From Classical Algorithms to AI: Evolving Trends in VLSI Physical Design Automation

Physical design is a fundamental stage in electronic design automation (EDA). Over the past two decades, significant progress has been made in core algorithms and methodologies in physical design to address the growing complexity of designs and the demands of advanced integrated circuit (IC) technologies. More recently, the emergence of artificial intelligence (AI) and GPU-accelerated computing has begun to transform the landscape of physical design, paving the way for new opportunities and innovations. This paper reviews some key developments in Very Large Scale Integration (VLSI) physical design automation and discusses future research directions.

Authors

Institutions

Publication Details

Journal
IEEE Design and Test
Published
2025-08-26
DOI
https://doi.org/10.1109/mdat.2025.3603108
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From Classical Algorithms to AI: Evolving Trends in VLSI Physical Design Automation

David Z. Pan, Chen-Hao Hsu, Yang Hsu, Yao-Wen Chang
IEEE Design and Test
Manufacturing Process and Optimization
article

From Classical Algorithms to AI: Evolving Trends in VLSI Physical Design Automation

David Z. Pan, Chen-Hao Hsu, Yang Hsu, Yao-Wen Chang
article en

Abstract

Physical design is a fundamental stage in electronic design automation (EDA). Over the past two decades, significant progress has been made in core algorithms and methodologies in physical design to address the growing complexity of designs and the demands of advanced integrated circuit (IC) technologies. More recently, the emergence of artificial intelligence (AI) and GPU-accelerated computing has begun to transform the landscape of physical design, paving the way for new opportunities and innovations. This paper reviews some key developments in Very Large Scale Integration (VLSI) physical design automation and discusses future research directions.

IEEE Design and TestVol. 42(6)
National Taiwan University (TW), The University of Texas at Austin (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 69%
Manufacturing Process and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.