Automatic classification of bridge defects

Abstract

A technique is proposed to automatically predict whether a failing chip has a bridge defect. Logic diagnosis is performed using scan test results to identify candidate nets. Several relevant features of the test data are measured for net pairs that consist of the diagnosis candidates and other nets in close physical proximity. Based on these features, rules are constructed to identify defects that fully exhibit classic bridge behaviors, while the remaining chips are classified using a forest of decision trees. Results indicate that a population of chips failing due to bridges can indeed be extracted with very high accuracy. Finally, the method correctly classifies 41 commercially-fabricated chips that underwent PFA.