(197i) Atomic Layer Deposition Simulation Using Stochastic Parallel Particle Kinetic Simulator | AIChE

(197i) Atomic Layer Deposition Simulation Using Stochastic Parallel Particle Kinetic Simulator

Authors 

Na, J., Carnegie Mellon University
Lee, W. B., Seoul National University
The semiconductor industry has experienced significant growth and change in recent years due to the increasing demand for smaller and more powerful devices. To meet this demand, an atomic layer deposition (ALD) process has been introduced to create thinner and more precise layers. ALD processes offer high controllability, precise thickness control, and uniform thin film formation over a large area. To better understand and enhance these processes, computational modeling and simulation methodologies are being employed. This study focuses on the simulation of the diffusion and deposition process on the wafer surface using a Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) and the kinetic Monte Carlo technique. Specifically, this study examines the chemical reactions of the chemical species present on the surface and sequentially pulsing and purging the surface with Dichlorosilane and ammonia gas. Adsorption, desorption, and deterioration occur probabilistically, which confirms the expected growth of the atomic layer. The growth of the atomic layer is compared with existing literature. The ultimate objective of this study is to develop a simulation model for a specific chemical species and predict the quality and thickness of the entire wafer under varying conditions, such as different pressures and temperatures. By providing insights into the ALD process and improving the ability to predict the properties of thin films, this study could play a crucial role in the development of smaller and more powerful devices in the semiconductor industry. In conclusion, this study demonstrates the power of computational modeling and simulation in advancing the ALD process. The results provide a foundation for developing a comprehensive simulation model to predict the quality and thickness of thin films on wafers under different conditions. These insights could ultimately enable the development of more precise, reliable, and efficient thin film deposition techniques, leading to the production of smaller and more powerful devices.