Generation and Quality Evaluation of Synthetic Process Control Monitoring Data

We discuss the problem of generating synthetic Process Control Monitoring (PCM) data and evaluating how accurately it reflects the distribution of actual measurements from manufactured wafers. PCMs are small test structures placed in the scribe lines of the wafer, on which electrical tests (E-tests) are conducted to monitor the impact of process variation on a manufactured wafer. Besides its immediate use in assessing and controlling wafer health, collective PCM data holds invaluable information for process engineers who seek to maximize yield and performance across process corners. Yet availability of such data is limited during the ramp-up phase of a process, when it is needed the most. To address this limitation, we introduce a methodology that leverages correlations across E-test measurements and across wafer locations to generate a large synthetic population from a small data sample. Furthermore, we discuss statistical metrics that can be used to evaluate the accuracy of the synthetically generated vis-à-vis the actual population. Effectiveness of our solution is experimentally validated using E-test data from ~8K wafers fabricated in an advanced GlobalFoundries FinFET node.