Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning

Hung Yun Hsu; Mark Po Hung Lin

doi:10.1145/3551901.3556486

Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning

Hung Yun Hsu, Mark Po Hung Lin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Schematic visualization is important for analog circuit designers to quickly recognize the structures and functions of transistor-level circuit netlists. However, most of the original analog design or other automatically extracted analog circuits are stored in the form of transistor-level netlists in the SPICE format. It can be error-prone and time-consuming to manually create an elegant and readable schematic from a netlist. Different from the conventional graph-based methods, this paper introduces a novel analog schematic diagram generation flow based on comprehensive building block classification and reinforcement learning. The experimental results show that the proposed method can effectively generate aesthetic analog circuit schematics with a higher building block compliance rate, and fewer numbers of wire bends and net crossings, resulting in better readability, compared with existing methods and modern tools.

Original language	English
Title of host publication	MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD
Publisher	Association for Computing Machinery, Inc
Pages	43-48
Number of pages	6
ISBN (Electronic)	9781450394864
DOIs	https://doi.org/10.1145/3551901.3556486
State	Published - 12 Sep 2022
Event	4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022 - Snowbird, United States Duration: 12 Sep 2022 → 13 Sep 2022

Publication series

Name	MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD

Conference

Conference	4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022
Country/Territory	United States
City	Snowbird
Period	12/09/22 → 13/09/22

Keywords

analog circuit
building block
reinforcement learning
schematic generation
schematic visualization

Access to Document

10.1145/3551901.3556486

Cite this

Hsu, H. Y., & Lin, M. P. H. (2022). Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning. In MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD (pp. 43-48). (MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD). Association for Computing Machinery, Inc. https://doi.org/10.1145/3551901.3556486

Hsu, Hung Yun ; Lin, Mark Po Hung. / Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning. MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. Association for Computing Machinery, Inc, 2022. pp. 43-48 (MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD).

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title = "Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning",

abstract = "Schematic visualization is important for analog circuit designers to quickly recognize the structures and functions of transistor-level circuit netlists. However, most of the original analog design or other automatically extracted analog circuits are stored in the form of transistor-level netlists in the SPICE format. It can be error-prone and time-consuming to manually create an elegant and readable schematic from a netlist. Different from the conventional graph-based methods, this paper introduces a novel analog schematic diagram generation flow based on comprehensive building block classification and reinforcement learning. The experimental results show that the proposed method can effectively generate aesthetic analog circuit schematics with a higher building block compliance rate, and fewer numbers of wire bends and net crossings, resulting in better readability, compared with existing methods and modern tools.",

keywords = "analog circuit, building block, reinforcement learning, schematic generation, schematic visualization",

author = "Hsu, {Hung Yun} and Lin, {Mark Po Hung}",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022 ; Conference date: 12-09-2022 Through 13-09-2022",

year = "2022",

month = sep,

day = "12",

doi = "10.1145/3551901.3556486",

language = "English",

series = "MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD",

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Hsu, HY & Lin, MPH 2022, Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning. in MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, Association for Computing Machinery, Inc, pp. 43-48, 4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022, Snowbird, United States, 12/09/22. https://doi.org/10.1145/3551901.3556486

Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning. / Hsu, Hung Yun; Lin, Mark Po Hung.
MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. Association for Computing Machinery, Inc, 2022. p. 43-48 (MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning

AU - Hsu, Hung Yun

AU - Lin, Mark Po Hung

PY - 2022/9/12

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N2 - Schematic visualization is important for analog circuit designers to quickly recognize the structures and functions of transistor-level circuit netlists. However, most of the original analog design or other automatically extracted analog circuits are stored in the form of transistor-level netlists in the SPICE format. It can be error-prone and time-consuming to manually create an elegant and readable schematic from a netlist. Different from the conventional graph-based methods, this paper introduces a novel analog schematic diagram generation flow based on comprehensive building block classification and reinforcement learning. The experimental results show that the proposed method can effectively generate aesthetic analog circuit schematics with a higher building block compliance rate, and fewer numbers of wire bends and net crossings, resulting in better readability, compared with existing methods and modern tools.

AB - Schematic visualization is important for analog circuit designers to quickly recognize the structures and functions of transistor-level circuit netlists. However, most of the original analog design or other automatically extracted analog circuits are stored in the form of transistor-level netlists in the SPICE format. It can be error-prone and time-consuming to manually create an elegant and readable schematic from a netlist. Different from the conventional graph-based methods, this paper introduces a novel analog schematic diagram generation flow based on comprehensive building block classification and reinforcement learning. The experimental results show that the proposed method can effectively generate aesthetic analog circuit schematics with a higher building block compliance rate, and fewer numbers of wire bends and net crossings, resulting in better readability, compared with existing methods and modern tools.

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KW - building block

KW - reinforcement learning

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KW - schematic visualization

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Hsu HY, Lin MPH. Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning. In MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. Association for Computing Machinery, Inc. 2022. p. 43-48. (MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD). doi: 10.1145/3551901.3556486

Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning

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