-situ metrology can be achieved from in-situ information only. Next Steps Introduce root cause tool control algorithm for maintenance and recipe parameter response. Continue evaluation of complex sensors to further enhance ex-situ metrology prediction using in-situ sources only. Sensitivity analysis Complex relationship to ex-situ metrology. However, if information present, root cause optimization can follow with no loss of precision.
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