Samsung Display Lab is looking to hire an AI expert to fuse physics-based simulation with modern AI/ML for circuit/pixel layout design and circuit simulation, aiming to drive AI-assisted panel layout, architect pixel/backplane circuits, and build high-fidelity surrogates for parasitics using AI.
Requirements
- Proficiency with SPICE/Spectre/HSPICE/Cadence Virtuoso/ADE; layout and parasitic extraction.
- Strong Python and ML fundamentals (PyTorch or TensorFlow, scikit-learn/NumPy/Pandas)
- Experience building and validating surrogate models and running multi-objective optimization.
- Experience with GNNs for layout-to-electrical, PINNs/physics-informed ML, or differentiable simulation.
- RL for control/param tuning; Bayesian optimization at scale; DoE/active learning.
- Solid analog/digital circuit fundamentals; hands-on OLED pixel/gate driver design.
- Demonstrated ability to take designs from simulation to hardware bring-up and correlation.
Responsibilities
- Develop physics-informed ML and GNN surrogates mapping layout/process to electrical KPIs.
- Run multi-objective optimization via Bayesian optimization/evolutionary search; generate candidate pixel, driver topologies.
- Apply RL for process control and compensation strategy tuning.
- Compose fast design loops that combine Layout/SPICE with learned surrogates; accelerate what-if sweeps.
- Build data/feature pipelines from fab/test and panel bring-up; implement active learning and uncertainty quantification.
Other
- BS Computer Science or equivalent with a minimum of 10+ years or a MS in Computer Science with 8+ years or PhD 5+ years in Electrical Engineering/Computer Engineering/Applied Physics (or equivalent experience).
- Publications/patents in display, EDA, or ML-for-hardware.
- You’re inclusive, adapting your style to the situation and diverse global norms of our people.
- An avid learner, you approach challenges with curiosity and resilience, seeking data to help build understanding.
- You’re collaborative, building relationships, humbly offering support and openly welcoming approaches.
