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.

A novel machine learning based parasitic estimation (MLParest) method for pre-layout custom circuit design is presented. It reduces the error between pre-layout and post-layout circuit simulation from 37% to 8% on average for different measurements across a variety of analog circuits. MLParest can thus greatly reduce the number of iterations between pre-layout and post-layout design phases. The key contributions of this work are a machine learning based approach to parasitic estimation and a push-button model training framework, scalable across different technology nodes. To the best of our knowledge, a machine learning based framework of parasitic estimation is an industry first.

Injection locking analysis based on classical Adler's equation is limited to LC oscillators as it is dependent on quality factor. In this paper, we present the generalized Adler's equation applicable for injection locking analysis on oscillators independent of the circuit topology. The equation is obtained by averaging the PPV phase macromodel. The procedure is considerably simple and handy to determine the locking range for arbitrary shape small AC injection signal. Analytical equations for injection locking dynamics are formulated using the generalized Adler's equation and validated with the PPV simulations.

Coupled oscillator networks occur in various domains such as biology, astrophysics and electronics. In this paper, we present a comprehensive procedure for rapid and accurate simulation of large coupled oscillator networks using widely accepted, fully-nonlinear perturbation projection vector (PPV) phase macromodels. We validate our method against full simulation of 20times20 coupled network of Brusselator biochemical oscillator and obtain computational speedups of 170x over full simulation. Furthermore, we apply the method to study self-organization phenomenon of Brusselator under asymmetric coupling and time period variations.