Test cost reduction through performance prediction using virtual probe

Abstract

The virtual probe (VP) technique, based on recent breakthroughs in compressed sensing, has demonstrated its ability for accurate prediction of spatial variations from a small set of measurement data. In this paper, we explore its application to cost reduction of production testing. For a number of test items, the measurement data from a small subset of chips can be used to accurately predict the performance of other chips on the same wafer without explicit measurement. Depending on their statistical characteristics, test items can be classified into three categories: highly predictable, predictable, and un-predictable. A case study of an industrial RF radio transceiver with more than 50 production test items shows that a good fraction of these test items (39 out of 51 items) are predictable or highly predictable. In this example, the 3σ error of VP prediction is less than 12% for predictable or highly predictable test items. Applying the VP technique can on average replace 59% of test measurement by prediction and, consequently, reduce the overall test time by 57.6%.