{"data":{"id":"10.6084/m9.figshare.c.3875881","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.3875881","prefix":"10.6084","suffix":"m9.figshare.c.3875881","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Jingzhao Zhang","affiliation":[],"nameIdentifiers":[]},{"name":"Pegard, Nicolas","nameType":"Personal","givenName":"Nicolas","familyName":"Pegard","affiliation":[],"nameIdentifiers":[]},{"name":"Jingshan Zhong","affiliation":[],"nameIdentifiers":[]},{"name":"Adesnik, Hillel","nameType":"Personal","givenName":"Hillel","familyName":"Adesnik","affiliation":[],"nameIdentifiers":[]},{"name":"Waller, Laura","nameType":"Personal","givenName":"Laura","familyName":"Waller","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"3D Computer Generated Holography by Nonconvex Optimization"}],"publisher":"Figshare","container":{},"publicationYear":2017,"subjects":[{"subject":"Uncategorized"},{"subject":"90606 Photonics and Electro-Optical Engineering (excl. Communications)","subjectScheme":"FOR"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2017-09-09","dateType":"Created"},{"date":"2017-10-18","dateType":"Updated"},{"date":"2017","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1364/optica.4.001306","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC BY 4.0","rightsUri":"https://creativecommons.org/licenses/by/4.0"}],"descriptions":[{"description":"We propose a non-convex optimization algorithm for improved 3D computer generated holography. Our algorithm solves for the 2D phase in Fourier space that best approximates a user-specified volumetric intensity pattern near the image plane. We introduce a customizable cost function that allows for application-specific solutions in neural photostimulation, machining or 3D lithography, where intensity responses may be nonlinear. By using a thresholded multi-part cost function for binary target intensities, we demonstrate robustness and high-fidelity patterning across a large number of depth planes. Our algorithm out-performs existing techniques in both simulation and experiment.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://figshare.com/collections/3D_Computer_Generated_Holography_by_Nonconvex_Optimization/3875881","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":1,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2017-10-18T13:48:02.000Z","registered":"2017-10-18T13:48:02.000Z","published":"2017","updated":"2024-12-19T10:34:43.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.c.3875881","type":"media"}},"references":{"data":[{"id":"10.1364/optica.4.001306","type":"dois"}]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}