Switching Dynamics in Vanadium Dioxide-Based Stochastic Thermal Neurons

• Published in: IEEE transactions on electron devices Vol. 69; no. 6; pp. 3135 - 3141
• Main Authors: Yu, Haoming, Islam, A. N. M. Nafiul, Mondal, Sandip, Sengupta, Abhronil, Ramanathan, Shriram
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neuron; computing; Current measurement; Devices; ENGINEERING; Heating systems; Homeostasis; Neural networks; Neurons; Oxygen consumption; phase transition; Phase transitions; Pulse amplitude; Pulse duration; Room temperature; Switches; Switching; Temperature measurement; Vanadium dioxide; vanadium oxide (VO₂); Voltage measurement; Voltage pulses; X-ray scattering
• ISSN: 0018-9383; 1557-9646
• DOI: 10.1109/TED.2022.3168248

We report on switching dynamics of individual and coupled vanadium dioxide (VO 2 ) 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 VO 2 devices has been noted in current transient plots and was found to depend on temperature, pulsewidth, and pulse amplitude. Experimental switching behavior measured from VO 2 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.