Yu Dian Lim;Peng Zhao;Luca Guidoni;Jean-Pierre Likforman;Chuan Seng Tan
{"title":"使用简化变压器模型对混合间距光栅的梁腰进行下一数值预测","authors":"Yu Dian Lim;Peng Zhao;Luca Guidoni;Jean-Pierre Likforman;Chuan Seng Tan","doi":"10.1109/JPHOT.2024.3442169","DOIUrl":null,"url":null,"abstract":"In this study, a simplified transformer model is used to predict the beam waist of 1,092 nm light coupled out from SiN-based mixed pitch gratings at various heights. The beam waists data at various heights above the grating is first compiled. Then, we used a sequence of the current beam waist values, z-positions, and the computed mathematical indicators (features) to predict the next beam waist value (labels). Optimized transformer model yields average percentage error (APE) of 6.6% between the predicted and actual beam waists, which corresponds to 93.4% prediction accuracy. This study provides a pioneering approach to using natural language processing model to perform predictive modelling on photonics data, and possible extrapolation of photonics data using transformer model.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"16 5","pages":"1-9"},"PeriodicalIF":2.1000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634767","citationCount":"0","resultStr":"{\"title\":\"Next-Value Prediction of Beam Waists From Mixed Pitch Grating Using Simplified Transformer Model\",\"authors\":\"Yu Dian Lim;Peng Zhao;Luca Guidoni;Jean-Pierre Likforman;Chuan Seng Tan\",\"doi\":\"10.1109/JPHOT.2024.3442169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, a simplified transformer model is used to predict the beam waist of 1,092 nm light coupled out from SiN-based mixed pitch gratings at various heights. The beam waists data at various heights above the grating is first compiled. Then, we used a sequence of the current beam waist values, z-positions, and the computed mathematical indicators (features) to predict the next beam waist value (labels). Optimized transformer model yields average percentage error (APE) of 6.6% between the predicted and actual beam waists, which corresponds to 93.4% prediction accuracy. This study provides a pioneering approach to using natural language processing model to perform predictive modelling on photonics data, and possible extrapolation of photonics data using transformer model.\",\"PeriodicalId\":13204,\"journal\":{\"name\":\"IEEE Photonics Journal\",\"volume\":\"16 5\",\"pages\":\"1-9\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634767\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Photonics Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10634767/\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Photonics Journal","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10634767/","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Next-Value Prediction of Beam Waists From Mixed Pitch Grating Using Simplified Transformer Model
In this study, a simplified transformer model is used to predict the beam waist of 1,092 nm light coupled out from SiN-based mixed pitch gratings at various heights. The beam waists data at various heights above the grating is first compiled. Then, we used a sequence of the current beam waist values, z-positions, and the computed mathematical indicators (features) to predict the next beam waist value (labels). Optimized transformer model yields average percentage error (APE) of 6.6% between the predicted and actual beam waists, which corresponds to 93.4% prediction accuracy. This study provides a pioneering approach to using natural language processing model to perform predictive modelling on photonics data, and possible extrapolation of photonics data using transformer model.
期刊介绍:
Breakthroughs in the generation of light and in its control and utilization have given rise to the field of Photonics, a rapidly expanding area of science and technology with major technological and economic impact. Photonics integrates quantum electronics and optics to accelerate progress in the generation of novel photon sources and in their utilization in emerging applications at the micro and nano scales spanning from the far-infrared/THz to the x-ray region of the electromagnetic spectrum. IEEE Photonics Journal is an online-only journal dedicated to the rapid disclosure of top-quality peer-reviewed research at the forefront of all areas of photonics. Contributions addressing issues ranging from fundamental understanding to emerging technologies and applications are within the scope of the Journal. The Journal includes topics in: Photon sources from far infrared to X-rays, Photonics materials and engineered photonic structures, Integrated optics and optoelectronic, Ultrafast, attosecond, high field and short wavelength photonics, Biophotonics, including DNA photonics, Nanophotonics, Magnetophotonics, Fundamentals of light propagation and interaction; nonlinear effects, Optical data storage, Fiber optics and optical communications devices, systems, and technologies, Micro Opto Electro Mechanical Systems (MOEMS), Microwave photonics, Optical Sensors.