{"title":"机器学习辅助的新型光伏优化定制超薄碲基太阳能电池","authors":"Erman Cokduygulular, Caglar Cetinkaya","doi":"10.1002/solr.202500455","DOIUrl":null,"url":null,"abstract":"<p>This study presents a machine learning-based design framework utilizing deep Q-learning (DQL) to optimize ultra-thin CdTe solar cells with active layer thicknesses ranging from 100 to 400 nm. By coupling the transfer matrix method for optical analysis with SCAPS-1D simulations for electrical modeling, the DQL agent effectively explored the complex parameter space, optimizing the thicknesses of all key layers, including SnO<sub>2</sub>, CdS, CdTe, MoO<sub>3</sub>, and Au. The DQL framework intelligently adjusted each layer based on electromagnetic wave propagation and absorption profiles, enhancing internal reflection and light trapping within sub-micron geometries. Even at extremely low absorber thicknesses (e.g., 100 nm), the optimized structures achieved high photovoltaic performance, with power conversion efficiencies up to 9.39% and <i>J</i><sub>sc</sub> values exceeding 11 mA/cm<sup>2</sup>. At 400 nm, efficiency increased to 15.75% with <i>J</i><sub>sc</sub> of 20.86 mA/cm<sup>2</sup>. These results demonstrate that efficient photon harvesting and carrier transport are achievable through full-stack optimization. External quantum efficiency and absorption spectra confirmed the integrated optical-electrical enhancement achieved by DQL. This work highlights the capabilities of reinforcement learning in high-dimensional solar cell design problems and provides a scalable approach for developing next-generation, lightweight, efficient, and material-conscious photovoltaic technologies.</p>","PeriodicalId":230,"journal":{"name":"Solar RRL","volume":"9 18","pages":""},"PeriodicalIF":6.0000,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Assisted Novel Photovoltaic Optimization for Tailored Ultra-Thin CdTe-Based Solar Cells\",\"authors\":\"Erman Cokduygulular, Caglar Cetinkaya\",\"doi\":\"10.1002/solr.202500455\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study presents a machine learning-based design framework utilizing deep Q-learning (DQL) to optimize ultra-thin CdTe solar cells with active layer thicknesses ranging from 100 to 400 nm. By coupling the transfer matrix method for optical analysis with SCAPS-1D simulations for electrical modeling, the DQL agent effectively explored the complex parameter space, optimizing the thicknesses of all key layers, including SnO<sub>2</sub>, CdS, CdTe, MoO<sub>3</sub>, and Au. The DQL framework intelligently adjusted each layer based on electromagnetic wave propagation and absorption profiles, enhancing internal reflection and light trapping within sub-micron geometries. Even at extremely low absorber thicknesses (e.g., 100 nm), the optimized structures achieved high photovoltaic performance, with power conversion efficiencies up to 9.39% and <i>J</i><sub>sc</sub> values exceeding 11 mA/cm<sup>2</sup>. At 400 nm, efficiency increased to 15.75% with <i>J</i><sub>sc</sub> of 20.86 mA/cm<sup>2</sup>. These results demonstrate that efficient photon harvesting and carrier transport are achievable through full-stack optimization. External quantum efficiency and absorption spectra confirmed the integrated optical-electrical enhancement achieved by DQL. This work highlights the capabilities of reinforcement learning in high-dimensional solar cell design problems and provides a scalable approach for developing next-generation, lightweight, efficient, and material-conscious photovoltaic technologies.</p>\",\"PeriodicalId\":230,\"journal\":{\"name\":\"Solar RRL\",\"volume\":\"9 18\",\"pages\":\"\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2025-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Solar RRL\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/solr.202500455\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Solar RRL","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/solr.202500455","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Machine Learning-Assisted Novel Photovoltaic Optimization for Tailored Ultra-Thin CdTe-Based Solar Cells
This study presents a machine learning-based design framework utilizing deep Q-learning (DQL) to optimize ultra-thin CdTe solar cells with active layer thicknesses ranging from 100 to 400 nm. By coupling the transfer matrix method for optical analysis with SCAPS-1D simulations for electrical modeling, the DQL agent effectively explored the complex parameter space, optimizing the thicknesses of all key layers, including SnO2, CdS, CdTe, MoO3, and Au. The DQL framework intelligently adjusted each layer based on electromagnetic wave propagation and absorption profiles, enhancing internal reflection and light trapping within sub-micron geometries. Even at extremely low absorber thicknesses (e.g., 100 nm), the optimized structures achieved high photovoltaic performance, with power conversion efficiencies up to 9.39% and Jsc values exceeding 11 mA/cm2. At 400 nm, efficiency increased to 15.75% with Jsc of 20.86 mA/cm2. These results demonstrate that efficient photon harvesting and carrier transport are achievable through full-stack optimization. External quantum efficiency and absorption spectra confirmed the integrated optical-electrical enhancement achieved by DQL. This work highlights the capabilities of reinforcement learning in high-dimensional solar cell design problems and provides a scalable approach for developing next-generation, lightweight, efficient, and material-conscious photovoltaic technologies.
Solar RRLPhysics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
12.10
自引率
6.30%
发文量
460
期刊介绍:
Solar RRL, formerly known as Rapid Research Letters, has evolved to embrace a broader and more encompassing format. We publish Research Articles and Reviews covering all facets of solar energy conversion. This includes, but is not limited to, photovoltaics and solar cells (both established and emerging systems), as well as the development, characterization, and optimization of materials and devices. Additionally, we cover topics such as photovoltaic modules and systems, their installation and deployment, photocatalysis, solar fuels, photothermal and photoelectrochemical solar energy conversion, energy distribution, grid issues, and other relevant aspects. Join us in exploring the latest advancements in solar energy conversion research.