SoC Speed Binning Using Machine Learning and On-Chip Slack Sensors

摘要

Speed binning of system-on-chips (SoCs) using conventional Fmax test requires application of complex functional test patterns. Functional workload-based speed binning techniques incur high test-cost in terms of long test-time and complexity in functional test generation, and require high-end automatic test equipment. In this paper, we propose a novel speed binning flow that uses path timing slacks, extracted with robust digital embedded sensor IPs, of selected criticalearcritical paths. We apply machine learning techniques to model a predictor considering the extracted slacks and the Fmax values from a set of randomly tested die during wafer sort. The trained predictor is used to obtain the Fmax for the remaining chips. The proposed flow has been demonstrated in an SoC benchmark circuit at 28 nm technology. For sufficient number of training samples, Fmax is correctly predicted for 99% of the prediction samples.