An Optimized Device Sizing of Analog Circuits using Particle Swarm Optimization

Abstract

Problem statement: Day by day more and more products rely on analog circuits to improve the speed and reduce the power consumption(Products rely on analog circuits to improve the speed and reduce the power consumption day by day more and more.). For the VLSI implementation analog circuit design plays an important role. This analog circuit synthesis might be the most challenging and time-consumed task, because it does not only consist of topology and layout synthesis but also of component sizing. Approach: A Particle Swarm Optimization (PSO) technique for the optimal design of analog circuits. Analog signal processing finds many applications and widely uses OpAmp based amplifiers, mixers, comparators. and filters. Results: A two-stage opamp (Miller Operational Trans-conductance Amplifier (OTA)) is considered for the synthesis that satisfies certain design specifications. Performance has been evaluated with the Simulation Program with Integrated Circuit Emphasis (SPICE) circuit simulator until optimal sizes of the transistors are found. Conclusion: The output of the simulation for the two-stage opamp shows that the PSO technique is an accurate and promising approach in determining the device sizes in an analog circuit.

Intelligent Optimization Methods for Analogue Electronic Circuits: GA and PSO Case Study

“…Particle swarm optimization (PSO) for design of analogue circuits is proposed in [92]. Analogue signal processing finds many applications and widely uses OPAMP based amplifiers, mixers, comparators and filters.…”

Improvement in Sizing Constrained Analog IC via Ts-CPD Algorithm

“…Therefore, another challenge for sizing high-performance analog circuits with tight specifications is the need for a powerful enough optimization kernel for EDA tools to handle tighter specifications and improve optimization capability [27]. Different optimization kernels are currently used for EDA tools; among them, we can mention the kernels based on GA [28], PSO [29], Ant Colony Optimization (ACO) in [30], Simulated Annealing (SA) in [31], GSA in [23], Nondominated Sorting Genetic Algorithm-II (NSGA-II) in [32] and NSGA-II, Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Simulated Annealing (MOSA) in [33].…”

A Survey: Particle Swarm Optimization Based Algorithms to Solve Premature Convergence Problem

“…PSO is one of the evolutional optimization methods and can solve many optimization problems that are encountered in various fields of technology such as switched reluctance motors (Balaji and Kamaraj, 2011), reduction of key search space of vigenere cipher (Sivagurunathan and Purusothaman, 2011), analog circuit (Kumar and Duraiswamy, 2012), controlling power systems (Mauryan et al, 2012) and etc. This method because of the simple concept and easy implementation has developed fast in recent years.…”