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Karl K. Berggren. Onen, O. Murat(Oguzhan Murat) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-07-17T20:59:36Z 2019-07-17T20:59:36Z 2019 2019 https://hdl.handle.net/1721.1/121740 1102050937 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 97-100). In this work, I first construct a unified simulation platform, where superconducting electronics can be designed and optimized with high performance and accuracy. For this purpose, I first select numerical simulation methods that can deal with the highly non-linear characteristics of the superconducting devices. I validate the simulated responses with experimental data on device and circuit level examples. Following the implementation of the simulator, I use this framework to analyze existing superconducting nanowire based technologies, and optimize them for wider operation regimes and higher performance metrics. I use nanofabrication processes to realize these devices and conduct liquid helium immersion measurements to characterize them experimentally. Optimized devices show superior characteristics that demonstrate the predictive capabilities of this simulator. Finally, I use this simulator to design a superconducting nanowire based deep neural network training accelerator. I design, implement, and characterize a unit cell for this application. These local processors have significant device-level advantages over the readily available non-volatile memory technologies in realizing mixed-signal architectures. The devices produced throughout this work have immediate and near-term applications, proving the merit of having a high-performance simulator. by O. Murat Onen. S.M. S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 100 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Construction of a superconducting circuit simulator and its applications in cryogenic computing Thesis application/pdf 2019-07-17T20:59:33Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ff25afe5-e9d3-4750-9c70-ca64e75668b2"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Construction of a superconducting circuit simulator and its applications in cryogenic computing</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Onen, O. Murat(Oguzhan Murat)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>In this work, I first construct a unified simulation platform, where superconducting electronics can be designed and optimized with high performance and accuracy. For this purpose, I first select numerical simulation methods that can deal with the highly non-linear characteristics of the superconducting devices. I validate the simulated responses with experimental data on device and circuit level examples. Following the implementation of the simulator, I use this framework to analyze existing superconducting nanowire based technologies, and optimize them for wider operation regimes and higher performance metrics. I use nanofabrication processes to realize these devices and conduct liquid helium immersion measurements to characterize them experimentally. Optimized devices show superior characteristics that demonstrate the predictive capabilities of this simulator. Finally, I use this simulator to design a superconducting nanowire based deep neural network training accelerator. I design, implement, and characterize a unit cell for this application. These local processors have significant device-level advantages over the readily available non-volatile memory technologies in realizing mixed-signal architectures. The devices produced throughout this work have immediate and near-term applications, proving the merit of having a high-performance simulator.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>