|標題:||Predicting V-t Mean and Variance from Parallel I-d Measurement with Model-Fitting Technique|
Hou, Alex Chun-Liang
Chao, Mango C. -T.
Department of Electronics Engineering and Institute of Electronics
|摘要:||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.|
|期刊:||2016 IEEE 34TH VLSI TEST SYMPOSIUM (VTS)|
|Appears in Collections:||Conferences Paper|