Taiwan
Computational lithography and OPC modeling researcher (PhD, NTHU) specializing in EUV patterning and Mask 3D (M3D) effects for sub-3nm nodes. My work focuses on developing high-performance electromagnetic simulation methods for lithography applications. I have developed a modified Stratton–Chu Maxwell solver achieving ~4× speed improvement over conventional FDTD, enabling efficient modeling of scanner-relevant effects. In addition, I have worked on silicon photonic device design and built end-to-end simulation workflows from layout to circuit-level validation. I also applied machine learning methods in supply chain demand forecasting within a semiconductor industry context. I have experience in DOE-driven simulation, silicon-to-simulation correlation, and nanoscale device optimization with strong manufacturing relevance. My work emphasizes pattern fidelity, process variation analysis, and fabrication-aware modeling. I am interested in opportunities in computational lithography, OPC modeling, and Silicon Photonics R&D.
• Developed a modified Stratton–Chu electromagnetic solver, achieving a ~4× speed improvement for efficient simulation of M3D effects. • Executed large-scale DOE-driven parametric sweeps using MATLAB and Python to optimize nanoscale patterning performance. • Established silicon-to-simulation correlation, improving prediction accuracy for asymmetric nanostructures through EM model validation. • Currently applying supervised machine learning and agentic AI-assisted workflows to accelerate optical model calibration and parametric optimization, reducing manual iteration time in simulation-to-experiment convergence loops.
• Co-developed nanoscale plasmonic logic circuits (XNAND, NOR, XNOR) utilizing FDTD/FDE simulations and MATLAB for nonlinear signal processing. • Optimized waveguide and device architectures, achieving a 67% footprint reduction and 50% power savings. • Engineered phase-matching conditions to enhance signal propagation efficiency in nanoscale photonic circuits.
Graduate Teaching Assistant: Signals and Systems • Dedicated four years to instructing and mentoring cohorts of approximately 100 students per semester in the Signals and Systems course, specializing in translating complex mathematical theories into practical engineering skills. • Led interactive in-class teaching sessions focused on advanced problem-solving techniques and real-world applications for a large student body. • Designed comprehensive signal processing exercises using MATLAB to bridge the gap between theoretical concepts and practical computational skills. • Authored original quiz and exam question papers along with detailed answer keys to effectively assess student comprehension at scale.
I took a dedicated leave of absence following a stage 4 cancer diagnosis in August 2020. My medical treatment continued through April 2021, and I focused entirely on my physical recovery until October 2021. Overcoming this profound health challenge required immense resilience, adaptability, and unwavering focus. Having successfully rebuilt my health, I returned to my PhD research in computational photonics with renewed energy and a steadfast commitment to advancing my work in the industry.
• Applied mathematical modeling and numerical analysis to study gravitational waves as a cosmological probe for light-mass primordial black holes • Developed physics-based simulation approaches for complex systems, strengthening expertise in computational modeling and data analysis