An Efficient High-Frequency Linear RF Amplifier Synthesis Method Based on Evolutionary Computation and Machine Learning Techniques

Abstract

Existing radio frequency (RF) integrated circuit (IC) design automation methods focus on the synthesis of circuits at a few GHz, typically less than 10 GHz. That framework is difficult to apply to RF IC synthesis at mm-wave frequencies (e.g., 60-100 GHz). In this paper, a new method, called efficient machine learning-based differential evolution, is presented for mm-wave frequency linear RF amplifier synthesis. By using electromagnetic (EM) simulations to evaluate the key passive components, the evaluation of circuit performances is accurate and solves the limitations of parasitic-included equivalent circuit models and predefined layout templates used in the existing synthesis framework. A decomposition method separates the design variables that require expensive EM simulations and the variables that only need cheap circuit simulations. Hence, a low- dimensional expensive optimization problem is generated. By the newly proposed core algorithm integrating adaptive population generation, naive Bayes classification, Gaussian process and differential evolution, the generated low-dimensional expensive optimization problem can be solved efficiently (by the online surrogate model), and global search (by evolutionary computation) can be achieved. A 100 GHz three-stage differential amplifier is synthesized in a 90 nm CMOS technology. The power gain reaches 10 dB with more than 20 GHz bandwidth. The synthesis costs only 25 h, having a comparable result and a nine times speed enhancement compared with directly using the EM simulator and global optimization algorithms.