Automatic detection of spatial signature on wafermaps in a high volume production

Unsupervised spatial pattern classification of electrical-wafer-sorting maps in semiconductor manufacturing

“…When the patterns exhibit typical shapes, it is possible to resort to image processing techniques (Breaux and Singh, 1995;Duvivier, 1999), whose aim is to recognize specific shapes formed by the defects, e.g. scratches, spots, rings, repetitive patterns, etc.…”

A neural-network approach to recognize defect spatial pattern in semiconductor fabrication

“…This system combined the image processing techniques and fuzzy logic expert system to recognize the scratch pattern or other patterns described by users. Duvivier [20] developed a statistical method to detect and classify spatial defect patterns. In his methods, all the wafermaps were examined to generate the so-called random ratios (RR).…”

Defect spatial pattern recognition using a hybrid SOM–SVM approach in semiconductor manufacturing

“…Today, automatic defect classification (ADC), spatial signature analysis (SSA) and data mining approaches have demonstrated successful wafer bin map pattern recognition implementation. SSA is an automated procedure that has been developed by researchers at the Oak Ridge National Laboratory to address the issue of intelligent data reduction while providing timely feedback on current manufacturing processes (Duvivier, 1999;Gleason, Tobin, & Karnowski, 1998;Karnowski, Tobin, Jensen, & Lakhani, 1999). This method has been extended to analyze and interpret electrical test data and provide a pathway for correlation of this data with in-line measurements (Gleason, Tobin, Karnowski, & Lakhani, 1997).…”