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Wangyang Zhang
Researcher at Cadence Design Systems
Publications - 37
Citations - 603
Wangyang Zhang is an academic researcher from Cadence Design Systems. The author has contributed to research in topics: Bayesian inference & Mixed-signal integrated circuit. The author has an hindex of 14, co-authored 36 publications receiving 543 citations. Previous affiliations of Wangyang Zhang include Mentor Graphics & IBM.
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Journal ArticleDOI
Virtual Probe: A Statistical Framework for Low-Cost Silicon Characterization of Nanoscale Integrated Circuits
TL;DR: This paper proposes a new technique, referred to as virtual probe (VP), to efficiently measure, characterize, and monitor spatially-correlated inter-die and/or intra-die variations in nanoscale manufacturing process, thereby reducing the cost of silicon characterization.
Proceedings ArticleDOI
Bayesian model fusion: large-scale performance modeling of analog and mixed-signal circuits by reusing early-stage data
TL;DR: This paper proposes a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF), which achieves up to 9× runtime speedup over the traditional modeling technique without surrendering any accuracy.
Journal ArticleDOI
Bayesian Model Fusion: Large-Scale Performance Modeling of Analog and Mixed-Signal Circuits by Reusing Early-Stage Data
TL;DR: Two circuit examples designed in a commercial 32 nm CMOS silicon on insulator process demonstrate that the proposed BMF method achieves up to $9\times $ runtime speed-up over the traditional modeling technique without surrendering any accuracy.
Proceedings ArticleDOI
Test cost reduction through performance prediction using virtual probe
TL;DR: The virtual probe technique, based on recent breakthroughs in compressed sensing, has demonstrated its ability for accurate prediction of spatial variations from a small set of measurement data, is explored to cost reduction of production testing.
Proceedings ArticleDOI
Bayesian virtual probe: minimizing variation characterization cost for nanoscale IC technologies via Bayesian inference
TL;DR: The proposed BVP method borrows the idea of Bayesian inference and information theory from statistics to determine an optimal set of sampling locations where test structures should be deployed and measured to monitor spatial variations with maximum accuracy.