In this post, we share some recent promising results regarding the applications of Deep Learning in analog IC design. While this work targets a specific application, the proposed methods can be used in other black box optimization problems where the environment lacks a cheap/fast evaluation procedure.

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So let’s break down how the analog IC design process is usually done, and then how we incorporated deep learning to ease the flow.

The intent of analog IC design is to build a physical manufacturable circuit that processes electrical signals in the analog domain, despite all sorts of noise sources that may affect the fidelity of signals. Usually analog circuit design starts off with topology selection. Generically speaking, engineers usually come up with topology of certain blocks and try to size them such that after putting them together the entire system behaves in a certain way and satisfies some figures of merit. There are certain levels of simulations and tests that need to take place to verify that the system will work before manufacturing. At the lowest level engineers do their design using their intuition and equations and then simulate and make the

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