Artificial Neurons and Synapses Based on Al/a-SiNxOy:H/P+-Si Device with Tunable Resistive Switching from Threshold to Memory

As the building block of brain-inspired computing, resistive switching memory devices have recently attracted great interest due to their biological function to mimic synapses and neurons, which displays the memory switching or threshold switching characteristic. To make it possible for the Si-based artificial neurons and synapse to be integrated with the neuromorphic chip, the tunable threshold and memory switching characteristic is highly in demand for their perfect compatibility with the mature CMOS technology. We first report artificial neurons and synapses based on the Al/a-SiNxOy:H/P+-Si device with the tunable switching from threshold to memory can be realized by controlling the compliance current. It is found that volatile TS from Al/a-SiNxOy:H/P+-Si device under the lower compliance current is induced by the weak Si dangling bond conductive pathway, which originates from the broken Si-H bonds. While stable nonvolatile MS under the higher compliance current is attributed to the strong Si dangling bond conductive pathway, which is formed by the broken Si-H and Si-O bonds. Theoretical calculation reveals that the conduction mechanism of TS and MS agree with P-F model, space charge limited current model and Ohm's law, respectively. The tunable TS and MS characteristic of Al/a-SiNxOy:H/P+-Si device can be successfully employed to mimic the biological behavior of neurons and synapse including the integrate-and-fire function, paired-pulse facilitation, long-term potentiation and long-term depression as well as spike-timing-dependent plasticity. Our discovery supplies an effective way to construct the neuromorphic devices for brain-inspired computing in the AI period.

Automated summary

Resistive switching memory devices, as the building block of brain-inspired computing, have attracted great interest due to their ability to mimic synapses and neurons. This study presents artificial neurons and synapses based on the Al/a-SiNxOy:H/P+-Si device, which exhibit tunable switching characteristics from threshold to memory. By controlling the compliance current, the researchers were able to induce volatile threshold switching and stable nonvolatile memory switching. The conduction mechanisms of these switches were found to agree with established models. This discovery offers a promising approach to constructing neuromorphic devices for brain-inspired computing in the era of AI.