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

Abstract

This work introduces a Partition Bound Particle Swarm Optimization (PB-PSO) algorithm to enhance convergence rates in analog circuit optimization. Two new parameters, $\zeta _{1}$ and $\zeta _{2}$ , are incorporated to adaptively update particle velocities based on iteration numbers. The parameter $\zeta _{1}$ depends on the non-linear convergence factor ( $\alpha $ ) and the number of iterations, $N$ . The results indicate that $\zeta _{1}$ ’s optimal value occurs with $\alpha = 4.~\zeta _{2}$ 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 $V^{-2}$ .

Keywords

• Particle swarm optimization (PSO)
• analog circuit sizing
• lowpower
• operational amplifiers
• constrained optimization