An Intelligent Analysis of Iddq Data for Chip Classification in Very Deep-Submicron (VDSM) CMOS Technology

Abstract

Iddq testing has been a critical integral component in test suites for screening unreliable devices. As the silicon technology keeps shrinking, Iddq values and their variation increase as well. Moreover, along with rapid design scaling, defect-induced leakage currents become less significant when compared to full-chip current and also make themselves less distinguishable. Traditional Iddq methods become less effective and cause more test escapes and yield loss. Therefore, in this paper, a new test method named sigma-Iddq testing is proposed and integrates (1) a variation-aware full-chip leakage estimator and (2) a clustering algorithm to classify chip without using threshold values. Experimental result shows that sigma-Iddq testing achieves a higher classification accuracy in a 45nm technology when compared to a single-threshold Iddq testing. As a result, both the process-variation and design-scaling impacts are successfully excluded and thus the defective chips can be identified intelligently.