Highly Efficient Inverse Design of Semiconductor Optical Amplifiers Based on Neural Network Improved Particle Swarm Optimization Algorithm

Zhao, T.; Ji, Wei; Liu, Pengcheng; Feng, Gao; Li, Changpeng; Wang, Yiming; Huang, Wei‐Ping

doi:10.1109/jphot.2023.3258071

Cited by 2 publications

(1 citation statement)

References 33 publications

(25 reference statements)

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“…Even though the paper focuses on the design of the InvCas TIA for the optical receiver's front end, the proposed work is not discussed in any further detail. According to Zhao et al [11], PSO algorithms are combined with AI-integrated neural networks to create a model for semiconductor optical amplifiers (SOAs). Based on the curve of the tested SOA performance, this model enhances the effectiveness of SOA design.…”

Section: Related Literaturementioning

confidence: 99%

A Reformed PSO-Based High Linear Optimized Up-Conversion Mixer for Radar Application

Delwar,

Aras,

Siddique

et al. 2024

Sensors

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A reformed particle swarm optimization (RPSO)-based up-conversion mixer circuit is proposed for radar application in this paper. In practice, a non-optimized up-conversion mixer suffers from high power consumption, poor linearity, and conversion gain. Therefore, the RPSO algorithm is proposed to optimize the up-conversion mixer. The novelty of the proposed RPSO algorithm is it helps to solve the problem of local optima and premature convergence in traditional particle swarm optimization (TPSO). Furthermore, in the RPSO, a velocity position-based convergence (VPC) and wavelet mutation (WM) strategy are used to enhance RPSO’s swarm diversity. Moreover, this work also features novel circuit configurations based on the two-fold transconductance path (TTP), a technique used to improve linearity. A differential common source (DCS) amplifier is included in the primary transconductance path (PTP) of the TTP. As for the subsidiary transconductance path (STP), the enhanced cross-quad transconductor (ECQT) is implemented within the TTP. A benchmark function verification is conducted to demonstrate the effectiveness of the RPSO algorithm. The proposed RPSO has also been compared with other optimization algorithms such as the genetic algorithm (GA) and the non-dominated sorting genetic algorithm II (NSGA-II). By using RPSO, the proposed optimized mixer achieves a conversion gain (CG) of 2.5 dB (measured). In this study, the proposed mixer achieves a 1 dB compression point (OP1dB) of 4.2 dBm with a high linearity. In the proposed mixer, the noise figure (NF) is approximately 3.1 dB. While the power dissipation of the optimized mixer is 3.24 mW. Additionally, the average time for RPSO to design an up-conversion mixer is 4.535 s. Simulation and measured results demonstrate the excellent performance of the RPSO optimized up-conversion mixer.

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Section: Related Literaturementioning

confidence: 99%

A Reformed PSO-Based High Linear Optimized Up-Conversion Mixer for Radar Application

Delwar,

Aras,

Siddique

et al. 2024

Sensors

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Comprehensive review of the directed design of optomechanical crystal cavities using intelligent algorithms

Yu,

Bai,

2024

Appl. Opt.

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Optomechanical crystal cavities are devices based on optomechanical interactions to manipulate photons and phonons on periodic subwavelength structures, enabling precise measurement of the force and displacement. The performance of the target structures varies when applied to different applications. Optomechanical crystal cavities now rely on an empirical forward design, which is inefficient. Therefore, a desired shift is toward directed design with a “problem-oriented” strategy. The directed optimization problem’s nonconvex nature and extensive parameter space necessitate substantial computational resources, driving the need for intelligent algorithms in a sub-wavelength structure design. Intelligent algorithms can surpass the constraints of traditional methods and discover novel structures that are effective in different materials, topologies, modes, and wavelengths. This paper provides an extensive overview of intelligent algorithms for guiding the directed design of optomechanical crystal cavities. It presents a systematic classification of 15 algorithmics, including, but not limited to, topology algorithms, particle swarm optimization algorithms, convolutional neural networks, and generative adversarial networks. The article provides a comprehensive review and thorough analysis of the principle and current application state, as well as the advantages and disadvantages of each intelligent algorithm. By using these intelligent algorithms, researchers can enhance the efficiency and accuracy of optimizing optomechanical crystal cavities in a broader design space.

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Highly Efficient Inverse Design of Semiconductor Optical Amplifiers Based on Neural Network Improved Particle Swarm Optimization Algorithm

Cited by 2 publications

References 33 publications

A Reformed PSO-Based High Linear Optimized Up-Conversion Mixer for Radar Application

A Reformed PSO-Based High Linear Optimized Up-Conversion Mixer for Radar Application

Comprehensive review of the directed design of optomechanical crystal cavities using intelligent algorithms

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