Synapse simulation method and system for resistive random access memory

Abstract

The embodiment of the invention provides a synapse simulation method and system for a resistive random access memory. The method comprises the following steps: applying a first sine wave signal to a top electrode of a target resistive random access memory at a first moment; applying a second sine wave signal to the bottom electrode of the target resistive random access memory at a second moment; adjusting the time interval between the first moment and the second moment, adjusting the frequency of the first sine wave signal and the frequency of the second sine wave signal, and obtaining a synaptic simulation curve of the target resistive random access memory; and performing synapse simulation on the target resistive device according to the synapse simulation curve of the target resistive device. According to the synapse simulation method and system for the resistive random access memory, synapse plasticity of the resistive random access memory can be tested, the change relation betweenthe synapse of the resistive random access memory and the two aspects of time and frequency is obtained, and therefore when the resistive random access memory is used, the resistive random access memory can better simulate the synapse.

Description

technical field [0001] The invention relates to the technical field of semiconductor integrated circuits and their manufacture, in particular to a synapse simulation method and system for resistive switching devices. Background technique [0002] Synapse is the basic building block of the nervous system. It has plasticity. It adjusts the weight according to the stimulation and frequency of electrical signals, so as to achieve the function of learning and memory. Resistive change device (referred to as RRAM) is a non-volatile memory device that records and stores data information based on resistance changes. The continuous resistance change state can be used to simulate various biological behaviors, such as SRDP, STDP, LTP and other learning rules. [0003] Currently, synaptic plasticity can be divided into two categories according to the length of time that synaptic weights are maintained: short-term synaptic plasticity and long-term synaptic plasticity. It is generally co...

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Problems solved by technology

[0007] When using RRAM devices to simulate synaptic plasticity, in the prior art, only pulse timing-dependent plasticity or frequency-dependent plasticity can be simulated alone, and timing-dependent plasticity and frequency-dependent plasticity cannot be simulated simultaneously, which makes the synaptic simulation accuracy of resistive switching devices relatively low. Low

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