Analog circuit sizing via swarm intelligence

Journal of Electrical Systems and Information Technology

et al. 2015

Please cite this article in press as: De, B.P., et al., Optimal CMOS inverter design using differential evolution algorithm. J. Electr. Syst. Inform. Technol. (2015), http://dx. AbstractThe inverter is known to be the nucleus of all digital designs. Evolutionary computation may be a competent implement for automatic design of digital integrated circuits (IC). In this paper, optimal switching characteristics of a CMOS inverter are realized using an evolutionary optimization approach called differential evolution (DE) algorithm. The real coded genetic algorithm (RGA) and particle swarm optimization (PSO) have been adopted for the sake of comparison. DE based design results have been compared also with those of the PSPICE results. The comparative simulation results establish the DE as a more competent candidate to other aforementioned evolutionary algorithms in terms of accuracy and convergence speed.

Section: Introductionmentioning

confidence: 99%

Optimal CMOS inverter design using differential evolution algorithm

Journal of Electrical Systems and Information Technology

et al. 2015

“…PSO is an evolutionary optimization technique developed by Eberhart et al [6]. PSO is utilized for CMOS transistor area optimization of a two-stage operational amplifier [5,7]. Considering two-stage operational amplifier (op-amp) design, PSO [5] proved its efficiency for the analog IC design with higher optimization ability with lower computation time.…”

Section: Introductionmentioning

confidence: 99%

Soft computing‐based approach for optimal design of on‐chip comparator and folded‐cascode op‐amp using colliding bodies optimization

Int J Numerical Modelling

et al. 2016

This paper presents an efficient approach for the optimal designs of two analog circuits, namely complementary metal oxide semiconductor) two-stage comparator with p-channel metal oxide semiconductor input driver and n-channel input and folded-cascode operational amplifier using a recently proposed meta-heuristic-based optimization algorithm named as colliding bodies optimization (CBO). It is a multi-agent algorithm that does not depend upon any internal control parameter, making the algorithm extremely simple. The main objective of this paper is to optimize the metal oxide semiconductor (MOS) transistors' sizes using CBO in order to reduce the areas occupied by the circuits and to get better performance parameters of the circuits. Simulation Program with Integrated Circuit Emphasis simulation has been carried out by using the optimal values of MOS transistors' sizes and other design parameters to validate that CBO-based design is satisfying the desired specifications. Simulation results demonstrate that the design specifications are closely met and the required functionalities are achieved. The simulation results also confirm that the CBO-based approach is superior to the other algorithms in terms of MOS area and performance parameters like gain, power dissipation, etc., for the examples considered.For CBO, number of colliding bodies (CBs) is taken as 10 and dimensions of each CB is defined as X Comparator = [SR, A vd , V IC(min) , V IC(max) ]. So, the dimension of the optimization problem is 4. 878 B. P. DE ET AL.

“…PSO is very simple to implement and a few steps are required to implement the algorithm. PSO was efficiently used in various application areas [16][17][18][19][20][21]. Design of digital IIR filter using particle swarm optimization was proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

“…The same was also applied for the synthesis of microstrip coupler and single shunt stub matching circuits [19]. Optimal design of analog circuits such as differential amplifier with current mirror load and two stage operational amplifiers were carried out using the same algorithm in [20]. The limitations of the conventional PSO are premature convergence and stagnation problem [21][22] .…”

Section: Introductionmentioning

confidence: 99%

Design of symmetric switching CMOS inverter using PSOCFIWA

2014 International Conference on Communication and Signal Processing

et al. 2014

In this paper, symmetric switching characteristics of CMOS inverter are realized using an evolutionary optimization technique called Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO CFIWA). PSO-CFIWA is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The performance of PSO-CFIW A is studied with the comparison of real coded genetic algorithm (RGA), a conventional PSO reported in the literature. PSO-CFIWA based design results have been compared also to those of the PSPICE results. The comparative simulation results show that the PSO CFIWA is superior to other aforementioned evolutionary algorithms for the employed examples and can be efficiently used for CMOS inverter design.