|標題:||A Model-Based-Random-Forest Framework for Predicting V-t Mean and Variance Based on Parallel I-d Measurement|
Hou, Alex Chun-Liang
Chao, Mango C. T.
Department of Electronics Engineering and Institute of Electronics
|關鍵字:||Machine learning;model-based random forest (MBRF);threshold voltage;wafer acceptance test (WAT)|
|摘要:||To measure the variation of device V-t requires long test for conventional wafer acceptance test (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 designs under test (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 only on the combined I-d measured from parallel connected DUTs. The proposed framework can further minimize the total number of I-d measurement required for prediction models while limiting their accuracy loss. The experimental results based on the SPICE simulation of a UMC 28-nm technology demonstrate that the proposed model-fitting framework can achieve a more than 99% R -squared for predicting either V-t mean or V-t variance. Compared to conventional WAT test structures using binary search, our proposed framework can achieve a 120.3x speedup on overall test time for test structures with 800 DUTs.|
|期刊:||IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS|
|Appears in Collections:||Articles|