A Spiking Stochastic Neuron Based on Stacked InGaZnO Memristors

Abstract

Spiking encoded stochastic neural network is believed to be energy efficient and biologically plausible and an increasing effort has been made recently to translate its great cognitive power into hardware implementations. Here, a stacked indium–gallium–zinc–oxide (IGZO)‐based threshold switching memristor with essential properties as a spiking stochastic neuron is introduced. Such IGZO spiking stochastic neuron shows a sigmoid firing probability that can be tuned by the amplitude, width, and frequency of the applied pulse sequence. More importantly, the stacked configuration is experimentally demonstrated with eliminated switching variation compared to one single memristor and a narrow relative deviation (≤6.8%) of the firing probability can be achieved. The IGZO stochastic neuron is applied to perform probabilistic unsupervised learning for handwritten digit reconstruction based on a restricted Boltzmann machine and a recognition accuracy of 91.2% can be achieved. Such IGZO stochastic neuron with reproducible firing probability emulates probabilistic computing in the brain, which is of significant importance to hardware implementation of spiking neural network to analyze sensory stimuli, produce adequate motor control, and make reasonable inference.

Emulating Nociceptive Receptor and LIF Neuron Behavior via ZrOx‐based Threshold Switching Memristor

“…Similar stochastic neuron characters have been reported. [ 55,56 ] Although the firing behavior seems to be stochastic, the firing probability (i.e., spiking number) can be modulated by changing the pulse amplitude or pulse width, as presented in Figure 5e,g. The stochastic neuron devices have been applied to neuromorphic computing for the classification of handwritten digits.…”

Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception

“…However, software-based artificial neural networks consume considerable energy and space, and they cannot efficiently simulate the neural network's parallel processing mechanism (Markram, 2006). Recently, various electronic devices, including two-terminal and threeterminal artificial synapses, have been constructed to simulate synaptic behavior (Chang et al, 2011;Kuzum et al, 2012a;Wang et al, 2012b;Kim et al, 2013;Shi et al, 2013;Wan et al, 2014;Zhu et al, 2014;Mao et al, 2021). Two-terminal devices include memristors, phase change memory, atomic switch and so on.…”

Suppressing Undesired Channel Length‐Dependent Electrical Characteristics of Fully Integrated InGaZnO Thin‐Film Transistors via Defect Control Layer

“…[ 5,6 ] Recently, such semiconductors have found many applications beyond display backplanes, for example, as versatile sensors (e.g., detecting distance, pressure, and light), artificial neuromorphic computing, memory devices, and in healthcare systems. [ 7–13 ] Interestingly, Tan et al exhibited imitating biological synaptic behaviors and implementing image detection by using IGZO based sensory device as an artificial photonic synapse with electric potential modulation. [ 7 ]…”

“…[5,6] Recently, such semiconductors have found many applications beyond display backplanes, for example, as versatile sensors (e.g., detecting distance, pressure, and light), artificial neuromorphic computing, memory devices, and in healthcare systems. [7][8][9][10][11][12][13] Interestingly, Tan et al exhibited imitating biological synaptic behaviors and implementing image detection by using IGZO based sensory device as an artificial photonic synapse with electric potential modulation. [7] Despite the mass production in display industry, a gap remains between emerging oxide TFTs and conventional Si-based TFTs in terms of field effect mobility (µ FE ), stability, and process immunity; such issues still limit widespread commercialization of oxide TFTs.…”