LADAC: Large Language Model-driven Auto-Designer for Analog Circuits
Chengjie Liu, Yijiang Liu, Yuan Du, Li Du
Abstract
Large Language Models (LLMs) have shown their capabilities in solving tasks across various domains. Recently, they have found applications in automating the design of digital circuits. However, there remains a dearth of research investigating the use of LLMs in automating the design of analog circuits. In this work, we propose LADAC: Large Language Model-driven Auto-Designer for Analog Circuits, to assist analog circuit design. LADAC designates an LLM as a decision-making agent, which formulates optimized design strategies based on user specifications and foundational principles of analog circuit design expertise. We applied LADAC to design two kinds of amplifiers with open-loop gain over 80dB, and a ring oscillator with 100MHz oscillation frequency successfully, showing that LADAC is capable of designing analog circuits. Furthermore, it demonstrates the LLMs' potential for designing a broader range of analog circuits.