{"title":"10 Years of development of first-order search and evaluation tools for the design of complex zoom lenses","authors":"J. Bentley","doi":"10.1117/12.2603620","DOIUrl":"https://doi.org/10.1117/12.2603620","url":null,"abstract":"","PeriodicalId":231516,"journal":{"name":"International Optical Design Conference 2021","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120963250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Methodology for the rapid design of an objective lens for multiphoton microscopy from off-the-shelf lenses with no a priori design","authors":"Michael D. Young","doi":"10.1117/12.2603622","DOIUrl":"https://doi.org/10.1117/12.2603622","url":null,"abstract":"","PeriodicalId":231516,"journal":{"name":"International Optical Design Conference 2021","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115459813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the use of deep learning for lens design","authors":"Geoffroi Côté, Jean-François Lalonde, S. Thibault","doi":"10.1117/12.2603656","DOIUrl":"https://doi.org/10.1117/12.2603656","url":null,"abstract":"Data-driven methods to assist lens design have recently begun to emerge, in particular under the form of lens design extrapolation: using machine learning, the features of successful lens design forms can be extracted, then recombined to create new designs. Here, we discuss the core aspects and next challenges of the LensNet framework, a deep learning-enabled tool that leverages lens design extrapolation as a more powerful alternative to lens design databases when searching for starting points. We also propose to borrow ideas and tools from the practice of machine learning and deep learning, and integrate them into standard lens design optimization. Namely, we recommend using automatic differentiation to power ray tracing engines, along with considering recent and powerful first-order gradient-based optimizers, and using data-driven glass models that are more suited for optimization than traditional variables.","PeriodicalId":231516,"journal":{"name":"International Optical Design Conference 2021","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117208126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Beadie, Michael E. Hus, Andrew Wright, T. Winski, Ramzi N. Zahreddine, R. Lepkowicz
{"title":"Multilayer polymer GRIN singlets: manufacturing and performance","authors":"G. Beadie, Michael E. Hus, Andrew Wright, T. Winski, Ramzi N. Zahreddine, R. Lepkowicz","doi":"10.1117/12.2603646","DOIUrl":"https://doi.org/10.1117/12.2603646","url":null,"abstract":"The design and performance of two multilayer polymer gradient index (GRIN) singlets are discussed. One singlet is an f/4 monochromat based on an axial GRIN geometry. The other is an f/6 achromat based on a spherical GRIN geometry. The design for each lens was modified to account for as-manufactured GRIN contours and final layer thicknesses. Asmanufactured performance for each lens was consistent with the performance of a commercial, air-spaced doublet predicted to be diffraction-limited at 532 nm, within the resolution of our setup for measuring the point spread functions of our lens elements.","PeriodicalId":231516,"journal":{"name":"International Optical Design Conference 2021","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125948465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}