Optoelectronic Artificial Synaptic Device Based on Amorphous InAlZnO Films for Learning Simulations

Abstract

Neuromorphic computing, which mimics brain function, can address the shortcomings of the "von Neumann" system and is one of the critical components of next-generation computing. The use of light to stimulate artificial synapses has the advantages of low power consumption, low latency, and high stability. We demonstrate amorphous InAlZnO-based light-stimulated artificial synaptic devices with a thin-film transistor structure. The devices exhibit fundamental synaptic properties, including excitatory postsynaptic current, paired-pulse facilitation (PPF), and short-term plasticity to long-term plasticity conversion under light stimulation. The PPF index stimulated by 375 nm light is 155.9% when the time interval is 0.1 s. The energy consumption of each synaptic event is 2.3 pJ, much lower than that of ordinary MOS devices and other optical-controlled synaptic devices. The relaxation time constant reaches 277 s after only 10 light spikes, which shows the great synaptic plasticity of the device. In addition, we simulated the learning-forgetting-relearning-forgetting behavior and learning efficiency of human beings under different moods by changing the gate voltage. This work is expected to promote the development of high-performance optoelectronic synaptic devices for neuromorphic computing.

Artificial Synaptic Simulation Using Two-Dimensional Lead-free Li3Sb2I9 Perovskite Films

“…It is concluded that the Al/Li 3 Sb 2 I 9 /ITO device shows competitive memory performance. Particularly, the Li 3 Sb 2 I 9 -based device exhibits the least numbers of voltage pulse to restore the “relearning” performance compared to that of other literature. − ,, Therefore, the present Al/Li 3 Sb 2 I 9 /ITO device has a rapid response ability to simulate the memory function of human brains.…”

“…Synapses, as the most fundamental component of the biological brain, crucially contribute to learning and memory process due to their connections between presynaptic neurons and postsynaptic neurons. , Recently, the memristor has attracted great attention for its potential in simulating the behavior of the biological synapse owing to its adjustable conductance properties. − Generally, the memristor possesses a sandwich structure consisting of two electrodes and a switching layer. The switching layer plays a dominant role in determining the state of conductance, reflecting the behavior state of the biological synapse. − …”

AlGaN/GaN‐Based Optoelectronic Synaptic Devices for Neuromorphic Computing

“…The optical energy consumption per synaptic event of the synaptic is calculated to be 128 fJ, which is comparable favorably to most of optical-stimulated synaptic devices. [20,33] The incorporation of logic functions into synaptic device is essential for neuromorphic computing. [34,35] In complementary metal oxide semiconductor (CMOS) circuits, all logic gate can be formed with complementary pairs of p-MOS and n-MOS.…”

“…The optical energy consumption per synaptic event of the synaptic is calculated to be 128 fJ, which is comparable favorably to most of optical‐stimulated synaptic devices. [ 20,33 ]…”

Low‐operation voltage conductive‐bridge random access memory based on amorphous NbS2

“…[1][2][3][4] ReRAM is a device that uses changes in the resistance to store data, which are called resistive switching (RS) behavior. [5][6][7][8][9] Based on the metal conductive filament (CF) to realize the RS, the conductive-bridge ReRAM (CBRAM) is a very promising one in the next generation of non-volatile memory. [10,11] The diameter of the resistance wire can be only a few nanometers or even sub-nanometers.…”