To measure the variation of device Vtrequires 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 Vtfor 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 Vtbased only on the combined Idmeasured from parallel connected DUTs. The proposed framework can further minimize the total number of Idmeasurement 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 Vtmean or Vtvariance. Compared to conventional WAT test structures using binary search, our proposed framework can achieve a 120.3 × speedup on overall test time for test structures with 800 DUTs.

To measure the variation of device Vtrequires long test for conventional WAT test structures. This paper presents a framework that can efficiently and effectively obtain the mean and variance of Vtfor 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 Vtbased on only the combined Idmeasured 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 Vtmean 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 Idmeasurement per DUT.