AI-Driven Optimization and Automation of Integrated Circuit Design
- 1 Edición - 1 de octubre de 2026
- Última edición
- Editores: Neha Singh, Shilpi Birla, Chetan Arvind Patil
- Idioma: Inglés
AI-Driven Optimization and Automation of Integrated Circuit Design discusses the latest AI-based methods, algorithms, architectures, and frameworks for digital, analog, and mixed-… Leer más
Descripción
Descripción
Artificial Intelligence (AI) offers a solution to the bottleneck issues in the design of integrated circuits (IC) by optimizing and automating tasks in the design and fabrication process. As the world focuses on the development of skilled manpower and automation tools for chip design, verification, testing and fabrication, AI can be utilized to optimize and automate various steps in design cycle, saving time, reducing errors, and managing power consumption.
Puntos claves
Puntos claves
- Explains how AI algorithms can significantly speed up the IC design process by automating tasks
- Describes how AI can enhance precision in circuit design by reducing errors and improving design verification through predictive analysis
- Offers insights into how to integrate AI tools and techniques into existing EDA (Electronic Design Automation) workflows
De interès para
De interès para
Índice
Índice
2. Neural Network for Logic Synthesis Optimization
3. Machine Learning Approaches to Fault Detection in Circuit Design
4. Enhancing Chip Verification using AI
5. AI Applications in Digital Design for Testability (DFT
6. Machine leaning based power estimation in VLSI Design
7. Next-Gen IC Fault Detection using Advanced Machine Learning Algorithms – A Comprehensive Survey
8. AI-driven high level synthesis and behavioral simulation in VLSI design
9. Leveraging machine learning algorithms for designing analog and RF integrated circuits
10. Challenges with traditional integrated circuits design methods
11. Optimization techniques in ML for VLSI design
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 1 de octubre de 2026
- Idioma: Inglés
Sobre los editores
Sobre los editores
NS
Neha Singh
SB
Shilpi Birla
CP
Chetan Arvind Patil
Mr. Chetan Arvind Patil is a Principal Engineer at Marvell Semiconductor Inc., USA, with over 10+ years of experience in the semiconductor industry. He focuses on Test Engineering and Customer Strategy for AI custom ASICs, driving scalable test methodologies, yield optimization, and manufacturing readiness for advanced compute silicon. He is actively engaged in global semiconductor initiatives spanning standards, industry forums, and roadmap development. He contributes to various IEEE initiatives, including IEEE Global Semiconductors (an Ad Hoc of the IEEE Future Directions Committee), supporting technical discussions on AI-driven semiconductor development, manufacturing scalability, and ecosystem evolution. He is actively involved in IEEE Standards activities, serves on the IEEE Senior Member Application Panel, and contributes as a committee member and reviewer for multiple IEEE conferences. He has also contributed to the Heterogeneous Integration Roadmap under the IEEE Electronics Packaging Society (EPS) and delivered 15+ invited talks at global universities and industry forums, and has supported the MAPT Roadmap under the Semiconductor Research Corporation. Chetan is the author of 30+ semiconductor-focused articles covering yield, testing, chiplets, reliability, and manufacturing trends, published in international industry media. He also maintains a semiconductor blog, www.ChetanPatil.in, with 300+ articles aimed at knowledge sharing across the ecosystem. He holds dual Master of Science degrees in Computer Engineering from Arizona State University and Northwestern University, and a Bachelor of Engineering in Electronics and Telecommunication from Pune Institute of Computer Technology, India. He is a Senior Member of IEEE.