Hybrid Particle Swarm Optimization Algorithm for Area Minimization in 65 nm Technology

Rashid, Ria; Nambath, Nandakumar

doi:10.1109/iscas51556.2021.9401139

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Details of the proposed optimisation technique and its application in the design of a two stage op-amp with Miller compensation are discussed in this section. Results of proposed method used for the area optimisation of a differential amplifier circuit is reported earlier in [16]. Fixing Bounds:…”

Section: Proposed Optimisation Methodsmentioning

confidence: 99%

Area Optimisation of Two Stage Miller Compensated Op-Amp in 65 nm Using Hybrid PSO

Rashid

Nambath

2022

IEEE Trans. Circuits Syst. II

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Analog circuit design can be formulated as a nonlinear constrained optimisation problem that can be solved using any suitable optimisation algorithms. Different optimisation techniques have been reported to reduce the design time of analog circuits. A hybrid particle swarm optimisation algorithm with linearly decreasing inertia weight for the optimisation of analog circuit design is proposed in this study. The proposed method is used to design a two-stage operational amplifier circuit with Miller compensation. The results show that the proposed optimisation method can substantially reduce the design time needed for analog circuits.

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Section: Proposed Optimisation Methodsmentioning

confidence: 99%

Area Optimisation of Two Stage Miller Compensated Op-Amp in 65 nm Using Hybrid PSO

Rashid

Nambath

2022

IEEE Trans. Circuits Syst. II

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“…PSO using fuzzy algorithm was used to overcome premature convergence, which is typically found in conventional PSO [33]. A hybrid PSO was presented in [34], in which the the convergence rate was improved by linearly decreasing the inertia weight.…”

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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“…However, within the field of analog IC design optimization, there is a notable absence of investigations related to PSO's inertia weight reduction. The authors in [22] and [28] have exclusively introduced the linear decremental inertia weight PSO method for area minimization in two-stage operational amplifiers, but these studies lack comparisons with the conventional PSO approach. To address the limited utilization of the PSO inertial weight reduction method in the field of analog IC design, in this paper, we introduce two innovative exponential inertia weight reduction techniques, denoted as PSO -local exploitation orienter (PSO-LEO) and PSO -global exploration orienter (PSO-GEO).…”

Section: Introductionmentioning

confidence: 99%

Novel Methods for Improved Particle Swarm Optimization in Designing the Bandgap Reference Circuit

Hoang,

Quoc,

Zhang

et al. 2023

IEEE Access

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Bandgap reference (BGR) circuits play a crucial role as voltage reference generators for other components within a variety of analog integrated circuits. Therefore, their power supply rejection ratio (PSRR), which represents the BGR's ability to maintain a stable output in the presence of supply ripples, has a substantial impact on the overall circuit performance and is consequently worthy of attention. In this work, the design of a 65nm-CMOS BGR circuit and the computational approach to optimize its PSRR value are presented. Our proposed BGR circuit incorporates a modification in the op-amp structure to enhance the PSRR parameter. Detailed explanations of the proposed op-amp's differential gain calculation and the PSRR parameter formula are provided. Additionally, we employ the particle swarm optimization (PSO) algorithm to maximize PSRR while satisfying other specifications. Four distinct approaches for utilizing the inertia weight parameter of the PSO algorithm are explored: two newly developed techniques (PSO -local exploitation orienter (PSO-LEO) and PSO -global exploration orienter (PSO-GEO)) together with two conventional approaches. A comprehensive comparative analysis reveals the superiority of our proposed PSO-GEO technique. Ultimately, our 65 nm CMOS bandgap circuit exhibits exceptional performance, featuring a temperature coefficient of 6.4056 ppm/°C and a PSRR of 99.9896 dB at 1 kHz. In conclusion, this work contributes substantially through op-amp architecture modification, PSRR optimization via the PSO algorithm, and introducing new inertia weight parameter utilization strategies and analyzing them.

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Hybrid Particle Swarm Optimization Algorithm for Area Minimization in 65 nm Technology

Cited by 8 publications

References 15 publications

Area Optimisation of Two Stage Miller Compensated Op-Amp in 65 nm Using Hybrid PSO

Area Optimisation of Two Stage Miller Compensated Op-Amp in 65 nm Using Hybrid PSO

An Error Bound Particle Swarm Optimization for Analog Circuit Sizing

Novel Methods for Improved Particle Swarm Optimization in Designing the Bandgap Reference Circuit

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