Classification of Defect Spatial Signatures Using Independent Component Analysis and Estimation of Process / Tool Malfunctions Using [chi]2 Test and Exact Test

We developed a system that detects spatial signatures from the defect inspection data of each substrate and thus identifies fault detection in device manufacturing. Leveraging the independent component analysis facilitates an unsupervised simultaneous classification of any defect distribution generated with one or more tool malfunctions. All substrates are classified according to our proposed coefficient of similarity to each defect distribution. A root cause process is identified through a test of independence between the manufacturing tools and their rates of the number of classified substrates on the basis of the classification result and their fabrication history data. The tests of independence use χ2 tests in combination with exact tests to decrease the incidence of false positive errors. The root cause tool is identified in terms of the highest rate between the tools in the identified process. Our system functions automatically and requires no experience or technical skill. We present the case where for approximately two days, our system detected a tool malfunction earlier than the conventional monitoring of substrates, and with greater total defect counts per substrate than a control limit; further, we present another case where our system detected a greater number of substrates than the conventional monitoring.