Predicting Vt mean and variance from parallel Id measurement with model-fitting technique

To measure the variation of device V _t requires long test for conventional WAT test structures. This paper presents a framework that can efficiently and effectively obtain the mean and variance of V _t for a large number of DUTs. The proposed framework applies the model-based random forest as its core model-fitting technique to learn a model that can predict the mean and variance of V _t based on only the combined I _d measured from parallel connected DUTs. The experimental results based on the SPICE simulation of a UMC 28nm technology demonstrate that the proposed model-fitting framework can achieve a more than 99% R-squared for predicting both of V _t mean and variance. Compared to conventional WAT test structures using binary search, our proposed framework can achieve 42.9X speedup in turn of the required iterations of I _d measurement per DUT.