Novel applications of deep learning hidden features for adaptive testing

Abstract

Adaptive test of integrated circuits (IC) promises to increase the quality and yield of products with reduced manufacturing test cost compared to traditional static test flows. Two mostly widely used techniques are Statistical Process Control (SPC) and Part Average Testing (PAT), whose capabilities to capture complex correlation between test measurements and the underlying IC's physical and electrical properties are, however, limited. Based on recent progress on machine learning, this paper proposes a novel deep learning based method for adaptive test. Compared to most machine learning techniques, deep learning has the distinctive advantage of being able to capture the underlying key features automatically from data without manual intervention. In this paper, we start from a trained deep neuron network (DNN) with a much higher accuracy than the conventional test flow for the pass and fail prediction. We further develop two novel applications by leveraging the features learned from DNN: one to enable partial testing, i.e., make decisions on pass and fail without finishing the entire test flow, and two to enable dynamic test ordering, i.e., changing the sequence of tests adaptively. Experiment results show significant improvement on the accuracy and effectiveness of our proposed method.