Wide-Bandgap Power Semiconductors & Systems

Gallium nitride (GaN)-based high electron mobility transistors (HEMTs) play a key role in modern power electronics, especially in applications such as high-frequency circuits, power converters and renewable energy systems. Precise modeling of these transistors is crucial to maximize their performance and efficiency. An established model for this is the ASM GaN HEMT model, which was developed specifically to describe the complex physical properties of GaN transistors. However, traditional approaches to parameter extraction, such as manual fitting, often reach their limits, especially for nonlinear systems such as HEMTs. Reinforcement learning (RL) offers a promising alternative by enabling adaptive, data-driven optimization strategies that could improve the accuracy and efficiency of parameter extraction for the ASM model.

Type of Thesis:

BA ❌ FA ✅ MA ✅ 

Relevant Experience:

• Basic knowledge of transistor modeling
• Experience with programming (ideally Python) and Machine Learning
• Interest in the application of modern AI methods in Power Electronics

Contact:

Diego Kuderna Melgar

Motivation

GaN power semiconductors enable significantly higher switching frequencies and power densities than silicon components. However, they are highly sensitive to parasitic inductances, thermal effects, and asymmetric gate/load distribution—especially in half-bridge modules with transistors connected in parallel. Conventional single-domain simulation approaches are no longer sufficient. An automated, parameterized co-simulation environment (combining electrical, thermal, and layout parasitics) enables systematic design optimization and provides robust recommendations for layout, cooling, and gate control.

Objective

Development and demonstration of a reproducible, automated workflow for coupled multi-physics simulation and optimization of a GaN half-bridge. This includes investigating key influence factors (parasitics, geometry, cooling, gate drive) and deriving practically relevant design and gate-drive guidelines.

Type of Thesis

Bachelor Thesis (BA): ❌ | Research Project (FA): ✅ | Master Thesis (MA): ✅

Work Packages

• Research & Specification: Definition of the reference topology and target metrics.

• Modeling: Development of electrical models in Keysight ADS and a thermal FEM model.

• Implementation: Development of the ADS-Python interface for coupling, tuning, and automation.

• Optimization: Execution of multi-objective optimizations and sensitivity analyses.

• Validation & Documentation: Data processing, validation, and creation of a design guide.

Requirements

• Knowledge of power electronics and semiconductor physics (GaN experience is a plus).

• Experience with Keysight ADS (or willingness to learn) and Python scripting.

• Basic knowledge of thermal/electrical FEM (e.g., COMSOL, Ansys) is advantageous.

• Strong programming skills (Python), analytical thinking, and an independent working style.

Expected Results / Deliverables

• A fully functional script/framework for automated co-simulation (ADS + FEM).

• An optimization study with quantified improvements compared to the baseline design.

• Design recommendations and guidelines.

• A reproduction guide and the source code (documentation).

If you are interested or have further questions, please contact:
Manuel Rueß

Together with the wide-bandgap semicondcutor based power transistors, such as SiC power MOSFETs and GaN power HEMTs, ceramic capacitors form the basic building blocks of fast-switching high current and voltage commutation cells and power modules in high power density switched-mode power converters. The power capacitors must follow suit with the power transistors in high-frequency capabilities, in order not to become the bottleneck for switching loss energy and thermal management, and accurate simulation models of the capacitors‘ high-frequency characteristcs must be developed for the reliable design of fast-switching power modules. Together with the wide-bandgap semicondcutor based power transistors, such as SiC power MOSFETs and GaN power HEMTs, ceramic capacitors form the basic building blocks of fast-switching high current and voltage commutation cells and power modules in high power density switched-mode power converters. The work is carried out in collaboration at University of Stuttgart, Germany, and at ENSI Caen, France. The final work programme is determined in trilateral planification between the tutors and the student and can comprise: Survey of the prevailing state of the art, Microwave characterisation of ceramic capacitors at ILH, including the design of dedicated test breadboards, Model development, implementation in VerilogA and verification at ENSI Caen, Optional: power module breadboard design and experimental validation at ILH.

Type of Thesis:

BA ✅ FA ✅ MA ✅ 

Relevant Experience:

• Theoretical expertise in power semiconductor devices and power electronics circuits is recommended.

Contact:

Ingmar Kallfass

More Information:
The work is carried out in collaboration and joint tutorship with ENSI Caen, France. Parts of the work may be carried out at ENSI Caen.

Gallium Nitride (GaN) power semiconductors offer high switching speeds, low losses, and compact designs, making them ideal for next-generation brushless DC (BLDC) motor inverters. However, achieving optimal performance requires targeted filter design to enhance efficiency, improve electromagnetic compatibility (EMC), and enable soft switching.

In this thesis, suitable LC filter concepts will be developed, simulated, and evaluated to optimize the switching behavior and overall system performance of GaN-based BLDC inverters. The goal is to achieve Zero Voltage Switching (ZVS) or Zero Current Switching (ZCS) through appropriately designed resonant or commutation networks.

Type of Thesis:

BA ✅ FA ✅ MA ✅ 

Relevant Experience:

• Power Electronics 1
• Basic knowledge of circuit simulation and electrical measurement techniques
• Experience with MATLAB/Simulink, LTspice, or PLECS is an advantage

Contact:

Jeremy Nuzzo