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Abstract:   (889 Views)
In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new and comprehensive proposed criterion, and also meet different design specifications such as DC gain, Gain-Band Width product (GBW), Phase Margin (PM), Slew Rate (SR), Common Mode Rejection Ratio (CMRR), Power Supply Rejection Ratio (PSRR), etc. The proposed MOLA contains several automata and each automaton is responsible for searching one dimension. The workability of the proposed approach is evaluated in comparison with the most well-known category of intelligent meta-heuristic Multi-Objective Optimization (MOO) methods such as Particle Swarm Optimization (PSO), Inclined Planes system Optimization (IPO), Gray Wolf Optimization (GWO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The performance of the proposed MOLA is demonstrated in finding optimal Pareto fronts with two criteria Overall Non-dominated Vector Generation (ONVG) and Spacing (SP). In simulations, for the desired application, it has been shown through Computer-Aided Design (CAD) tool that MOLA-based solutions produce better results.
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  • A new application of LA for MOO in the optimal design of CMOS analog IC.
  • Proper definition of design parameters and objective functions to create an effective trade-off between performance characteristics.
  • Implementation of an automated design simulation tool by creating a link between two usable software environments.
  • Providing a comprehensive criterion to evaluate the proposed approach due to the simultaneous effect of objectives and design specifications on the optimization problem.
  • The statistical evaluation of the proposed approach based on numerical results obtained from circuit simulations with other competing algorithms.

Type of Study: Research Paper | Subject: Integrated Circuits: Digital, Analog
Received: 2019/04/11 | Accepted: 2019/06/28 | Published: 2019/06/30

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© 2020 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.