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Description

Analog and custom circuits have long been a bottleneck to the integrated circuit design process. Automation generation of such circuits has long been a topic of research, but has failed to break through to popular practice. This work introduces a modular framework including a cloud-native IC design database, an analog circuit programming framework, a web-native schematic system, and tools for directed programming and automatic compilation of semi-custom IC layout. Highlighted applications include wireline transceivers and data converters, including a recent prototype ADC targeted for neural sensing applications, and research infrastructure for distributed, machine learning based circuit optimization.

Details

Title
An Integrated Circuit Design Framework for Human, Computer, and ML Designers
Creator
Fritchman, Dan, Author
Published
EECS Department, University of California at Berkeley, Berkeley, California, 12/15/23
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2023-275
Type
Text
Format
technical reports
Extent
149 p
Language
eng
Archive
The Library
Usage Statement
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