Fa Wang
Carnegie Mellon University
34 Papers
163 Citations
Fa Wang is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Mixed-signal integrated circuit & Bayesian inference. The author has an hindex of 14, co-authored 33 publications. Previous affiliations of Fa Wang include Oracle Corporation & Tsinghua University.
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Papers
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.
A 18mW, 3.3dB NF, 60GHz LNA in 32nm SOI CMOS technology with autonomic NF calibration
Jean-Olivier Plouchart,Fa Wang,A. Balteanu,Benjamin D. Parker,Mihai Sanduleanu,Mark Yeck,V. H.-C. Chen,Wayne H. Woods,Bodhisatwa Sadhu,Alberto Valdes-Garcia,Xin Li,Daniel Friedman +11 more
- 17 May 2015
TL;DR: A self-healing mmWave SoC integrating an 8052 microcontroller with 12kB of memory, an ADC, a temperature sensor, and a 3-stage cascode 60GHz LNA exhibits a peak gain, an average 3.3dB NF from 53 to 62GHz and 18mW power consumption.
Identifying Wafer-Level Systematic Failure Patterns via Unsupervised Learning
TL;DR: A Pseudo-Boolean satisfiability solver is used to extract a minimal set of systematic failure patterns that explain all wafer-level spatial signatures that help process engineers identify the root causes of failures and accelerate yield learning.
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Large-scale statistical performance modeling of analog and mixed-signal circuits
Xin Li,Wangyang Zhang,Fa Wang +2 more
- 01 Sep 2012
TL;DR: This paper focuses on two core techniques, sparse regression and Bayesian model fusion, that facilitate large-scale performance modeling with low computational cost and their efficacy is compared to other traditional modeling approaches.
Bayesian model fusion: a statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits
Xin Li,Fa Wang,Shupeng Sun,Chenjie Gu +3 more
- 18 Nov 2013
TL;DR: A novel statistical framework, referred to as Bayesian Model Fusion (BMF), is described that allows us to minimize the simulation and/or measurement cost for both pre-silicon validation and post- silicon tuning of analog and mixed-signal circuits with consideration of large-scale process variations.
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