  This paper proposes a learning framework, RoSE-Opt, to achieve robust and
efficient analog circuit parameter optimization. RoSE-Opt has two important
features. First, it incorporates key domain knowledge of analog circuit design,
such as circuit topology, couplings between circuit specifications, and
variations of process, supply voltage, and temperature, into the learning loop.
This strategy facilitates the training of an artificial agent capable of
achieving design goals by identifying device parameters that are optimal and
robust. Second, it exploits a two-level optimization method, that is,
integrating Bayesian optimization (BO) with reinforcement learning (RL) to
improve sample efficiency. In particular, BO is used for a coarse yet quick
search of an initial starting point for optimization. This sets a solid
foundation to efficiently train the RL agent with fewer samples. Experimental
evaluations on benchmarking circuits show promising sample efficiency,
extraordinary figure-of-merit in terms of design efficiency and design success
rate, and Pareto optimality in circuit performance of our framework, compared
to previous methods. Furthermore, this work thoroughly studies the performance
of different RL optimization algorithms, such as Deep Deterministic Policy
Gradients (DDPG) with an off-policy learning mechanism and Proximal Policy
Optimization (PPO) with an on-policy learning mechanism. This investigation
provides users with guidance on choosing the appropriate RL algorithms to
optimize the device parameters of analog circuits. Finally, our study also
demonstrates RoSE-Opt's promise in parasitic-aware device optimization for
analog circuits. In summary, our work reports a knowledge-infused BO-RL design
automation framework for reliable and efficient optimization of analog
circuits' device parameters.
