Robust Resistive Open Defect Identification Using Machine Learning with Efficient Feature Selection

Abstract

Resistive open defects in FinFET circuits are reliability threats and should be ruled out before deployment. The performance variations due to these defects are similar to the effect of process variations which are mostly benign. In order not to sacrifice yield for reliability the effect of defects should be distinguished from process variations. It has been shown that machine learning (ML) schemes are able to classify defective circuits with high accuracy based on the maximum frequencies $F_{max}$ obtained under multiple supply voltages $V_{dd} \in V_{op}$. The paper at hand presents a method to minimize the number of required measurements. Each supply voltage $V_{dd}$ defines a feature $F_{max}(V_{dd})$. A feature selection technique is presented, which uses also the already available $F_{max}$ measurements. It is shown that ML-based techniques can work efficiently and accurately with this reduced number of $F_{max}(V_{dd})$ measurements.