Switching Dynamics in Vanadium Dioxide-Based Stochastic Thermal Neurons
Switching Dynamics in Vanadium Dioxide-Based Stochastic Thermal Neurons
复制标题
二氧化钒基随机热神经元的开关动力学
DOI：
10.1109/ted.2022.3168248
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发表时间：
2022
影响因子：
3.1
通讯作者：
Ramanathan, Shriram
中科院分区：
文献类型：
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作者：
Yu, Haoming;Islam, A. N.;Mondal, Sandip;Sengupta, Abhronil;Ramanathan, Shriram
我们报告了单个和耦合的二氧化钒（VO 2）器件在电压脉冲下的开关动力学，因为温度从室温系统地变化，跨越绝缘体金属转变（IMT）温度。单个器件的开关电压与温度和电压脉宽都有很强的关系。在连接VO2器件的两步开关已注意到在电流瞬态图，并被发现依赖于温度，脉冲宽度和脉冲幅度。实验开关行为从VO2人工神经元测量到实现到一个尖峰神经网络（SNN）。在训练过程中，通过温度调节开关电压提供了一种新颖的方法来实现与耦合器件的稳态。仿真结果表明，在一个标准的数字识别任务的随机神经元特性和建议的稳态机制的有效性。这些研究有助于神经形态计算利用集体相变的持续努力。
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 VO2devices has been noted in current transient plots and was found to depend on temperature, pulsewidth, and pulse amplitude. Experimental switching behavior measured from VO2artificial 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.