Calculating Signal Controllability using Neural Networks: Improvements to Testability Analysis and Test Point Insertion

Abstract

This article presents an artificial neural network-based signal probability predictor for VLSI circuits which considers reconvergent fan-outs. Current testability analysis techniques are useful for inserting test points to improve circuit testability, but reconvergent fan-outs in digital circuits creates inaccurate testability analysis. Conventional testability analysis methods like COP do not consider reconvergent fan-outs and can degrade algorithm results (e.g., test point insertion), while more advanced methods increase analysis time significantly. This study shows training and using artificial neural networks to predict signal probabilities increases post-test point insertion fault coverage compared to using COP, especially in circuits with many reconvergent fan-outs.