Browsing by Author "Lin, Chien-Hsueh"

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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.
Generating Routing-Driven Power Distribution Networks with Machine-Learning Technique
(2016)
Chang, Wen-Hsiang
;
Chen, Li-De
;
Lin, Chien-Hsueh
;
Mu, Szu-Pang
;
Chao, Mango C. -T.
As technology node keeps scaling and design complexity keeps increasing, power distribution networks (PDNs) require more routing resource to meet IR-drop and EM constraints. This paper presents a design flow to generate a PDN that can result in minimal overhead for the routing of the underlying standard cells while satisfying both IR-drop and EM constraints based on a given cell placement. The design flow relies on a machine-learning model to quickly predict the total wire length of global route associated with a given PDN configuration in order to speed up the search process. The experimental results based on various 28nm industrial block designs have demonstrated the accuracy of the learned model for predicting the routing cost and the effectiveness of the proposed framework for reducing the routing cost of the final PDN.
Generating Routing-Driven Power Distribution Networks With Machine-Learning Technique
(2017-08-01)
Chang, Wen-Hsiang
;
Lin, Chien-Hsueh
;
Mu, Szu-Pang
;
Chen, Li-De
;
Tsai, Cheng-Hong
As technology node keeps scaling and design complexity keeps increasing, power distribution networks (PDNs) require more routing resource to meet IR-drop and electromigration (EM) constraints. This paper presents a design flow to generate a PDN that can result in near-minimal overhead for the routing of the underlying standard cells while satisfying both IR-drop and EM constraints based on a given cell placement. The design flow relies on a machine-learning model to quickly predict the total wire length of global route associated with a given PDN configuration in order to speed up the search process. The experimental results based on various 28 nm industrial block designs have demonstrated the accuracy of the learned model for predicting the routing cost and the effectiveness of the proposed framework for reducing the routing cost of the final PDN.
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.