Linearity characterization of Successive Approximation Register (SAR) Analog-to-Digital Converters (ADCs) is a cornerstone of mixed-signal verification, yet conventional methods—ramp or sine-histogram tests—require millions of samples and costly, highly-linear stimulus sources. Recent model-based techniques like uSMILE dramatically cut down sample counts but rely on offline post-processing. To push the boundaries of real-time testing, an adaptive, closed-loop measurement sequencing methodology using an Extended Kalman Filter (EKF) has been developed, yielding sub-0.2 LSB INL accuracy in under 70ms without post-processing. Current state: A behavioral model of a SAR-ADC has been implemented in C. Simulated ADC-Tests have been conducted using conventional histogram testing and the new approach. Still, the USER-SMILE methodology needs to be assessed for comparative reasons. No stimulus-error removal or segmented INL‐identification routines are yet available in the C test framework.
The goal of this Master Thesis is to integrate and benchmark the USMILE SAR-ADC test within the existing C-based simulation, enabling a direct performance comparison in terms of accuracy, test duration, and stimulus requirements. Tasks In the scope of this work, the following tasks will be accomplished: 1. Literature research on a. USMILE methodology: Segmented INL model fitting. b. SAR-ADC nonidealities and behavioral modeling in C. 2. Develop C modules for USMILE stimuli, including: a. Parameterizable, possibly non-ideal ramp/sine generators. b. Offset-injection routines for segmented testing. 3. Implement the USMILE algorithm in C, comprising: a. Stimulus Error Removal to correct generator nonlinearities. b. Segmented non-parametric INL identification across MSB/ISB/LSB levels. c. Calculation and formatting of INL/DNL metrics. 4. Integrate and automate the end-to-end USMILE flow: a. Batch invocation of stimulus modules and SAR-ADC model. b. Data capture, Segmented fitting, and report generation. 5. Simulation, evaluation, and validation: a. Execute on multiple ADC resolutions (e.g., 12–16 bit) and noise levels. b. Quantify test time reduction and INL/DNL accuracy 6. (Optional) Deploy and validate on an existing hardware test setup: a. Integrate the USMILE stimulus generators and C-based algorithms with the real-world DAC/ADC hardware.
- Basic understanding of ADC functionality - Strong mathematical understanding - Strong conceptual understanding - Strong programming skills (especially in C) - Background in analog and mixed-signal design (preferred)
Thorben Schey
Integrating autonomous ground vehicles (AGVs) into public traffic environments adds a new layer of complexity to vehicle interactions. This is particularly challenging when AGVs must coexist with both human-driven vehicles (HDVs) and semi-autonomous vehicles (SAVs). Differences in communication capabilities and available sensory data result in asymmetrical information and dynamic uncertainty. While AGVs can communicate with SAVs and perceive their environment through onboard sensors, they cannot directly interact with HDVs and must therefore infer their behavior. This creates a challenging setting where the AGV must balance efficiency and risk. This thesis explores the research question of how AGVs can ensure safety when faced with competing risks and asymmetric information. We will model the behavior of HDVs and SAVs probabilistically and combine these models with game-theoretic reasoning. The AGV acts as a strategic decision-maker. Its behavior is guided by an optimization problem that balances risk and efficiency.
1. Literature Review: a. Research existing approaches to behavioral modeling of human drivers and semi-autonomous agents. b. Review probabilistic models and game-theoretic frameworks used in autonomous driving. 2. Behavioral Modeling of HDVs: Development of a model to represent the behavior of human-driven vehicles (HDVs). 3. High-Level Decision-Making for the AGV: Design a high-level control framework that allows the AGV to react to dynamic traffic environments. 4. Interaction Modeling: Model the interaction between the AGV, SAV, and HDV in mixed-traffic scenarios, accounting for differences in communication and behavior. 5. Evaluation and Analysis: Identify and define suitable evaluation metric. Evaluation of the models with those metrics.
Mathematics & Probability: • Statistics, Probability Theory, Optimization Problems. Programming: • Proficiency in Python and/or C++. • Familiarity with Containerization Tools like Docker (optional).
Georgios Katranis
This thesis aims to improve the accuracy of internal temperature estimation in integrated circuits by enhancing the conventional thermal model (T_core = T_ambient + P_diss * R_th) using Kalman filtering techniques. The current approach relies on datasheet thermal resistance values that are often imprecise and fails to account for dynamic thermal behavior. The proposed method combines real-time power measurements with limited temperature sensor data within a Kalman filter framework to create more accurate temperature estimates.
-Identifying the correct thermal response characteristics through controlled experiments -Developing a dynamic thermal model that includes capacitive effects -Designing and implementing an appropriate Kalman filter structure -Characterizing measurement and process noise parameters -Validating the estimator performance across various operating conditions
Maurice Artelt
This master thesis focuses on optimizing the inference phase of graph processing models, addressing the unique challenges posed by graph-structured data. The research aims to improve model performance for enhanced user experience while exploring hardware-specific optimizations to achieve maximum efficiency in graph-based computation.
- Performance Enhancement: Develop techniques to reduce inference latency and improve throughput in graph processing models without compromising accuracy or graph analysis capabilities. - Graph Algorithm Optimization: Investigate and implement specialized methods for optimizing graph algorithms, including graph traversal, message passing, neighborhood sampling, and aggregation operations. - Hardware Acceleration: Explore hardware-specific optimizations for various platforms (GPUs, TPUs, CPUs, edge devices) with a focus on irregular memory access patterns, sparse computation, and parallel execution strategies particular to graph processing. - User Experience Metrics: Define and measure user experience metrics related to graph model inference speed and establish benchmarks for real-world graph-based applications.
- Knowledge of mathematical machine learning fundamentals. - Proficiency in at least one graph processing framework (PyG, DGL, GraphScope). - Experience with neural network architectures. - Basic CUDA or triton programming skills are beneficial. - Understanding of memory hierarchies, bandwidth constraints regarding Linux systems. - Programming in Python, C++ or Zig.
Sebastian Baum
This master thesis explores methods for decomposing geometric structures represented as graphs into independent topological units. By identifying natural boundaries within geometric graph structures, these units can be processed separately while maintaining the overall topological integrity.
You will develop algorithms to identify and extract topologically coherent subunits from geometric graphs. These algorithms must preserve critical structural information while allowing independent processing of each unit. One major problem could the stitching of these independent units. You will implement and evaluate reconstruction methods that can generalize from these topological units to more complex structures. The thesis will include theoretical analysis of the approach's mathematical foundations as well as practical demonstrations in application domains such as 3D simulation processing.
- Knowledge of mathematical machine learning fundamentals. - Proficiency in at least one graph processing framework (PyG, DGL, GraphScope). - Experience with neural network architectures. - Programming in Python, C++ or Zig.
Sebastian Baum
Testing of AI-based systems such as autonomous vehicles is challenging due to many situations and scenarios. Brute force is expensive and has gaps, as we see in practice. We thus use synthetic data for an AI-driven testing. This data covers real-world scenarios to train autonomous systems in a simulation-based environment. The training success is evaluated in a data loop and enhanced to close blind spots and unknown knowns. This thesis targets to integrate a requirements and test engine to an automated test system.
The goal of the thesis is to integrate existing parts of the system. A fully running system shall be implemented. The integration comprises verification and validation checks for the existing parts. Professional tools such as DOORS shall be used for industry-scale AI-based testing of autonomous systems.
Knowledge in Python Industry-scale software engineering and tools Work in a self-independent way Passionate about clean and good quality code Capable of integrating your work with other parts of the system
Christof Ebert