Process Models

Understanding and predicting the behavior of materials and processes across scales—from individual atoms to entire wafers—is at the core of our process modeling research. At the nanoscale, material properties are governed by quantum effects, where atomic composition, structure, and local fluctuations define functionality. We use atomistic simulations, including quantum mechanical methods, to model these phenomena and explore how nanostructures and devices perform under realistic fabrication conditions. These detailed simulations are complemented by classical and continuum-scale physical-chemical models that describe established manufacturing processes such as atomic layer deposition (ALD), chemical vapor deposition (CVD), physical vapor deposition (PVD), plasma etching, and wet processing. By linking atomistic insights with macroscopic process understanding, we build comprehensive models that bridge the gap between fundamental material behavior and process-level outcomes.

Our mission extends beyond physical modeling toward the development of resilient, predictive process models that are directly applicable in semiconductor manufacturing. These models are designed to operate in real-time, enabling process control and optimization without requiring direct physical measurements. Recognizing the limitations of purely physics-based or AI-only methods, we pursue hybrid approaches that combine simulation data, empirical research, expert knowledge, and production feedback. Digital twins created in this way are capable of adapting to real-world complexities, accounting for process drift and tool variability. Additionally, we implement fast surrogate models to accelerate high-fidelity simulations, and apply advanced computer vision algorithms for automated metrology and inline process monitoring. Together, these approaches form a scalable, flexible modeling framework that supports innovation in both materials development and industrial process integration.

Research Topics

• Multiscale and Multiphysics Simulation
  Modeling of complex processes across multiple scales—from atomic-level interactions to device-scale up to the wafer level. This includes detailed process and structure simulations for micro- and nano-fabrication technologies such as lithography, etching, and deposition.
• Hybrid and Data-Driven Modeling
  Development of models that integrate physical principles with data science, enhancing the prediction of process behaviors like plasma etching and CMP through AI-assisted approaches.
• Surrogate and Reduced-Order Modeling
  Use of efficient computational approximations to replace time-consuming full-physics simulations, enabling faster decision-making and real-time applications.
• Advanced Materials and Structure Analysis
  Simulation of novel materials, MEMS/NEMS structures, and device behavior using tools such as FEM, Monte Carlo methods, molecular dynamics and quantum transport models.
• Digital Twins and Process Optimization
  Construction of virtual replicas of real processes that are continuously updated with experimental and production data to optimize tool behavior, reduce variability, and improve yield.
• Machine Vision and Intelligent Metrology
  Application of computer vision techniques for inline process control and automated evaluation of results in semiconductor production.