新型忆阻器神经形态电路的设计及其在条件反射行为中的应用

Design of novel memristor-based neuromorphic circuit and its application in classical conditioning

突触是生物神经系统的重要组成部分.忆阻器因具备连续可调的非线性电导,与连接强度可连续调节的生物突触极为相似,因此在构建人工突触及类脑系统方面引起了广泛研究.本文制备了Cu/MXene/SiO2/W结构的忆阻器,基于该器件的电学特性、尤其是电导的连续可调特性,构建人工突触单元并设计了神经形态电路.在该电路中,通过施加连续的电压脉冲,对人工突触单元进行训练,成功模仿实现了经典条件反射行为.这一工作将对未来基于忆阻器构建大规模神经形态系统以进行类脑智能运算具有重要的意义.

Inspired by the working mechanism of human brain, the artificial neural network attracts great interest for its capability of parallel processing, which is favored by big data task. However, the electronic synapse based on CMOS neural network needs at least ten transistors to realize one biological synaptic function. So, CMOS-based neural network exhibits obvious weakness in speed, power consumption, circuit area and resource utilization and so on, compared with biological synapses. Therefore, how to build neuromorphic circuits and realize biological functions by constructing electronic synapses with low power consumption and high integration density have become the key points for human to realize brain-like computing system.Memristors, as the fourth basic component, is a two-terminal nonlinear device possessing nonlinear conductance that can be tuned continuously. For that special characteristic, it is very similar to biological synapse whose connection strength can be adjusted continuously. In this article, first of all, we study the electrical characteristic of the Cu/MXene/SiO2/W memristor. When applying a positive DC sweeping voltage to the Cu electrode, the Cu electrode is oxidized, generating Cu2+. The generated Cu2+ in function layer tends tomove towards the bottom electrode under the action of electric field. Near the bottom electrode the Cu2+moving from top electrode are reduced, generating a conductive Cu atom. With Cu atoms accumulating and extending from bottom electrode to top electrode, the memristor is gradually converted from the initial high resistance state(HRS) into the low resistance state(LRS). Secondly, combining with HP model of memristor,we utilize Verilog A language to simulate memristor in the experiment we conducted. Subsequently, we successfully construct the artificial synaptic unit and design the weight differential circuit with self-feedback branch. In the above circuit, we successfully implementa classical "Pavlov’s dog" experiment. By applying the sinusoidal signal and pulse signal to the synaptic unit for testing and training it, respectively, the circuit realizes the convention between the conditions that unconditioned stimulus producing unconditioned response to conditioned stimulus producing conditions response.This work takes memristor as a center, through modelling the electrical characteristic of Cu/MXene/SiO2/W device, we construct a neuromorphic circuit with weight differential branch andself-feedback branch,successfully simulate the classical learning behavior of biological synapses, and realizes the whole process of biologically conditioned reflex, which is illustrated in detail in the experiment on "Pavlov′s dog". The results will provide effective guidance forconstructing a large scale and high density neuromorphic circuitbased on memristor, thus promoting the realization of brain-like computation in the future.