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

Abstract

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.

Artificial Neurons Using Ag−In−Zn−S/Sericin Peptide‐Based Threshold Switching Memristors for Spiking Neural Networks

“…[16,19] The TS behavior is most likely elucidated by the decomposition of weak partially-formed Ag CFs assisted by Joule heating. [8] Investigation of the behavior of sericin under injection of Ag ions has not employed from the device itself due to the difficulty of etching the Ag TE to expose the sericin layer or slicing the sericin layer for the later characterization, which will be characterized by TEM in future research. In addition, the potential scalability of sericin-based device is worth further investigating but many challenging issues need to be solved, such as developing an effective solution-processed technique for fabricating high-quality and large-scale uniform material film, utilizing a sacrificial layer (e.g., a parylene layer) to make sericin compatible with complementary metal-oxide-semiconductor (CMOS) fabrication processes, scaling device to 10 nm dimension, exploring thermal stability, and exploiting advanced characterization methods to clarify the mechanism.…”

“…[6,7] In order to guarantee the synapse matrix works effectively, volatile threshold switching (TS) devices are needed to simulate integrate-and-fire function of neurons. [8] Nevertheless, many neural devices used for SNNs are the mainstream complementary metal-oxidesemiconductor (CMOS) neurons that are indispensable for realizing of sophisticated circuits, which are considered unsuitable for large-scale network implementation due to their large area and low efficiency. [9,10] As a result, it is urgently expected to develop functional neuronal devices that enable the achievements of the basic functions of spiking neurons including the integration behavior and the firing action with simple circuits.…”

Состав и оптические свойства аморфного плазмохимического оксинитрида кремния переменного состава a-SiO-=SUB=-x-=/SUB=-N-=SUB=-y-=/SUB=- : H

“…Использование процессов с низким термическим бюджетом крайне важно, так как позволяет получать мемристорные структуры в конце всех технологических процессов (так называемые back-end-of-line процессы [10]). Недавно была продемонстрирована возможность создания мемристоров на основе пленок гидрогенизированного оксинитрида кремния (a−SiO x N y : H) для нейронных сетей [11]. Поскольку метод РФЭС является поверхностночувствительным, была проведена оценка глубины анализа изученных образцов.…”

Composition and optical properties of amorphous plasma-chemical silicon oxynitride of variable composition a-SiO-=SUB=-x-=/SUB=-N-=SUB=-y-=/SUB=- : H

“…The use of processes with a low thermal budget is extremely important, as it allows obtaining memristor structures at the end of all technological processes (the so-called back-end-of-line processes [10]). Recently, the possibility of creating memristors based on films of hydrogenated silicon oxynitride (a−SiO x N y : H) for neural networks [11] has been demonstrated.…”