Physics-inspired ML framework for Compact Model Calibration

Device compact models (CMs) are critical for linking semiconductor device physics with circuit-level design, providing simplified yet accurate representations of complex device behaviour for efficient simulation. As technology scales into nanometer dimensions and introduces new materials and architectures, these models must capture non-ideal effects such as short-channel phenomena, variability, and temperature dependence. This capability enables designers to predict performance, power, and reliability early in the design cycle, reducing development time and cost. However, calibrating CMs at advanced nodes is challenging due to intricate device physics like quantum confinement, strong parasitic interactions, and parameter interdependencies. Variability from process imperfections and the complexity of emerging architectures such as FinFETs and Gate-All-Around FETs further complicate calibration, requiring sophisticated techniques and large datasets while balancing accuracy and computational efficiency.

Recent advances in machine learning (ML) have introduced a promising alternative path for CM calibration. ML-based methods automate parameter extraction by learning complex, non-linear relationships between measurements and model parameters, reducing manual effort and accelerating calibration. Techniques such as neural networks, Bayesian optimization, and surrogate modelling handle high-dimensional parameter spaces and improve robustness against noise and variability, while transfer learning aids adaptation to new devices. Despite these advantages, ML approaches face challenges including the need for large, high-quality datasets, risk of overfitting, and integration with SPICE and EDA workflows to maintain physical consistency. Interpretability also remains a concern, as black-box models can obscure the physical meaning of parameters, making validation and trust critical for industrial adoption.

Internship/Master thesis Scope

This internship will focus on developing physics-inspired ML methods for calibrating CMs. Unlike purely data-driven approaches, physics-inspired ML integrates fundamental device principles-such as charge transport, electrostatics, and temperature dependence-into the ML architecture or loss function. This hybrid approach combines the predictive power of ML with physical interpretability, reducing overfitting and improving generalization to unseen bias or temperature conditions. By leveraging prior knowledge, these methods can work effectively with smaller datasets, making them particularly suitable for emerging technologies where measurement data is limited. Key challenges include designing architectures that balance flexibility with physical constraints and ensuring seamless integration into SPICE-compatible workflows for circuit simulation. The ultimate goal of the internship is to create a physics-informed ML framework for CM calibration that complements existing in-house calibration tools.

Type of internship: Master internship, PhD internship

Duration: 8-10 months

Required educational background: Electrotechnics/Electrical Engineering

Supervising scientist(s): For further information or for application, please contact Arvind Sharma ([email protected]) and Alexander Makarov ([email protected]) and Aishwarya Singh ([email protected]) and Maarten Van de Put ([email protected]) and Fernando Garcia Redondo ([email protected])

The reference code for this position is 2026-INT-002. Mention this reference code in your application.

Applications should include the following information:

• resume
• motivation
• current study

Incomplete applications will not be considered.