EKV-fit

Heuristic Enz-Krummenacher-Vittoz (EKV) Model Fitting for Low-Power IC Design

This repository contains the Python implementation of a heuristic iterative sub-ranging technique for fitting the Enz-Krummenacker-Vittoz (EKV) model to MOSFET data. The tool is designed for low-power integrated circuit (IC) design, with a focus on accurate parameter extraction in weak and moderate inversion regions.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Contributing
• License
• Citation

Overview

The EKV model is widely used for MOSFET modeling in low-power IC design due to its accuracy and simplicity. This project provides a Python-based tool for extracting key EKV model parameters, including:

• Threshold voltage ((V_{TH}))
• Specific current ((I_S))
• Subthreshold slope parameter ((n))

The tool uses an iterative sub-ranging technique to refine the threshold voltage and ensure accurate fitting across different regions of operation (weak, moderate, and strong inversion).

Features

• Iterative Sub-Ranging: Progressively narrows the voltage range to focus on the region of interest.
• Robustness Against Noise: Handles measurement noise and uncertainties effectively.
• Flexible Fitting: Allows control over parameters such as fit_range_parameter, max_iter, and error_margin.
• Visualization: Generates plots to visualize the measured data, fitted curve, and fit quality region.
• Open-Source: Built using Python and open-source libraries (NumPy, SciPy, Pandas, Matplotlib).

Installation

1. Clone the Repository:

   git clone https://github.com/yourusername/ekv-fitting.git
   cd ekv-fitting

2. Install Dependencies: Ensure you have Python 3.7 or later installed. Then, install the required libraries:

   pip install numpy scipy pandas matplotlib

3. Run the Script: Use the provided Python script to fit EKV model parameters to your data.

Usage

The core functionality is encapsulated in the fit_data function. Here’s an example of how to use it:

import mos_extract_ekv as ekv

# Define the input file and parameters
filename = 'data.csv'  # Path to your CSV file

# Perform the fitting
fitter = ekv.Fitter(filename='data.csv', 
                    temperature=27,  # Temperature in Celsius
                    fit_range_parameter=1.2
                    # key parameter for controlling the range of fitting
                   )

# Print the (main) results
print(f"VTH: {fitter.VTH_cf:.3f} V")
print(f"IS: {fitter.IS_cf:.3e} A")
print(f"n: {fitter.n_cf:.3f}")

Input Data Format

The input CSV file should contain voltage and current data in the following format:

Voltage1, Current1, Voltage2, Current2, ...
V1, I1, V2, I2, ...

Contributing

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Submit a pull request with a detailed description of your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Citation

If you use this tool in your research, please cite the following paper:

Dei, M. Heuristic Enz–Krummenacher–Vittoz (EKV) Model Fitting for Low-Power Integrated Circuit Design: An Open-Source Implementation. Electronics 2025, 14, 1162. https://doi.org/10.3390/electronics14061162

For the SciPy library used in this project, please cite:

Virtanen, P.; et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 2020, 17, 261–272. https://doi.org/10.1038/s41592-019-0686-2

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Contact

For questions or feedback, please contact:

• Michele Dei
  Email: michele.dei [at] unipi.it
  GitHub: [https://github.com/michele-dei]