Die Failure Pattern Detection in Semiconductor Wafers
Analyzing the WBMs manually is an extremely time consuming process. It also leads to a high margin error when a subsample of WBMs are examined manually because the sample is not a good representation of the entire lot.
The identified defect patterns are used by experts to tie back to the process variables for root cause analysis, saving a lot of money and time by early prevention in the manufacturing process.
Analyze Wafer Bin Map to detect patterns of defective wafers using Deep Learning techniques.
Anomaly detection in Semiconductor Manufacturing Etching Process
Detect anomalies in Semiconductor plasma etching process during fabrication.
Fault-detection/diagnosis of etching is done primarily by visual inspection of graphically displayed process data by experienced technicians. The tediousness and intrinsic human unreliability of this method, as well as the high cost of mistakes, makes automation attractive.
Analyze Optical Emission Spectrometry data and Etching related data like etching rate using Deep Learning techniques
