Fast and Accurate Deep Neural Network (DNN) Model Extension Method for Signal Integrity (SI) Applications

Abstract

In this paper, we first propose a fast and accurate deep neural network (DNN) model extension method for signal integrity (SI) applications. Reusing pre-trained weights of DNN model, the model can be extended when new training data are given. Instead of updating whole weights of DNN in traditional machine learning (ML) approaches, fine-tuning of a part of weights can accelerate training. For verification, we applied the proposed method to regression model of peak time domain reflectometry (TDR) impedance of through hole via (THV) and classification model of through silicon via (TSV) void defects. Training time of the proposed method were 0.3 s and 2.3 s respectively, which are 99 % and 82.3 % reduction compared to the traditional approach. Moreover, test accuracy of the proposed method achieved 99.2 % and 100 %, respectively.