A probabilistic compact model of ReRAM memories for accurate and high-performance simulation

IF 1.4 4区物理与天体物理 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Solid-state Electronics Pub Date : 2026-06-01 Epub Date: 2026-02-05 DOI:10.1016/j.sse.2026.109349

S. Guitarra, M. Gavilánez, J. Cevallos, A. Vélez

{"title":"A probabilistic compact model of ReRAM memories for accurate and high-performance simulation","authors":"S. Guitarra, M. Gavilánez, J. Cevallos, A. Vélez","doi":"10.1016/j.sse.2026.109349","DOIUrl":null,"url":null,"abstract":"<div><div>This work presents a compact, circuit-level model for resistive random-access memories (ReRAMs) that combines physical consistency with computational efficiency. Within a memristive framework, device history is explicitly captured through a state variable describing the cumulative evolution of the active region of the conductive filament. The filament transition region is modeled as a network of parallel stochastic conductive paths governed by voltage-dependent switching probabilities calibrated from experimental data, enabling accurate reproduction of intrinsic IV variability. Electrical transport is described using closed-form expressions that capture ohmic conduction in the low-resistance state and nonlinear behavior in the high-resistance state. The model is fully implemented in HSPICE and calibrated using HfO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>-based 1T1R devices. Circuit-level validation demonstrates accurate reproduction of electrical characteristics, variability, multilevel operation, and logic-in-memory functionality.</div></div>","PeriodicalId":21909,"journal":{"name":"Solid-state Electronics","volume":"234 ","pages":"Article 109349"},"PeriodicalIF":1.4000,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Solid-state Electronics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038110126000195","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/2/5 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

This work presents a compact, circuit-level model for resistive random-access memories (ReRAMs) that combines physical consistency with computational efficiency. Within a memristive framework, device history is explicitly captured through a state variable describing the cumulative evolution of the active region of the conductive filament. The filament transition region is modeled as a network of parallel stochastic conductive paths governed by voltage-dependent switching probabilities calibrated from experimental data, enabling accurate reproduction of intrinsic IV variability. Electrical transport is described using closed-form expressions that capture ohmic conduction in the low-resistance state and nonlinear behavior in the high-resistance state. The model is fully implemented in HSPICE and calibrated using HfO

_{2}

-based 1T1R devices. Circuit-level validation demonstrates accurate reproduction of electrical characteristics, variability, multilevel operation, and logic-in-memory functionality.

查看原文本刊更多论文

一个精确和高性能仿真的概率紧凑模型

这项工作提出了一种紧凑的电路级电阻随机存取存储器（reram）模型，该模型结合了物理一致性和计算效率。在忆阻框架内，器件历史通过描述导电丝有源区域的累积演化的状态变量被明确捕获。灯丝过渡区被建模为由实验数据校准的电压相关开关概率控制的并行随机导电路径网络，从而能够精确再现固有的IV可变性。电输运是用封闭形式的表达式来描述的，它捕获了低电阻状态下的欧姆传导和高电阻状态下的非线性行为。该模型在HSPICE中完全实现，并使用基于hfo2的1T1R器件进行校准。电路级验证演示了电特性、可变性、多电平操作和内存逻辑功能的准确再现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Solid-state Electronics 物理-工程：电子与电气

CiteScore

3.00

自引率

5.90%

发文量

212

审稿时长

3 months

期刊介绍： It is the aim of this journal to bring together in one publication outstanding papers reporting new and original work in the following areas: (1) applications of solid-state physics and technology to electronics and optoelectronics, including theory and device design; (2) optical, electrical, morphological characterization techniques and parameter extraction of devices; (3) fabrication of semiconductor devices, and also device-related materials growth, measurement and evaluation; (4) the physics and modeling of submicron and nanoscale microelectronic and optoelectronic devices, including processing, measurement, and performance evaluation; (5) applications of numerical methods to the modeling and simulation of solid-state devices and processes; and (6) nanoscale electronic and optoelectronic devices, photovoltaics, sensors, and MEMS based on semiconductor and alternative electronic materials; (7) synthesis and electrooptical properties of materials for novel devices.