A Novel Approach to Identify Dynamic Deficiency in Cell using Gaussian NB Classifier

Abstract

For the identification of intra-cell faults that may arise in returned products, a novel learning-guided technique has been described in this study. Assumed static flaws described by stuck-at errors are used in the first half of the study. Many supervised learning methods are investigated, with varied degrees of efficiency. The second section of the study is built on the earlier research by addressing more complex dynamic flaws. As part of the diagnostic procedure, a Naive Bayesian classification algorithm is applied to identify and categorise the type of each new data fault. The suggested approach’s reliability and clarity have been shown via testing on standard circuits and compared with a conventional cell-aware diagnostic tool.