vgsot-sim 0.1.0

VGSOT/SOT MTJ switching simulation (electronic + anisotropy + LLG step solver)

vgsot-sim

A physics-based simulation toolkit for VGSOT-MTJ (Voltage-Gated Spin-Orbit Torque Magnetic Tunnel Junction) and SOT-MTJ switching dynamics.

The simulator couples several physical effects into a time-domain switching model:

• Electronic transport (V_MTJ, I_SOT)
• Voltage-controlled magnetic anisotropy (VCMA)
• Thermal fluctuation field
• Analytical one-step LLG magnetization update
• TMR resistance feedback

This enables simulation of magnetization switching under realistic electrical excitation waveforms.

The package can be used either:

• as a command-line simulator
• as a Python simulation library

---

Installation

Clone the repository and install in editable mode:

pip install -e .

Requirements (automatically installed):

• numpy
• matplotlib
• tqdm

---

Information flow

flowchart LR
    classDef io fill:#f5f3ff,stroke:#7c3aed,stroke-width:1.2px,color:#111;
    classDef case fill:#f3e8ff,stroke:#9333ea,stroke-width:1.2px,color:#111;
    classDef kernel fill:#ede9fe,stroke:#7c3aed,stroke-width:1.2px,color:#111;
    classDef out fill:#faf5ff,stroke:#a855f7,stroke-width:1.2px,color:#111;

    CLI["CLI / Config"]:::io
    CASE["Selected case<br/>(time-series / SER)"]:::case

    INIT["initialize.py<br/>initial state"]:::kernel
    ELEC["electronic.py<br/>current / voltage mapping"]:::kernel
    DYN["dynamic_switching.py<br/>magnetization update"]:::kernel
    ANI["anisotropy.py<br/>effective field"]:::kernel
    STO["stochastic.py<br/>thermal noise"]:::kernel
    TMR["tmr.py<br/>resistance feedback"]:::kernel

    RES["SimResult / SweepResult /<br/>SerResult"]:::out
    SAVE["result_io.py<br/>CSV / plot export"]:::out

    CLI --> CASE
    CASE --> INIT
    CASE --> ELEC
    CASE --> DYN
    DYN --> ANI
    ANI --> STO
    DYN --> TMR

    INIT --> CASE
    ELEC --> CASE
    DYN --> CASE
    TMR --> CASE

    CASE --> RES
    RES --> SAVE

This structure separates physics kernels, experiment orchestration, and output utilities, making the project usable both as a CLI simulator and as a reusable Python library.

For more details, please refer to: Project structure

---

Running Simulation Cases

All experiments are exposed through a unified CLI:

vgsot-sim <case_name>

By default, outputs are written to:

• ./result/*.png (figures)
• ./result/*.csv (time series / sweep results)

You can change output directory via:

vgsot-sim <case_name> --out_dir my_results

Disable progress bars:

vgsot-sim <case_name> --no_progress

Available cases

You can run all cases below (names match src/vgsot_sim/cli.py):

# 1) Three-terminal voltage control (V1,V2,V3 -> I_SOT,V_MTJ)
vgsot-sim terminal_voltage_control

# 2) Baseline: SOT-only, constant current pulse (V_MTJ=0)
vgsot-sim sot_only_constant_current

# 3) No-VCMA SOT switching: sweep I_SOT and overlay mz(t)
vgsot-sim sot_switching_no_vcma

# 4) SER vs I_SOT: no-VCMA + thermal noise (Monte-Carlo)
vgsot-sim ser_sot_no_vcma_thermal

# 5) VCMA-assisted: fix V_MTJ, sweep I_SOT and overlay mz(t)
vgsot-sim vcma_assisted_switching_isot_sweep

# 6) VCMA-assisted: fix I_SOT, sweep V_MTJ and overlay mz(t)
vgsot-sim vcma_assisted_switching_vmtj_sweep

# 7) optimized two-pulse scheme: sweep (t1,t2) and overlay mz(t)
vgsot-sim optimized_vgsot_switching

# 8) SER vs t1 for optimized scheme: thermal noise (Monte-Carlo)
vgsot-sim ser_optimized_vgsot

For more information, please refer to: Simulation Cases and Their Physical Meaning

