Title: An Efficient High-Frequency Linear RF Amplifier Synthesis Method Based on Evolutionary Computation and Machine Learning Techniques
Authors: Liu, Bo ×
Deferm, Noël
Zhao, Dixian
Reynaert, Patrick
Gielen, Georges #
Issue Date: 1-Jul-2012
Series Title: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems vol:31 issue:7 pages:981-993
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.
ISSN: 0278-0070
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - MICAS, Microelectronics and Sensors
× corresponding author
# (joint) last author

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