{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/140143"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/140143","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Memristor-based AI Hardware for Reliable and Reconfigurable Neuromorphic Computing","abstract":"In the field of artificial intelligence hardware, a memristor has been proposed as an artificial synapse for creating neuromorphic computer applications. Changes in weight values in the form of conductance must be identifiable and uniform to train a neural network in memristor arrays. Because of the high mobility of metal ions in the Si switching medium, an electrochemical metallization (ECM) memory has shown a high analogue switching capacity. However, switching unpredictability is caused by the extreme stochasticity of ion transport. I demonstrated a Si memristor with alloyed conduction channels that works dependably and enables large-scale crossbar array deployment. In addition, heterogeneously integrated neuromorphic chips have been developed to allow physically reconfigurable neuromorphic computing. This thesis examines alloyed metal-based silicon memristors and stackable neuromorphic chips with heterogeneous integration for reliable and reconfigurable neuromorphic computing.","abstract_html":"In the field of artificial intelligence hardware, a memristor has been proposed as an artificial synapse for creating neuromorphic computer applications. Changes in weight values in the form of conductance must be identifiable and uniform to train a neural network in memristor arrays. Because of the high mobility of metal ions in the Si switching medium, an electrochemical metallization (ECM) memory has shown a high analogue switching capacity. However, switching unpredictability is caused by the extreme stochasticity of ion transport. I demonstrated a Si memristor with alloyed conduction channels that works dependably and enables large-scale crossbar array deployment. In addition, heterogeneously integrated neuromorphic chips have been developed to allow physically reconfigurable neuromorphic computing. This thesis examines alloyed metal-based silicon memristors and stackable neuromorphic chips with heterogeneous integration for reliable and reconfigurable neuromorphic computing.","abstract_has_math":false,"creators":["Choi, Chanyeol"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Kim, Jeehwan"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09","date_published":"2021-09","updated_at":"2026-07-22T22:21:35Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/140143","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kim, Jeehwan"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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Changes in weight values in the form of conductance must be identifiable and uniform to train a neural network in memristor arrays. Because of the high mobility of metal ions in the Si switching medium, an electrochemical metallization (ECM) memory has shown a high analogue switching capacity. However, switching unpredictability is caused by the extreme stochasticity of ion transport. I demonstrated a Si memristor with alloyed conduction channels that works dependably and enables large-scale crossbar array deployment. In addition, heterogeneously integrated neuromorphic chips have been developed to allow physically reconfigurable neuromorphic computing. This thesis examines alloyed metal-based silicon memristors and stackable neuromorphic chips with heterogeneous integration for reliable and reconfigurable neuromorphic computing."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Memristor-based AI Hardware for Reliable and Reconfigurable Neuromorphic Computing"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kim, Jeehwan"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Choi, Chanyeol"],"dc:date.accessioned":["2022-02-07T15:26:36Z"],"dc:date.available":["2022-02-07T15:26:36Z"],"dc:date.issued":["2021-09"],"dc:description.abstract":["In the field of artificial intelligence hardware, a memristor has been proposed as an artificial synapse for creating neuromorphic computer applications. Changes in weight values in the form of conductance must be identifiable and uniform to train a neural network in memristor arrays. Because of the high mobility of metal ions in the Si switching medium, an electrochemical metallization (ECM) memory has shown a high analogue switching capacity. However, switching unpredictability is caused by the extreme stochasticity of ion transport. I demonstrated a Si memristor with alloyed conduction channels that works dependably and enables large-scale crossbar array deployment. In addition, heterogeneously integrated neuromorphic chips have been developed to allow physically reconfigurable neuromorphic computing. This thesis examines alloyed metal-based silicon memristors and stackable neuromorphic chips with heterogeneous integration for reliable and reconfigurable neuromorphic computing."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/140143"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Memristor-based AI Hardware for Reliable and Reconfigurable Neuromorphic Computing"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:21:35Z"}