  Analog circuit sizing takes a significant amount of manual effort in a
typical design cycle. With rapidly developing technology and tight schedules,
bringing automated solutions for sizing has attracted great attention. This
paper presents DNN-Opt, a Reinforcement Learning (RL) inspired Deep Neural
Network (DNN) based black-box optimization framework for analog circuit sizing.
The key contributions of this paper are a novel sample-efficient two-stage deep
learning optimization framework leveraging RL actor-critic algorithms, and a
recipe to extend it on large industrial circuits using critical device
identification. Our method shows 5--30x sample efficiency compared to other
black-box optimization methods both on small building blocks and on large
industrial circuits with better performance metrics. To the best of our
knowledge, this is the first application of DNN-based circuit sizing on
industrial scale circuits.
