Experts in the Loop: Conditional Variable Selection for Accelerating Post-Silicon Analysis Based on Deep Learning

TL;DR

This paper introduces a novel conditional variable selection method that incorporates expert knowledge into deep learning models to improve post-silicon validation in semiconductor manufacturing, addressing high-dimensional challenges.

Contribution

It presents the first approach to involve experts in the variable selection process, enhancing accuracy and efficiency in identifying critical variables for post-silicon analysis.

Findings

The method outperforms existing data-driven approaches on synthetic datasets.

It demonstrates significant improvements in real-world semiconductor data.

Experts' involvement reduces bias and increases the relevance of selected variables.

Abstract

Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devices is key to enable post-silicon tuning and debugging. This analysis is typically performed by experienced human experts. However, with the fast development in semiconductor industry, test cases can contain hundreds of variables. The resulting high-dimensionality poses enormous challenges to experts. Thereby, some recent prior works have introduced data-driven variable selection algorithms to tackle these problems and achieved notable success. Nevertheless, for these methods, experts are not involved in training and inference phases, which may lead to bias and inaccuracy due to the lack of prior knowledge. Hence, this work for the first time aims to design a novel conditional variable selection approach while keeping experts in the loop. In this way, we expect that our algorithm can be more efficiently and effectively trained to identify the most critical variables under certain expert knowledge. Extensive experiments on both synthetic and real-world datasets from industry have been conducted and shown the effectiveness of our method.