OECT Transfer Curve Analysis

简体中文

A Python package for analyzing Organic Electrochemical Transistor (OECT) transfer characteristic curves. This library provides comprehensive tools for extracting key device parameters including transconductance, threshold voltage, and device performance metrics.

🚀 Features

• Transconductance Analysis: Calculate transconductance (gm) using robust numerical differentiation
• Threshold Voltage Extraction: Automatic Von calculation using logarithmic slope method
• Device Type Support: Support for both N-type and P-type devices
• Robust Data Processing: Built-in validation and error handling for noisy data
• Forward/Reverse Analysis: Separate analysis of forward and reverse sweep directions
• Performance Metrics: Extract maximum/minimum current points and related parameters

📦 Installation

Prerequisites

• Python 3.7 or higher
• NumPy
• Standard library dependencies

Install from source

git clone https://github.com/Durian-leader/oect_transfer/oect-transfer.git
cd oect-transfer
pip install -e .

Dependencies

pip install numpy

🔧 Quick Start

Basic Usage

import numpy as np
from oect_transfer import Transfer

# Example data: Gate voltage (Vg) and drain current (Id)
vg = np.linspace(-0.5, 0.5, 100)  # Gate voltage in V
id = np.exp(vg * 10) * 1e-6       # Drain current in A (example)

# Create Transfer object
transfer = Transfer(vg, id, device_type="N")

# Access computed parameters
print(f"Maximum transconductance: {transfer.gm_max.raw:.2e} S")
print(f"Threshold voltage: {transfer.Von.raw:.3f} V")
print(f"Maximum current: {transfer.I_max.raw:.2e} A")

Advanced Analysis

# Analyze forward and reverse sweeps separately
print(f"Forward gm_max: {transfer.gm_max.forward:.2e} S")
print(f"Reverse gm_max: {transfer.gm_max.reverse:.2e} S")

# Check where maximum transconductance occurs
print(f"gm_max location: {transfer.gm_max.where}")  # 'forward', 'reverse', or 'turning_point'

# Access raw data sequences
print(f"Gate voltage range: {transfer.Vg.raw.min():.2f} to {transfer.Vg.raw.max():.2f} V")
print(f"Current range: {transfer.I.raw.min():.2e} to {transfer.I.raw.max():.2e} A")

P-type Device Analysis

# For P-type devices, specify device_type="P"
transfer_p = Transfer(vg, id, device_type="P")
print(f"P-type Von: {transfer_p.Von.raw:.3f} V")

📚 API Reference

Classes

Transfer

Main class for transfer curve analysis.

Constructor:

Transfer(x, y, device_type="N")

Parameters:

• x (array-like): Gate voltage data (Vg)
• y (array-like): Drain current data (Id)
• device_type (str): Device type, "N" for N-type, "P" for P-type

Attributes:

• Vg (Sequence): Gate voltage data (raw, forward, reverse)
• I (Sequence): Drain current data (raw, forward, reverse)
• gm (Sequence): Transconductance data
• gm_max (Point): Maximum transconductance point
• I_max (Point): Maximum current point
• I_min (Point): Minimum current point
• Von (Point): Threshold voltage point

Sequence

Data container for raw, forward, and reverse sweep data.

Attributes:

• raw (NDArray): Complete dataset
• forward (NDArray): Forward sweep (up to maximum Vg)
• reverse (NDArray): Reverse sweep (from maximum Vg)

Point

Container for parameter values at specific points.

Attributes:

• raw (float): Value from complete dataset
• where (str): Location ("forward", "reverse", or "turning_point")
• forward (float): Value from forward sweep
• reverse (float): Value from reverse sweep

Methods

safe_diff(f, x)

Static method for robust numerical differentiation.

Parameters:

• f (array): Function values
• x (array): Independent variable values

Returns:

• NDArray: Computed derivatives

🧪 Examples

Example 1: Analyzing Experimental Data

import numpy as np
import matplotlib.pyplot as plt
from oect_transfer import Transfer

# Load your experimental data
vg_data = np.loadtxt('gate_voltage.txt')
id_data = np.loadtxt('drain_current.txt')

# Create transfer object
transfer = Transfer(vg_data, id_data, device_type="N")

# Plot results
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Transfer curve
ax1.semilogy(transfer.Vg.raw, np.abs(transfer.I.raw))
ax1.axvline(transfer.Von.raw, color='red', linestyle='--', 
           label=f'Von = {transfer.Von.raw:.3f} V')
ax1.set_xlabel('Gate Voltage (V)')
ax1.set_ylabel('|Drain Current| (A)')
ax1.legend()
ax1.grid(True)

# Transconductance
ax2.plot(transfer.Vg.raw[:-1], transfer.gm.raw)
ax2.axhline(transfer.gm_max.raw, color='red', linestyle='--',
           label=f'gm_max = {transfer.gm_max.raw:.2e} S')
ax2.set_xlabel('Gate Voltage (V)')
ax2.set_ylabel('Transconductance (S)')
ax2.legend()
ax2.grid(True)

plt.tight_layout()
plt.show()

Example 2: Comparing Forward and Reverse Sweeps

# Analyze hysteresis
forward_von = transfer.Von.forward
reverse_von = transfer.Von.reverse
hysteresis = abs(forward_von - reverse_von)

print(f"Forward Von: {forward_von:.3f} V")
print(f"Reverse Von: {reverse_von:.3f} V")
print(f"Hysteresis: {hysteresis:.3f} V")

# Plot forward vs reverse
plt.figure(figsize=(8, 6))
plt.semilogy(transfer.Vg.forward, np.abs(transfer.I.forward), 
             'b-', label='Forward')
plt.semilogy(transfer.Vg.reverse, np.abs(transfer.I.reverse), 
             'r--', label='Reverse')
plt.axvline(forward_von, color='blue', alpha=0.7, linestyle=':')
plt.axvline(reverse_von, color='red', alpha=0.7, linestyle=':')
plt.xlabel('Gate Voltage (V)')
plt.ylabel('|Drain Current| (A)')
plt.legend()
plt.grid(True)
plt.title('Transfer Curve: Forward vs Reverse')
plt.show()

⚠️ Important Notes

Data Requirements

• Input arrays must be 1D and of equal length
• No NaN or infinite values are allowed
• Minimum 2 data points required for meaningful analysis
• Data should span a reasonable voltage range including the device turn-on region

Device Type Selection

• N-type devices: Use device_type="N" (default)

  • Von calculated using maximum logarithmic slope
  • Suitable for enhancement-mode n-channel devices

• P-type devices: Use device_type="P"

  • Von calculated using minimum logarithmic slope
  • Suitable for enhancement-mode p-channel devices

Transconductance Calculation

The transconductance is calculated using a robust numerical differentiation method that:

• Uses forward, backward, and central difference schemes appropriately
• Handles turning points with averaged derivatives
• Includes safeguards against division by zero

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

git clone https://github.com/Durian-leader/oect_transfer.git
cd oect-transfer
pip install -e .[dev]

Running Tests

pytest tests/

📄 License

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

👥 Authors

• lidonghao - Initial work - lidonghao100@outlook.com

🙏 Acknowledgments

• Thanks to the OECT research community for valuable feedback
• Inspired by standard practices in organic electronics characterization

📞 Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed description
3. Contact the maintainer at lidonghao100@outlook.com

📈 Roadmap

• Add support for output characteristic analysis
• Implement mobility extraction methods
• Add data export functionality
• Develop GUI interface
• Add more device parameter extraction methods

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Keywords: OECT, Organic Electrochemical Transistor, Transfer Curve, Transconductance, Threshold Voltage, Device Characterization, Python, Analysis