Leveraging Machine Learning and AI to enhance Back End of Line (BEOL) thermal properties and mitigation thermal hotspots

The persistent pursuit of miniaturization and performance enhancement in semiconductor devices has led to significant challenges in thermal management, particularly in the Back End of Line (BEOL). This is a critical structure in semiconductor devices responsible for deliver power and interconnect the various components of an integrated circuit. However, as the device dimensions shrink and power densities increase, thermal hotspots become a critical issue, potentially leading to performance degradation, reliability concerns, and device failure. Traditional approaches to thermal management in BEOL processes often fall short in addressing the complexities of modern semiconductor devices. This PhD proposal aims to develop a novel approach using machine learning (ML) and artificial intelligence (AI) to design and optimize BEOL structures to efficiently mitigate thermal hotspots and enhance its effective thermal conductivity to improve uniform heat conduction. By integrating advanced computational techniques with experimental validation, this research seeks to provide a robust solution to thermal management issues in modern semiconductor manufacturing. The proposed approach will involve the development of predictive ML models to identify thermal hotspots, the use of AI algorithms to optimize BEOL materials and structures for improved thermal conductivity, and the experimental validation of these optimized designs. This research will address the trade-off between power and temperature in new semiconductor technologies by applying advanced computational techniques combined with practical applications in semiconductor manufacturing with the aim to achieve high performance without compromising thermal stability.

1. Develop ML Models: Create predictive models to identify and mitigate thermal hotspots in BEOL structures.

Collection: Gather data on thermal properties of various BEOL materials and structures from existing literature and experimental results.

Feature Selection: Identify key features influencing thermal conductivity and hotspot formation.

Algorithm Selection: Choose appropriate ML algorithms (e.g., neural networks, support vector machines) for predictive modeling.

Training and Validation: Train the models using collected data and validate their accuracy using cross-validation techniques.

2. Optimize Thermal Conductivity: Use AI algorithms to design BEOL materials and structures with enhanced thermal conductivity.

AI Algorithms: Implement AI algorithms (e.g., genetic algorithms, reinforcement learning) to optimize BEOL designs for thermal management.

Simulation: Use finite element analysis (FEA) to simulate thermal performance of optimized designs.

3. Experimental Validation: Fabricate and test the optimized BEOL structures to validate the computational models.

Design: Design an optimized BEOL structures using standard semiconductor fabrication techniques.

Testing: Propose and perform thermal measurement that accurately extract BEOL thermal conductivity. Propose method to experimentally measure the impact of hotspot.