Machine Learning Driven Throughput Optimization of Volume Diagnosis Methodology

Abstract

Numerous areas of VLSI Design and Automation including test and diagnosis have already started benefiting from machine learning based approaches. In this paper, we focus on application of machine learning techniques in the context of Volume Diagnosis methodology which aims at improving the yield analysis and management process. Specifically, we apply machine learning to monitor and predict throughput bottlenecks in diagnosis process that impede the pace of yield analysis. In the proposed supervised machine learning technique, diagnosis features extracted from thousands of devices are used to train a random forest regression model and features causing greatest impact on run times are predicted. This technique has resulted in identifying a class of faults (labelled “hyperactive faults”) to be strongly correlated to diagnosis run time. Based on this finding, we propose improvements to volume diagnosis methodology to identify and mask hyperactive faults in advance from volume diagnosis process. Experimental results using proposed improvements on large industrial designs demonstrate up to ~8% reduction in volume diagnosis run time with no loss of accuracy and resolution.