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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, &#x03B6; 1  and &#x03B6;2, are incorporated to adaptively update particle velocities based on iteration numbers. The parameter &#x03B6; 1  depends on the non-linear convergence factor (&#x03B1;) and the number of iterations, N. The results indicate that &#x03B6; 1 &#x2019;s optimal value occurs with &#x03B1; = 4. &#x03B6; 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&#x00B0;, 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 .

Partition Bound Random Number based Particle Swarm Optimization for Analog Circuit Sizing