P‐61: Distinguished Poster: Research on Parameter Extraction of Thin‐Film Transistors Based on Swarm Intelligence

Automatic parameter extraction of RPI Model for Polysilicon Thin‐Film Transistors is achieved by genetic algorithm(GA) and Particle swarm optimization(PSO) algorithm, and the solution of two algorithms are compared. Furthermore, mutual learning particle swarm optimization (MLPSO) algorithm is propos...

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Vydáno v:SID International Symposium Digest of technical papers Ročník 54; číslo 1; s. 1856 - 1859
Hlavní autoři: Liu, Peng, Liu, Bailing, Feng, Jing, Wang, Zhichong, Chang, Chuanchuan, Zhang, Qian, Zhang, Han, Liu, Dong, Guo, Xu, Zhang, Xin, Liu, Xingyao, Yuan, Guangcai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Campbell Wiley Subscription Services, Inc 01.06.2023
Témata:
Device DC Model
GA(Genetic Algorithm)
Genetic algorithms
Machine learning
Parameters
Particle swarm optimization
Polysilicon
PSO(Particle Swarm Optimization Algorithm)
Semiconductor devices
SPICE(Simulation program with integrated circuit emphasis)
Swarm Intelligence
Thin film transistors
ISSN:0097-966X, 2168-0159
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Shrnutí:Automatic parameter extraction of RPI Model for Polysilicon Thin‐Film Transistors is achieved by genetic algorithm(GA) and Particle swarm optimization(PSO) algorithm, and the solution of two algorithms are compared. Furthermore, mutual learning particle swarm optimization (MLPSO) algorithm is proposed, which simplifies the complex manual processes and the empirical calibration, and achieves accurate parameters extraction.
Bibliografie:https://doi.org/10.1002/jsid.1224
Authors that wish to refer to this work are advised to cite the full‐length version by referring to its DOI
https://sid.onlinelibrary.wiley.com/doi/full/10.1002/jsid.1224
Poster P‐61 has been designated as a Distinguished Poster at Display Week 2023. The full‐length version of this poster appears in a Special Section of the Journal of the Society for Information Display (JSID) devoted to Display Week 2023 Distinguished Papers. This Special Section will be freely accessible until December 31, 2023 via
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ISSN:0097-966X
2168-0159
DOI:10.1002/sdtp.16970