Abstract
In recent decades, with the rapid development of artificial intelligence technologies and bionic engineering, the spiking neural network (SNN), inspired by biological neural systems, has become one of the most promising research topics, enjoying numerous applications in various fields. Due to its complex structure, the simplification of SNN circuits requires serious consideration, along with their power consumption and space occupation. In this regard, the use of SSN circuits based on single-electron transistors (SETs) and modified memristor synapses is proposed herein. A prominent feature of SETs is Coulomb oscillation, which has characteristics similar to the pulses produced by spiking neurons. Here, a novel window function is used in the memristor model to improve the linearity of the memristor and solve the boundary and terminal lock problems. In addition, we modify the memristor synapse to achieve better weight control. Finally, to test the SNN constructed with SETs and memristor synapses, an associative memory learning process, including memory construction, loss, reconstruction, and change, is implemented in the circuit using the PSPICE simulator.
Highlights
Most human activities are controlled by the brain, which is a low-power-consumption system offering high-speed processing of large amounts of data
Cantley et al [6] proposed a spiking neuron circuit based on nanoscale noncrystalline silicon thin-film transistors (TFTs), which solved the issue of the driving ability of spiking neural network (SNN) perfectly, they ignored the complexity of the circuit
An SNN based on single-electron transistors (SETs) and memristor synapses is proposed, and detailed associative memory-related processes are implemented therein
Summary
Most human activities are controlled by the brain, which is a low-power-consumption system (roughly 10 W [1]) offering high-speed processing of large amounts of data. Cantley et al [6] proposed a spiking neuron circuit based on nanoscale noncrystalline silicon thin-film transistors (TFTs), which solved the issue of the driving ability of SNNs perfectly, they ignored the complexity of the circuit Based on this discussion, devices with the characteristics of a simplified circuit, high integration, and low power dissipation are considerably desirable for use in microelectronics circuit design and related fields. Devices with the characteristics of a simplified circuit, high integration, and low power dissipation are considerably desirable for use in microelectronics circuit design and related fields In this sense, single-electron transistors (SETs) have attracted attention from researchers due to their smaller space occupation, simpler structure, and lower energy consumption compared with traditional FETs [16]. A series of simulations are performed to verify the design
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