Browsing by Author "Kuo, Chun-Yi"

Now showing 1 - 3 of 3

A Model-Based-Random-Forest Framework for Predicting V-t Mean and Variance Based on Parallel I-d Measurement
(2018-10-01)
Lin, Chien-Hsueh
;
Tsai, Chih-Ying
;
Lee, Kao-Chi
;
Yu, Sung-Chu
;
Liau, Wen-Rong
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.
Predicting V-t Mean and Variance from Parallel I-d Measurement with Model-Fitting Technique
(2016)
Tsai, Chih-Ying
;
Lee, Kao-Chi
;
Lin, Chien-Hsueh
;
Yu, Sung-Chu
;
Liau, Wen-Rong
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.
Predicting Vt Variation and Static IR Drop of Ring Oscillators Using Model-Fitting Techniques
(2017-01-01)
Huang, Tzu-Hsuan
;
Hung, Wei-Tse
;
Yang, Hao-Yu
;
Chang, Wen-Hsiang
;
Chen, Ying-Yen
This paper presents a statistical model-fitting framework to efficiently decompose the impact of device Vt variation and power-network IR drop from the measured ring-oscillator frequencies without adding any extra circuitry to the original ring oscillators. The framework applies Gaussian process regression as its core model-fitting technique and stepwise regression as a preprocess to select significant predictor features. The experiments conducted based on the SPICE simulation of an industrial 28nm technology demonstrate that our framework can simultaneously predict the NMOS Vt, PMOS Vt and static IR drop of the ring oscillators based on their frequencies measured at different external supply voltages. The final resulting R squares of the predicted features are all more than 99.93%.