Automated sizing of low-noise CMOS analog amplifier using ALCPSO optimization algorithm

Singh, Chabungbam Lison; Anandini, Chabungbam; Gogoi, Ashim Jyoti; Baishnab, Krishna Lal

doi:10.1080/02522667.2017.1380408

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…203.36 µm 2 compared to those achieved through the PSO and DE algorithms. Singh et al [27] worked on analogous circuit by using PSO algorithm with aging leader and challengers (ALCPSO) algorithm in the CMOS technology. Table 3 shows the result for comparison of the performance.…”

Section: Results and Analysismentioning

confidence: 99%

Comparative analysis of meta heuristics algorithm for differential amplifier design

Prajapati,

Kshatriya,

Patel

et al. 2023

Bulletin EEI

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The design of analog circuits is very essential for the development of system design based on electronics as the world is analog in nature. Furthermore, performance of emerging products depends on analog circuits for reduction of power dissipation and improvement of speed. Though in the system on chip (SoC) analog circuit consumes less, it is more complex to design it due to the analog circuit nature complexity. The job of the evolutionary algorithm (EA) in the process of optimization of an analog circuit based on complementary metal oxide semiconductor (CMOS) is to get the appropriate values that can be used for the design parameters of the given circuit in such a way such that all the targeted specification of the given circuit can be obtained. The chaotic serial particle swarm optimization (CSPSO) and cuckoo search (CS) algorithms are lower than to those found through the particle swarm optimization (PSO) and differential evolution (DE) algorithms. The CS algorithm obtained all the desired specifications with less power dissipation and the least total transistor area compared to those attained by the DE and PSO algorithms.

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Section: Results and Analysismentioning

confidence: 99%

Comparative analysis of meta heuristics algorithm for differential amplifier design

Prajapati,

Kshatriya,

Patel

et al. 2023

Bulletin EEI

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“…To further validate IRIME's performance, this study compared it with some advanced algorithms using the IEEE CEC 2017 benchmark test suite. These algorithms include EBOwithCMAR [80], LSHADE_cnEpSi [73], ALCPSO [81], CLPSO [82], LSHADE [83], SADE [84], JADE [85], RCBA [86], EPSO [87], CBA [88], and LWOA [89], The specific experimental data is detailed in Table A6 (Appendix). The results in the table highlight that IRIME, alongside some advanced algorithms, achieves performance near the theoretical optimum in functions such as F1, F3, F6, and F9.…”

Section: Comparison With Advanced Algorithmsmentioning

confidence: 99%

IRIME: A Novel Approach to Mitigating Exploitation-Exploration Imbalance in Rime Optimization Algorithm for Feature Selection

Huang,

Chen,

Heidari

et al. 2024

Preprint

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Rime optimization algorithm (RIME) is an emerging metaheuristic algorithm. However, RIME encounters issues such as an imbalance between exploitation and exploration, susceptibility to local optima, and low convergence accuracy when handling problems. To address these drawbacks, this paper introduces a variant of RIME called IRIME. IRIME integrates the soft besiege (SB) and composite mutation strategy and restart strategy (CMS-RS), aiming to balance exploitation and exploration in RIME, enhance population diversity, improve convergence accuracy, and endow RIME with the capability to escape local optima. To comprehensively validate IRIME's performance, IEEE CEC 2017 benchmark tests were conducted, comparing it against 13 conventional algorithms and 11 advanced algorithms, including excellent algorithms in the CEC competition such as JADE. The results indicate that the performance of IRIME is the best. To validate IRIME's practical applicability, the paper proposes a binary version, bIRIME, applied to feature selection problems. bIRIMR performs well on 12 low-dimensional datasets and 24 high-dimensional datasets. It outperforms other advanced algorithms in terms of the number of feature subsets and classification accuracy. In conclusion, bIRIME performs notably well in feature selection, particularly in high-dimensional datasets.

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“…Step 4: Likewise, by taking into consideration the maximum output voltage of the circuit and the drain current equations for the PMOS transistors, the aspect ratios of transistors M 14 –M 16 can be calculated. The transistors M 17 –M 20 are

times that of

and

[ 47 , 48 ].…”

Section: Modified Recycling Folded Cascode Amplifier (Mrfc)mentioning

confidence: 99%

Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method

et al. 2022

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This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits.

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Automated sizing of low-noise CMOS analog amplifier using ALCPSO optimization algorithm

Cited by 9 publications

References 10 publications

Comparative analysis of meta heuristics algorithm for differential amplifier design

Comparative analysis of meta heuristics algorithm for differential amplifier design

IRIME: A Novel Approach to Mitigating Exploitation-Exploration Imbalance in Rime Optimization Algorithm for Feature Selection

Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method

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