TY - JOUR
T1 - Switching Dynamics in Vanadium Dioxide-Based Stochastic Thermal Neurons
AU - Yu, Haoming
AU - Islam, A. N.M.Nafiul
AU - Mondal, Sandip
AU - Sengupta, Abhronil
AU - Ramanathan, Shriram
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - We report on switching dynamics of individual and coupled vanadium dioxide (VO2) devices subject to voltage pulses as the temperature is systematically varied from room temperature spanning the insulator-metal transition (IMT) temperature. The switching voltage of single devices has a strong relationship with both temperature and voltage pulsewidth. Two-step switching in connected VO2 devices has been noted in current transient plots and was found to depend on temperature, pulsewidth, and pulse amplitude. Experimental switching behavior measured from VO2 artificial neurons was implemented into a spiking neural network (SNN). During training, modulating the switching voltage via temperature affords a novel method to implement homeostasis with the coupled devices. Simulation results show the efficacy of the stochastic neuronal characteristics and the proposed homeostasis mechanism on a standard digit recognition task. These studies contribute to ongoing efforts in neuromorphic computing exploiting collective phase transitions.
AB - We report on switching dynamics of individual and coupled vanadium dioxide (VO2) devices subject to voltage pulses as the temperature is systematically varied from room temperature spanning the insulator-metal transition (IMT) temperature. The switching voltage of single devices has a strong relationship with both temperature and voltage pulsewidth. Two-step switching in connected VO2 devices has been noted in current transient plots and was found to depend on temperature, pulsewidth, and pulse amplitude. Experimental switching behavior measured from VO2 artificial neurons was implemented into a spiking neural network (SNN). During training, modulating the switching voltage via temperature affords a novel method to implement homeostasis with the coupled devices. Simulation results show the efficacy of the stochastic neuronal characteristics and the proposed homeostasis mechanism on a standard digit recognition task. These studies contribute to ongoing efforts in neuromorphic computing exploiting collective phase transitions.
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U2 - 10.1109/TED.2022.3168248
DO - 10.1109/TED.2022.3168248
M3 - Article
AN - SCOPUS:85129585095
SN - 0018-9383
VL - 69
SP - 3135
EP - 3141
JO - IEEE Transactions on Electron Devices
JF - IEEE Transactions on Electron Devices
IS - 6
ER -