Memristor Simulator

An interactive, browser-based simulator for HP Labs Ion-Drift and Yakopcic generalised memristor models. Explore memristive switching behaviour in real time with adjustable parameters, multiple experiment presets, and dark-themed Plotly charts — no backend required.

Try it live →

Features

• Two memristor models — HP Labs Ion-Drift and Yakopcic Generalised, with full parameter control
• 6 experiment presets reproducing published device characterisations (Strukov, Oblea, Miao, Jo)
• Real-time ODE solver — adaptive Dormand-Prince (RK45) integration with dense output interpolation
• Interactive parameter sliders with LaTeX labels and grouped categories (I-V relationship, thresholds, state dynamics)
• Dark-themed Plotly charts — dual-panel time-series and I-V characteristic plots with direction markers
• Educational content — built-in "What is a Memristor?" intro, model descriptions, and parameter tooltips with paper citations
• Fully client-side — runs entirely in the browser, no server or backend needed

Quick Start

cd memristor-sim
npm install
npm run dev

Open http://localhost:5173 in your browser.

Models

HP Labs Ion-Drift

Linear ion-drift model for TiO₂ thin-film memristors [1]. The device is modelled as two resistors in series (doped and undoped regions), with the boundary moving under applied voltage:

$$M(x) = R_{\text{ON}} \cdot x + R_{\text{OFF}} \cdot (1 - x)$$

Supports Joglekar, Biolek, and Anusudha window functions to handle boundary effects.

Yakopcic Generalised

Generalised behavioural model [2] applicable to many oxide-based memristors. Uses a sinh-based I-V relationship with threshold-gated state evolution:

$$I = a_1 x \sinh(bV) \quad (V \geq 0), \qquad I = a_2 x \sinh(bV) \quad (V < 0)$$

Fitted to published data from Oblea [3], Miao [4], and Jo devices [5].

Experiment Presets

Preset	Model	Signal	Description	Source
HP Labs — Sine Wave	HP Labs	1 Hz sine, 1 V	Basic pinched hysteresis on 27 nm TiO₂	Strukov et al. [1]
HP Labs — Triangle Pulse	HP Labs	0.5 Hz triangle, 1 V	DC sweep on 85 nm device	Strukov et al. [1]
Oblea — Sine Wave	Yakopcic	100 Hz sine, 0.45 V	Fast sub-ms switching in Nb-doped SrTiO₃	Oblea et al. [3]
Oblea — Triangle Pulse	Yakopcic	100 Hz triangle, 0.25 V	Asymmetric SET/RESET, low voltage	Oblea et al. [3]
Miao Device	Yakopcic	Triangle, +0.75/−1.25 V	Strongly asymmetric I-V, low thresholds	Miao et al. [4]
Jo Device	Yakopcic	Triangle, +4/−2 V	High-resistance device, sharp switching	Jo et al. [5]

Screenshots

I-V Characteristic (Oblea sine)	Parameter Sliders (Yakopcic)

Architecture

Tech stack: React 19 + TypeScript, Vite, Zustand (state management), Plotly.js (charts), KaTeX (math rendering).

memristor-sim/src/
├── engine/                  # Pure simulation logic (no React)
│   ├── models/              # HP Labs & Yakopcic model implementations
│   ├── signals/             # Sine & triangle input signal generators
│   ├── solver.ts            # Adaptive Dormand-Prince (RK45) ODE solver
│   ├── simulate.ts          # Main simulation loop
│   ├── presets.ts           # 6 experiment configurations from literature
│   └── windows.ts           # Joglekar, Biolek, Anusudha window functions
├── components/
│   ├── controls/            # Model selector, presets, sliders, signal config
│   ├── plots/               # I-V curve and time-series Plotly charts
│   ├── layout/              # Header, sidebar, parameter bar
│   ├── education/           # Memristor intro, model descriptions, tooltips
│   └── ui/                  # Shared UI primitives (Tooltip)
├── content/                 # Educational text & parameter descriptions
├── stores/                  # Zustand simulation state store
├── styles/                  # Global CSS
└── App.tsx                  # Root layout

Legacy Code

The repository also contains the original Python/MATLAB/SPICE implementations from the research project:

• simulate.py — CLI experiment runner with solver selection and video export
• hp_labs_interactive.py / yakopcic_interactive.py — matplotlib-based interactive GUIs
• fit_interactive.py — Tkinter fitting application for matching real device data
• models.py — Python model implementations (HP Labs, Yakopcic, updated Yakopcic [6])
• experiments.py — experiment definitions, functions.py — helper utilities
• MATLAB/ and SPICE/ — alternative implementations

References

[1] Yang, J. J. et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol 3, 429-433 (2008).

[2] Yakopcic, C., Taha, T. M., Subramanyam, G., Pino, R. E. & Rogers, S. A Memristor Device Model. IEEE Electr Device L 32, 1436-1438 (2011).

[3] Oblea, A. S., Timilsina, A., Moore, D. & Campbell, K. A. Silver Chalcogenide Based Memristor Devices. 2010 Int Jt Conf Neural Networks IJCNN 1, 1-3 (2010).

[4] Miao, F. et al. Anatomy of a Nanoscale Conduction Channel Reveals the Mechanism of a High-Performance Memristor. Adv Mater 23, 5633-5640 (2011).

[5] C. Yakopcic, T. M. Taha, G. Subramanyam, and R. E. Pino, "Generalized Memristive Device SPICE Model and its Application in Circuit Design," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 32(8) August, 2013 pp. 1201-1214.

[6] Yakopcic, C. et al. Memristor Model Optimization Based on Parameter Extraction From Device Characterization Data. IEEE T Comput Aid D 39, 1084-1095 (2020).