Machine learning enhanced in situ electron beam lithography of photonic nanostructures

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dc.contributor.authorDonges, Jan
dc.contributor.authorSchlischka, Marvin
dc.contributor.authorShih, Ching-Wen
dc.contributor.authorPengerla, Monica
dc.contributor.authorLimame, Imad
dc.contributor.authorSchall, Johannes
dc.contributor.authorBremer, Lucas
dc.contributor.authorRodt, Sven
dc.contributor.authorReitzenstein, Stephan
dc.date.accessioned2022-11-15T15:50:17Z
dc.date.available2022-11-15T15:50:17Z
dc.date.issued2022-09-20
dc.description.abstractWe report on the deterministic fabrication of quantum devices aided by machine-learning-based image processing. The goal of the work is to demonstrate that pattern recognition based on specifically trained machine learning (ML) algorithms and applying it to luminescence maps can strongly enhance the capabilities of modern fabrication technologies that rely on a precise determination of the positions of quantum emitters like, for instance, in situ lithography techniques. In the present case, we apply in situ electron beam lithography (EBL) to deterministically integrate single InGaAs quantum dots (QDs) into circular Bragg grating resonators with increased photon extraction efficiency (PEE). In this nanotechnology platform, suitable QDs are selected by 2D cathodoluminescence maps before EBL of the nanoresonators aligned to the selected emitters is performed. Varying the electron beam dose of cathodoluminescence (CL) mapping, we intentionally change the signal-to-noise ratio of the CL maps to mimic different brightness of the emitters and to train the ML algorithm. ML-based image processing is then used to denoise the images for reliable and accurate QD position retrieval. This way, we achieve a significant enhancement in the PEE and position accuracy, leading to more than one order increase of sensitivity in ML-enhanced in situ EBL. Overall, this demonstrates the high potential of ML-based image processing in deterministic nanofabrication which can be very attractive for the fabrication of bright quantum light sources based on emitters with low luminescence yield in the future.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2022
dc.description.sponsorshipBMBF, 16KISQ014, Verbundprojekt: Quantenrepeater.Link - QR.X
dc.identifier.eissn2040-3372
dc.identifier.issn2040-3364
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/17675
dc.identifier.urihttps://doi.org/10.14279/depositonce-16460
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/
dc.subject.ddc540 Chemie und zugeordnete Wissenschaftende
dc.subject.ddc530 Physikde
dc.subject.otherquantum dotsen
dc.subject.otherimage processingen
dc.subject.otherquantum photonic deviceen
dc.subject.othermachine learningen
dc.subject.otherin situ lithographyen
dc.titleMachine learning enhanced in situ electron beam lithography of photonic nanostructuresen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.doi10.1039/D2NR03696G
dcterms.bibliographicCitation.issue39
dcterms.bibliographicCitation.journaltitleNanoscale
dcterms.bibliographicCitation.originalpublishernameRoyal Society of Chemistry (RSC)
dcterms.bibliographicCitation.originalpublisherplaceCambridge
dcterms.bibliographicCitation.pageend14536
dcterms.bibliographicCitation.pagestart14529
dcterms.bibliographicCitation.volume14
oaire.citation.issue39
oaire.citation.volume14
tub.accessrights.dnbfree
tub.affiliationFak. 2 Mathematik und Naturwissenschaften::Inst. Festkörperphysik::AG Optoelektronik und Quantenbauelemente
tub.publisher.universityorinstitutionTechnische Universität Berlin

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