<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-20T10:41:12Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/152020" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/152020</identifier><datestamp>2023-09-01T03:01:08Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor">Sarkar, Deblina</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author">Kajale, Shivam Nitin</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Program in Media Arts and Sciences (Massachusetts Institute of Technology)</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2023-08-30T16:01:03Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2023-08-30T16:01:03Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued">2023-02</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="submitted">2023-08-16T20:36:13.472Z</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">https://hdl.handle.net/1721.1/152020</dim:field>
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   <dim:field mdschema="dc" element="description" qualifier="abstract">Energy consumption of artificial intelligence (AI) systems are projected to grow at an alarming rate over the next two decades and stand to stress the global energy sector. A way forward is to replace the traditional von-Neumann computing hardware with technologies like neuromorphic and stochastic computing which are better suited for AI applications. Here, I study van der Waals magnetic materials for their application in developing spintronic devices to form the building blocks of neuromorphic and stochastic computing architectures. Use of correlated systems like ferromagnets provides a way towards low energy device switching, while 2D nature of the materials provides an avenue for building spintronic devices with maximum dimensional&#xd;
scalability and have strong prospects of enabling highly energy efficient mechanism of switching magnetism. A reliable protocol for fabricating devices with air-sensitive vdW magnetic materials and characterising them has been developed, including the electrochemical exfoliation of bulk vdW crystals, the design and building of a 2D material transfer setup and nanofabrication of devices using lithography, and magneto-transport measurements. This work will serve as a strong foundation for future work which would involve developing spin-valve devices with vdW materials and exploring energy-efficient modes of switching magnetism in them.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree">S.M.</dim:field>
   <dim:field mdschema="dc" element="publisher">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights">In Copyright - Educational Use Permitted</dim:field>
   <dim:field mdschema="dc" element="rights">Copyright MIT</dim:field>
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   <dim:field mdschema="dc" element="title">Study of vdW Magnetic Materials for Spintronic Applications</dim:field>
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   	&lt;Title>Study of vdW Magnetic Materials for Spintronic Applications&lt;/Title>
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   	&lt;PublicationDate>2023-02&lt;/PublicationDate>
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        	&lt;DisplayName>Kajale, Shivam Nitin&lt;/DisplayName>
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   	&lt;Abstract>Energy consumption of artificial intelligence (AI) systems are projected to grow at an alarming rate over the next two decades and stand to stress the global energy sector. A way forward is to replace the traditional von-Neumann computing hardware with technologies like neuromorphic and stochastic computing which are better suited for AI applications. Here, I study van der Waals magnetic materials for their application in developing spintronic devices to form the building blocks of neuromorphic and stochastic computing architectures. Use of correlated systems like ferromagnets provides a way towards low energy device switching, while 2D nature of the materials provides an avenue for building spintronic devices with maximum dimensional&#xd;
scalability and have strong prospects of enabling highly energy efficient mechanism of switching magnetism. A reliable protocol for fabricating devices with air-sensitive vdW magnetic materials and characterising them has been developed, including the electrochemical exfoliation of bulk vdW crystals, the design and building of a 2D material transfer setup and nanofabrication of devices using lithography, and magneto-transport measurements. This work will serve as a strong foundation for future work which would involve developing spin-valve devices with vdW materials and exploring energy-efficient modes of switching magnetism in them.&lt;/Abstract>
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