An Ordinal Optimization-Based Approach To Die Distribution Estimation For Massive Multi-site Testing Validation: A Case Study
Isaac Bruce, Praise O. Farayola, Shravan K. Chaganti, Abdullah O. Obaidi, Abalhassan Sheikh, Srivaths Ravi, Degang J. Chen · 2021
Multisite testing has become a proven method to reduce test time and costs for integrated circuits (IC). However, the technique suffers from site-to-site variations, especially when a large number of test sites are involved. It becomes imperative to identify issue sites that exhibit unacceptable variations to prevent yield loss or incorrect passing of faulty devices. By correctly identifying the true probability distribution of tested specifications, identification of issue sites becomes easier. We introduce an ordinal optimization-based algorithm to select the right sites to estimate the true distribution in situations where it is difficult or impossible to find the true distribution for tested specifications. Using both simulation and real-world ATE test data, we demonstrate that this approach yields good results.