TR2022-096

Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization


    •  Cao, Weidong, Benosman, Mouhacine, Zhang, Xuan, Ma, Rui, "Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization", Tech. Rep. TR2022-096, Mitsubishi Electric Research Laboratories, Cambridge, MA, August 2022.
      BibTeX TR2022-096 PDF
      • @techreport{MERL_TR2022-096,
      • author = {Cao, Weidong; Benosman, Mouhacine; Zhang, Xuan; Ma, Rui},
      • title = {Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2022-096},
      • month = aug,
      • year = 2022,
      • url = {https://www.merl.com/publications/TR2022-096/}
      • }
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  • Research Areas:

    Artificial Intelligence, Electronic and Photonic Devices, Machine Learning, Optimization, Signal Processing

Abstract:

The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit parameter optimization at the pre-layout stage, i.e., finding device parameters to fulfill desired circuit specifications. Unlike all prior methods, our approach is inspired by human experts who rely on domain knowledge of analog circuit design (e.g., circuit topology and couplings between circuit specifications) to tackle the problem. By originally incorporating such key domain knowledge into policy training with a multimodal network, the method best learns the complex relations between circuit parameters and design targets, enabling optimal decisions in the optimization process. Experimental results on exemplary circuits show it achieves human-level design accuracy (∼99%) with 1.5× efficiency of existing best-performing methods. Our method also shows better generalization ability to unseen specifications and optimality in circuit performance optimization. Moreover, it applies to design radio-frequency circuits on emerging semiconductor technologies, breaking the limitations of prior learning methods in designing conventional analog circuit