A memristor has been proposed as an artificial synapse for emerging neuromorphic computing applications as AI hardware. To train a neural network in memristor arrays, changes in weight values in the form of conductance should be distinct and uniform. An electrochemical metallization (ECM) memory, typically based on silicon (Si), has demonstrated a good analogue switching capability due to the high mobility of metal ions in the Si switching medium. However, the large stochasticity of the ion movement results in switching variability. I have demonstrated a Si memristor with alloyed conduction channels that shows a reliable device operation, which enables the large-scale implementation of crossbar arrays. In addition, heterogeneously-integrated neuromorphic chips have been developed to achieve physically reconfigurable neuromorphic computing. In this talk, alloyed memristors and neuromorphic chips with heterogeneous integration for reliable and reconfigurable neuromorphic computing are discussed.
Thesis Supervisor: Prof. Jeehwan Kim (MechE)
To attend this defense, please contact the doctoral candidate at chanyeol at mit dot edu