Design Automation of Analog and Mixed-Signal Circuits Using Neural Networks - A Tutorial Brief

• Published in: IEEE transactions on circuits and systems. II, Express briefs Vol. 71; no. 3; p. 1
• Main Authors: Linan-Cembrano, Gustavo, Lourenco, Nuno, Horta, Nuno, Rosa, Jose M. de la
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analog and Mixed-Signal Circuit Design; Analog circuits; Artificial neural networks; Automation; Behavioral sciences; Circuit design; Circuits; Circuits and systems; Computer architecture; Design Automation; Design methodology; Design optimization; Integrated circuit modeling; Modulators; Neural Networks; Operational amplifiers; Optimization; Simulators; Topology; Transconductance
• ISSN: 1549-7747; 1558-3791
• DOI: 10.1109/TCSII.2023.3323886

This tutorial brief shows how Artificial Neural Networks (ANNs) can be used for the optimization and automated design of analog and mixed-signal circuits. A survey of conventional and computational-intelligence design methods is given as a motivation towards using ANNs as optimization engines. A step-by-step procedure is described explaining the key aspects to consider in our approach, such as dataset preparation, ANNs modeling, training, and optimization of network hyperparameters. As an application, two case studies at different hierarchy levels are presented. The first one is the system-level sizing of Sigma-Delta Modulators (ΣMs), where ANNs are combined with behavioral simulations to generate valid circuit-level design variables for a given set of specifications. The second example combines ANNs with electrical simulators to optimize the circuit-level design of operational transconductance amplifiers. The results validate the presented approach and show its benefits with respect to prior art on synthesis methods of analog and mixed-signal circuits and systems.