Photopolymerization Curing Simulation Suite

Overview

This repository contains a comprehensive suite of tools for simulating and analyzing photopolymerization curing processes, specifically designed for Riston LDI7000 negative photoresist systems. The project integrates experimental kinetic data with advanced finite element modeling to provide accurate predictions of curing behavior under various processing conditions.

Project Background

Based on the research framework established in "Integrated Photo-DSC and Time-Resolved FTIR Analysis of Photopolymerization Dynamics in Riston LDI7000 Photoresist", this simulation suite implements the Prout-Tompkins autocatalytic kinetic model to capture the complex multi-phase polymerization mechanisms observed in UV-cured photoresist systems.

Key Features

• Multi-Modal Kinetic Modeling: Implementation of Prout-Tompkins autocatalytic framework with superior correlation (R² = 0.91) compared to conventional Avrami-Erofeev approaches
• Simplified Abaqus Integration: Streamlined UMAT subroutines reducing code complexity from 610 to 214-274 lines
• Morphology Prediction: 50% cure threshold analysis for solid-state formation prediction
• Alternative Analysis Tools: Python and MATLAB implementations for rapid prototyping and validation
• Process Optimization: Light intensity threshold analysis and exposure time optimization

Repository Structure

├── abaqus/                     # Abaqus simulation files
│   ├── simplified/            # Simplified UMAT implementation
│   ├── enhanced/              # Enhanced UMAT with morphology prediction
│   └── original/              # Original complex implementation (reference)
├── python/                    # Python analysis tools
├── matlab/                    # MATLAB/Octave scripts
├── docs/                      # Documentation and research papers
├── data/                      # Experimental data and validation datasets
├── examples/                  # Example simulations and tutorials
└── tests/                     # Validation and test cases

Quick Start

Prerequisites

• Abaqus 2019 or later
• Python 3.7+ with numpy, scipy, matplotlib
• MATLAB R2018b+ or GNU Octave 5.0+
• Git for version control

Installation

git clone https://github.com/your-username/photopolymerization-curing-suite.git
cd photopolymerization-curing-suite

Running Simulations

Simplified Abaqus Simulation

cd abaqus/simplified
call abq2019-sub job=simplified_job user=simplified_curing.for cpus=2

Enhanced Morphology Prediction

cd abaqus/enhanced
call abq2019-sub job=enhanced_job user=enhanced_curing.for cpus=2

Python Analysis

cd python
python alternative_curing_analysis.py

MATLAB Analysis

cd matlab
simple_curing_matlab()

Kinetic Model

The core kinetic model implements the Prout-Tompkins autocatalytic framework:

dα/dt = A exp(-Ea/(RT)) I(t,x)^β (α/α_max)^m (1 - α/α_max)^n

Where:

• α: degree of curing
• I(t,x): spatial-temporal light intensity distribution
• β, m, n: empirical exponents characterizing reaction mechanisms
• Ea = 20374.3 J/mol: activation energy
• A = 414.553: pre-exponential factor

Calibrated Parameters

Based on photo-DSC and FTIR experimental data:

• Kinetic exponents: m = 2.46, n = 0.499
• Light intensity exponent: β = 0.370
• Maximum heat flow: 1.4 mW/mg
• Optimal light intensity: 266 mW/cm²
• Optimal exposure time: 4 minutes

Simulation Approaches

1. Simplified UMAT (Recommended)

• File: abaqus/simplified/simplified_curing.for
• Features: Streamlined implementation, direct light intensity calculation
• Use case: Standard curing analysis with reduced computational overhead

2. Enhanced UMAT with Morphology Prediction

• File: abaqus/enhanced/enhanced_curing.for
• Features: 50% cure threshold morphology prediction, multi-phase tracking
• Use case: Advanced analysis requiring morphology state information

3. Python Analysis Tool

• File: python/alternative_curing_analysis.py
• Features: 3D visualization, rapid prototyping, parameter studies
• Use case: Quick validation and parameter optimization

4. MATLAB/Octave Script

• File: matlab/simple_curing_matlab.m
• Features: ODE solver integration, automated plotting
• Use case: Academic research and educational applications

Validation Data

The simulation suite has been validated against experimental data including:

• Photo-DSC measurements (25°C, 33-665 mW/cm² intensity range)
• Time-resolved FTIR spectroscopy (2000-3010 cm⁻¹ spectral region)
• Chromatic progression analysis (pale blue to deep purple)
• Principal component analysis (83.3% variance explained)

Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

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

Citation

If you use this simulation suite in your research, please cite:

@article{photopolymerization2024,
  title={Characterization and modeling of a typical curing material for photoresist films},
  author={[Chongnan Peng]},
  journal={[EUROSIME]},
  year={2022},
  doi={[10.1109/EuroSimE54907.2022.9758849]}
}

Contact

For questions and support, please contact [your-email@institution.edu]

Acknowledgments

• Research framework based on Peng et al. (2022) kinetic modeling approach
• Experimental validation through integrated photo-DSC and FTIR analysis
• Finite element analysis validation for industrial lithographic applications