Changyu Meng

· Improved chamber thermal reliability and lifetime through multiple hardware redesigns addressing temperature uniformity, contamination, and failure mechanisms · Inventor on 6+ filed semiconductor hardware patents focused on thermal management, contamination reduction, and chamber reliability · Led system-level engineering activities including simulation, hardware validation, root-cause analysis, and customer deployment across multiple ALD chamber development programs · Led cross-functional teams for critical hardware productization — from concept to validation and deployment at customer sites · Applied DFMEA, DOE, Design for Manufacturability (DFM), Failure Analysis (FA), and statistical methods throughout product development

• Implemented a modular lattice particle simulation framework for nonlinear analysis using C, OpenMP and intel MKL (github.com/longfish/LPM-C) • Co-designed the AM fatigue testing experiments, took responsibility in micro-CT scanning and image analyzing • Designed and implemented the crack detection and growth measurement program using the OpenCV python library • Utilized machine learning model (neural network) to predict the crack initiation site with open/closed pore features

· Owned reliability and process stability of rapid thermal annealing equipment through SPC monitoring, DOE, and failure analysis · Participated in cross-functional DOE and FMEA to identify process risks and improve manufacturing robustness · Development of quality control software platform, including the JMP/SQL-based automatic wafer back-surface particles and inline defect dashboard · Received 8 times of formal recognition for outstanding team-working skills in process experiments and defect analysis efforts

• Explored the multiscale simulation techniques to investigate the mechanical behavior of polymers • Used Iterative Boltzmann Inversion (IBI) to study the effect of coarse-graining degree on the dynamic property of polymers • Observed a speedup of over three orders of magnitude when using IBI compared to the all-atomistic (AA) model

- A Multigrid Finite Element Neural Network for Material Response Prediction - Nonlinear Material Behavior Study Using Lattice Particle Method - Imaging‑based Fatigue Mechanism Study of Additively Manufactured Ti64 Alloy - Multi‑fidelity Data Analysis for Additive Manufacturing Metallic Defects

- Multiscale Polymer/metal Interface Study Using Molecular Dynamics (MD) - Thermal Decomposition and Mechanical Properties of Polyethylene - Study on Rheological Properties of Crude Oil/Water Two‑phase Emulsion

- Design of Sensor‑enhanced Small Self‑navigation Vehicle - Design and Test of Detachable Modular Ship - Design of Cold Water Recycling System for Solar Water Heater