Design Automation of Two-Stage Operational Amplifier Using Multi-Objective Genetic Algorithm and SPICE Framework

Das, Purvi; Jajodia, Babita

doi:10.1109/icict54344.2022.9850461

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…The drawback of the heuristics to be problem-dependent, limits its usage in diverse field of applications [10]. Meta-heuristic algorithms include various optimization techniques like, among many others, particle swarm optimization (PSO) [22]- [27], genetic algorithms [28]- [29] and differential evolution [30]- [31]. Nature based metaheuristic algorithms are gaining high recognition in solving computationally expensive optimization problems [21].…”

Section: Introductionmentioning

confidence: 99%

An Error Bound Particle Swarm Optimization for Analog Circuit Sizing

Shreeharsha,

Siddharth,

Vasantha

et al. 2024

IEEE Access

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An Error-Bound Particle Swarm Optimization (EB-PSO) is proposed in this work. The objective function is evaluated for each particle in each iteration. The velocity update equation is modified by introducing two new parameters ζ 1 and ζ 2 . These parameters varies exponentially, within the bounds (ζ 1,min , ζ 2,min ) and (ζ 1,max , ζ 2,max ), with respect to the number of iterations. Initially, a higher value of ζ 2 and minimum value of ζ 1 is chosen to facilitate a global search. Once the global error (ε 2 ) is less than the desired value, ζ 1 is allowed to increase from its minimum value and ζ 2 is held constant at ζ 2,max . This leads to local exploitation of the search space. The proposed algorithm is implemented on Python platform. To verify the effectiveness of the proposed EB-PSO algorithm in analog circuit sizing, a case study on the performance and optimization of two-stage op-amp is presented, whose validation is done in Cadence-Virtuoso environment at 45-nm CMOS technology. The results show that the proposed EB-PSO algorithm converges in 11 iterations for two-stage op-amp, whereas it takes 23, 29, and 41 iterations to converge for conventional GA, DE, and PSO algorithms respectively. INDEX TERMS Analog circuit sizing, Particle swarm optimization (PSO), constrained optimization.

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Section: Introductionmentioning

confidence: 99%

An Error Bound Particle Swarm Optimization for Analog Circuit Sizing

Shreeharsha,

Siddharth,

Vasantha

et al. 2024

IEEE Access

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“…A high performance current conveyor and low noise amplifier were designed using PSO in [16]. In [17], a Genetic Algorithm (GA) is used to optimize the performance parameters of mixed-signal circuits. In [18], it was used to find the optimal solution for the analog circuits.…”

Section: Introductionmentioning

confidence: 99%

Partition Bound Random Number-Based Particle Swarm Optimization for Analog Circuit Sizing

Shreeharsha,

Siddharth,

Vasantha

et al. 2023

IEEE Access

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This work introduces a Partition Bound Particle Swarm Optimization (PB-PSO) algorithm to enhance convergence rates in analog circuit optimization. Two new parameters, ζ 1 and ζ 2 , are incorporated to adaptively update particle velocities based on iteration numbers. The parameter ζ 1 depends on the nonlinear convergence factor (α) and the number of iterations, N . The results indicate that ζ 1 's optimal value occurs with α = 4. ζ 2 partitions iterations into two regions, aiding local and global search. The PB-PSO algorithm, implemented in Python, demonstrates higher convergence rates than existing methods, with successful designs verified through Cadence-Virtuoso circuit simulations. The proposed PB-PSO algorithm converges in 15 and 13 iterations for differential amplifier and two-stage op-amp respectively. For a case study of two-stage amplifier, it achieves a gain of 60.4 dB with a phase margin of 79.76 • , meeting input specifications within constraints. The figure of merit was then evaluated using the obtained parameters, which turns out to be 0.275 V −2 . INDEX TERMS Particle swarm optimization (PSO), analog circuit sizing, low power, operational amplifiers, constrained optimization.

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