Adaptive NN-based Root Cause Analysis in Volume Diagnosis for Yield Improvement

Abstract

Root Cause Analysis (RCA) is a critical technology for yield improvement in integrated circuit manufacture. Traditional RCA prefers unsupervised algorithms such as Expectation Maximization based on Bayesian models. However, these methods are severely limited by the weak predictive capability of statistical models and can’t effectively transfer the yield learning experience from old designs and processes to the new ones. Motivated by recent advancements of deep learning, in this paper we propose a Neural-Network-based adaptive framework for RCA in yield improvement. The proposed framework consists of an inference module and a self-adaptive module. The former receives volume diagnosis reports and predicts the root cause distributions. The latter is able to adapt the inference module to new designs and processes based on a few of targeted samples without any manual adjustment. Experimental results show that a relatively large improvement on accuracy is achieved by the proposed framework on simulated diagnosis data. Furthermore, the transferring capability of the self-adaptive module is also validated by the results.