Analog circuit design requires substantial human expertise and involvement,
which is a significant roadblock to design productivity. Bayesian Optimization
(BO), a popular machine learning based optimization strategy, has been
leveraged to automate analog design given its applicability across various
circuit topologies and technologies. Traditional BO methods employ black box
Gaussian Process surrogate models and optimized labeled data queries to find
optimization solutions by trading off between exploration and exploitation.
However, the search for the optimal design solution in BO can be expensive from
both a computational and data usage point of view, particularly for high
dimensional optimization problems. This paper presents ADO-LLM, the first work
integrating large language models (LLMs) with Bayesian Optimization for analog
design optimization. ADO-LLM leverages the LLM's ability to infuse domain
knowledge to rapidly generate viable design points to remedy BO's inefficiency
in finding high value design areas specifically under the limited design space
coverage of the BO's probabilistic surrogate model. In the meantime, sampling
of design points evaluated in the iterative BO process provides quality
demonstrations for the LLM to generate high quality design points while
leveraging infused broad design knowledge. Furthermore, the diversity brought
by BO's exploration enriches the contextual understanding of the LLM and allows
it to more broadly search in the design space and prevent repetitive and
redundant suggestions. We evaluate the proposed framework on two different
types of analog circuits and demonstrate notable improvements in design
efficiency and effectiveness.

ADO-LLM 整合了大型语言模型与贝叶斯优化用于模拟设计优化，利用大型语言模型的领域知识并结合贝叶斯优化的多样性和探索能力实现了设计效率和效果的显著提升。