EDBT 2026 Demo / reviewers in the wild / expert

Brett Shook

dblp:276/1939 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 50% Integrated circuit design · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Integrated circuit design › analog and mixed-signal circuits
analog circuit design		 0.412020
MLParest: Machine Learning based Parasitic Estimation for Custom Circuit Design · DAC 2020
Electronic design automation › physical design
parasitic extraction		 0.412020
MLParest: Machine Learning based Parasitic Estimation for Custom Circuit Design · DAC 2020

Methods — techniques the papers use, named apart from their topics

model training framework · 0.4machine learning · 0.4
YearPublicationVenuePosition
2020 MLParest: Machine Learning based Parasitic Estimation for Custom Circuit Design
abstract
A novel machine learning based parasitic estimation (MLParest) method for pre-layout custom circuit design is presented. It reduces the error between pre-layout and post-layout circuit simulation from 37% to 8% on average for different measurements across a variety of analog circuits. MLParest can thus greatly reduce the number of iterations between pre-layout and post-layout design phases. The key contributions of this work are a machine learning based approach to parasitic estimation and a push-button model training framework, scalable across different technology nodes. To the best of our knowledge, a machine learning based framework of parasitic estimation is an industry first.
Brett Shook, Prateek Bhansali, Chandramouli V. Kashyap, Chirayu Amin, Siddhartha Joshi
 DAC1