Abstract

Physical structures changing their resistance in operation can serve as a basis for making elements of neural networks (synapses, neurons, etc.). The processes inducing changes of resistance are rather complicated and cannot be described readily. To demonstrate the potential of this sort of variable resistors it is possible to substitute a complex physical system by a simple mathematical model reproducing the important behavioral characteristics of the actual system. A simple resistor element whose state is defined by a single scalar variable is taken as a model unit. Equations responsible for changes of the state variable are determined. Different functions and parameters that can enter these equations are discussed. Combinations of such elements and conventional electronic components are considered. Measurement methods for variable resistors are investigated. Experimental data are used to determine characteristics of a particular type of variable resistor, metal-insulator-metal structures with amorphous titanium dioxide as insulator. Specific sets of functions defining the “voltage-current” experiment-resembling behavior of a resistor element are presented.

中文翻译：

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神经形态系统电阻元件的建模与表征

摘要

在操作中改变其抵抗力的物理结构可以作为制造神经网络（突触，神经元等）的基础。引起电阻变化的过程相当复杂，不能轻易描述。为了证明这种可变电阻器的潜力，可以通过一个简单的数学模型代替复杂的物理系统，该数学模型再现了实际系统的重要行为特征。以状态为单个标量变量定义的简单电阻器元件作为模型单元。确定引起状态变量变化的方程。讨论了可以输入这些方程式的不同函数和参数。考虑了这些元件和常规电子部件的组合。研究了可变电阻的测量方法。实验数据用于确定特定类型的可变电阻器，以非晶态二氧化钛作为绝缘体的金属-绝缘体-金属结构的特性。介绍了定义电阻器元件“电压-电流”实验相似行为的特定功能集。