High-throughput computational platform for photosensitive insulating materials

Overview

The high-throughput computational platform developed by the University of New England is designed to revolutionize the design of photosensitive insulating materials for the semiconductor industry. This innovative platform leverages GPU-based computations and machine learning to automate the generation of molecular structures and simulate their properties under various conditions. The core value lies in its ability to predict experimental outcomes with high statistical correlation, significantly accelerating the material design and development process for finer i-line patterning and enhanced material reliability.

Technical specifications

Key features:

• Automated molecular generation: Explores a vast chemical space, allowing for the discovery of novel resins and photosensitizers.
• Machine Learning-Molecular Dynamics (ML-MD): Rapidly predicts critical bulk properties such as glass transition temperature (Tg), coefficient of thermal expansion (CTE), and modulus, essential for material reliability.
• Quantum Mechanical (QM) calculations: Provides insights into i-line reaction mechanisms and optimizes photosensitivity for semiconductor applications.
• Comprehensive database: Offers a detailed dataset of predicted material properties, facilitating future material discovery and validation.

Technology readiness level

The technology is currently at TRL 3, indicating that it has been validated in a laboratory setting. The platform is undergoing further refinement and optimization, with plans for detailed quantum mechanical analysis and iterative optimization of lead candidates to ensure balanced performance in terms of photosensitivity, reliability, and processability.