2026-09-18T19:34:41Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1401432022-02-08T03:36:54Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Kim, Jeehwan
Choi, Chanyeol
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-02-07T15:26:36Z
2022-02-07T15:26:36Z
2021-09
2021-09-21T19:30:57.615Z
https://hdl.handle.net/1721.1/140143
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.
Ph.D.
Massachusetts Institute of Technology
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http://rightsstatements.org/page/InC-EDU/1.0/
Memristor-based AI Hardware for Reliable and Reconfigurable Neuromorphic Computing
Thesis
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Doctoral
Doctor of Philosophy
Publication
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<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>
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