Self-improvement of OPAmp parameters using Q-Learning

被引：0

作者：

Takai, Nobukazu ^{[1
]}

Fukuda, Masafumi ^{[2
]}

Saruta, Masahiro ^{[2
]}

机构：

[1] Gunma Univ, Fac Sci & Technol, Gunma, Japan

[2] Gunma Univ, Fac Sci & Technol, Kiryu, Gunma, Japan

来源：

2019 16TH INTERNATIONAL CONFERENCE ON SYNTHESIS, MODELING, ANALYSIS AND SIMULATION METHODS AND APPLICATIONS TO CIRCUIT DESIGN (SMACD 2019) | 2019年

关键词：

Reinforcement learning; Q-learning; analog integrated circuit; Operational Amplifier;

D O I：

10.1109/smacd.2019.8795232

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Q-Learning is one of the best ways to find action in certain situations through trials and errors. In this paper, we propose a novel method that finds element values of OPAmp satisfying specifications of a target circuit using Q-learning. In the proposed method, the relationship between "element value" and "circuit characteristic" is learned by Q-learning. From simulation results, we confirmed that the computer itself learned circuit design procedure, and autonomous design became possible.

引用

页码：293 / 296

页数：4

共 9 条

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