Principal Investigator
What if there were a way to develop and design complex chips and multi-layered microelectronic devices without the painstaking, iterative and sometimes trial-and-error methods currently in use? Thanks to digital twin technology and some quantum-level simulation technology developed by engineers from the University of Florida, there is.
Supported by a Defense Advanced Research Projects Agency Crystal grant, ECE Assistant Professor Dennis S. Kim, Ph.D., and Chemical Engineering Professor Travis Anderson, Ph.D., are working to use predictive modeling and machine learning to improve current methods in wafer bonding.
Wafer bonding is an advanced manufacturing technique used for stacking and fusing ultra-thin layers of different materials to create high-performance chips tailored for next-generation technologies, including 5G communications, advanced electronics, photonic devices, quantum computers and quantum sensors.
Co-Principal Investigator
The project takes on a critical manufacturing challenge: Even platforms like lithium niobate on insulator (LNOI) lack reliable models to predict optimal bonding conditions, limiting the scalable production of next-generation photonic, sensing and electronic devices.
The team’s multi-scale approach combines quantum-level simulations and machine learning to model how atoms bond at material interfaces, analyzing everything from thermal expansion to defects and phase changes. These atomic insights will be integrated into wafer-scale simulations that predict stress, strain, and thermal effects under realistic manufacturing conditions, effectively modeling wafer bonding down to the level of individual chemical bonds.
“Our goal is a first-principles-informed ‘digital twin’ for chip manufacturing, through linking atomic-level bonding to discover new materials or conditions to expand the gamut of what’s manufacturable,” Kim said.
By combining atomic physics with wafer-scale mechanics and experimental validation, the project aims to create a versatile tool to accelerate development, lower manufacturing costs, and expand the variety of multifunctional materials and devices that can be produced across industries.