An efficient high-frequency linear RF amplifier synthesis method based on evolutionary computation and machine learning techniques

Liu, B. , Deferm, N., Zhao, D., Reynaert, P. and Gielen, G. G.E. (2012) An efficient high-frequency linear RF amplifier synthesis method based on evolutionary computation and machine learning techniques. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 31(7), pp. 981-993. (doi: 10.1109/TCAD.2012.2187207)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Professor Bo
• Authors: Liu, B., Deferm, N., Zhao, D., Reynaert, P., and Gielen, G. G.E.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• Publisher: IEEE
• ISSN: 0278-0070
• ISSN (Online): 1937-4151
• Published Online: 14 June 2012