Learning to Design Analog Circuits to Meet Threshold Specifications

• URL: http://arxiv.org/abs/2307.13861v1
• Date: Tue, 25 Jul 2023 23:25:05 GMT
• Title: Learning to Design Analog Circuits to Meet Threshold Specifications
• Authors: Dmitrii Krylov, Pooya Khajeh, Junhan Ouyang, Thomas Reeves, Tongkai Liu, Hiba Ajmal, Hamidreza Aghasi, Roy Fox
• Abstract summary: We propose a method for generating from simulation data a dataset on which a system can be trained to design circuits to meet threshold specifications. We show that our method consistently reaches success rate better than 90% at 5% error margin, while also improving data efficiency by upward of an order of magnitude.
• Score: 2.5818330243826924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated design of analog and radio-frequency circuits using supervised or reinforcement learning from simulation data has recently been studied as an alternative to manual expert design. It is straightforward for a design agent to learn an inverse function from desired performance metrics to circuit parameters. However, it is more common for a user to have threshold performance criteria rather than an exact target vector of feasible performance measures. In this work, we propose a method for generating from simulation data a dataset on which a system can be trained via supervised learning to design circuits to meet threshold specifications. We moreover perform the to-date most extensive evaluation of automated analog circuit design, including experimenting in a significantly more diverse set of circuits than in prior work, covering linear, nonlinear, and autonomous circuit configurations, and show that our method consistently reaches success rate better than 90% at 5% error margin, while also improving data efficiency by upward of an order of magnitude. A demo of this system is available at circuits.streamlit.app

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