Default parameters are listed in: Default parameters by case

---

Using as a Python library (recommended)

Besides running from command line, you can import and run each case directly in your own Python scripts, and override parameters as needed. The recommended workflow is:

1. Create a configuration dataclass
2. Run a simulation case
3. Optionally save results using result_io

---

1. Basic Python API usage

Quick start (minimal example)

from vgsot_sim import sot_only_constant_current

res = sot_only_constant_current()
print(res.mz[-1])

Example: run a VCMA-assisted switching simulation.

from vgsot_sim import (
    vcma_assisted_switching_isot_sweep,
    VcmaAssistedSwitchingIsotSweepConfig,
)

cfg = VcmaAssistedSwitchingIsotSweepConfig(
    v_mtj=1.1,
    i_sot_list=[-40e-6, -30e-6, -20e-6],
)

result = vcma_assisted_switching_isot_sweep(cfg)

print(result.time_s.shape)

print(result.mz_curves.keys())
print(result.r_mtj_curves.keys())
print(result.pulse_curves.keys())

Returned object:

field	type	description
time_s	np.ndarray	simulation time axis
mz_curves	dict[str, np.ndarray]	magnetization trajectories
r_mtj_curves	dict[str, np.ndarray]	MTJ resistance vs time
pulse_curves	dict[str, np.ndarray]	applied pulse waveform
switch_energy_j	dict[str, float]	switching energy
pulse_ylabel	str	label for pulse plot

Each entry in curves corresponds to one sweep parameter.

---

2. plotting results (optional)

import matplotlib.pyplot as plt

for label, mz in result.mz_curves.items():
    plt.plot(result.time_s, mz, label=label)

plt.xlabel("time (s)")
plt.ylabel("mz")
plt.legend()
plt.show()

---

3. Running Monte-Carlo SER simulations

Example:

from vgsot_sim import (
    ser_sot_no_vcma_thermal,
    SerSotNoVcmaThermalConfig,
)

cfg = SerSotNoVcmaThermalConfig(
    trials=500,
    i_sot_list=[-100e-6, -95e-6, -90e-6],
)

res = ser_sot_no_vcma_thermal(cfg)

print(res.x)      # I_SOT values
print(res.ser)    # switching error rate

Returned object:

SerResult
 ├── x   : ndarray
 ├── ser : ndarray
 └── x_label : str

---

4. Low-level simulation kernels (advanced users)

If you want full control over excitation waveforms, you can call the internal kernels directly:

run_piecewise_terminal_voltage(...)
run_piecewise_direct_excitation(...)
run_two_pulse_proposed(...)

Example:

from vgsot_sim import run_piecewise_direct_excitation

res = run_piecewise_direct_excitation(
    sim_start_step=1,
    sim_mid1_step=2000,
    sim_mid2_step=3500,
    sim_end_step=5000,

    pap=1,

    v_mtj_stage1=0.0,
    v_mtj_stage2=0.0,
    v_mtj_stage3=0.0,

    i_sot_stage1=-90e-6,
    i_sot_stage2=0.0,
    i_sot_stage3=0.0,

    estt_stage1=0,
    esot_stage1=1,

    estt_stage2=0,
    esot_stage2=1,

    estt_stage3=0,
    esot_stage3=1,

    vnv=1,
    non=1,

    r_sot_fl_dl=0.83,
)

print(res.time_s)
print(res.mz)

These kernels return a SimResult dataclass containing the full time evolution of the system.

For full API documentation, please refer to: API document

For more detailed API usage, please refer to: CASES GUIDE

---

Notes

• I_SOT units: Ampere
• V_MTJ units: Volt
• time units: seconds

The simulation time step is defined in:

vgsot_sim/constants.py

via:

constants.t_step

For reproducible Monte-Carlo runs:

import numpy as np
np.random.seed(0)

---

Citation

If you use this simulator in academic research, please cite:

Zhang Jincheng. (2026). VGSOT-SIM: A VGSOT switching simulation toolkit [Computer software]. GitHub.

For more technical details, please refer to: Technical Details