The traditional development of test programs for Analog Mixed Signal (AMS) circuits is both time-consuming and cost-intensive, with validation possible only for physically available chips. Testing is conducted by providing a set of test inputs, where the type of inputs and the optimal sequence is highly dependent on the Circuit Under Test (CUT) and therefore requires expert knowledge. A virtual test framework that enables pre-tapeout testing and aids the design phase with early feedback regarding the fulfilment of circuit specifications is highly desirable. Current state: A framework in Python has been developed around the simulator Siemens Questa (originally Mentor Graphics). For simulation, various models of AMS circuits, test instruments, and signal transmission paths are utilized. These models are programmed as Verilog-AMS modules and function as a configurable library. Currently, the framework is controlled through Python API calls.
The goal of this Master Thesis is to develop a graphical user interface (GUI) inside Matlab Simulink for creating a test setup with automatic netlist export. This interface should function similarly to electrical schematic editors and should be intuitive to use. A key aspect should be its extendibility for future components and enhancements. In the scope of this work, the following tasks will be accomplished: 1. Conducting literature research on a. chip testing and virtual chip testing and b. graphical user interfaces and GUI design in Matlab Simulink. 2. Development of the GUI elements for the test setup generator. This includes: a. A schematic editor for connecting different components b. A component editor for creating components c. A parameterization overlay for the components and a compatibility checker 3. Development of a data exchange format for storing components and setup configurations 4. Development of parsers for saving and loading configurations/netlists
- Very good conceptual skills - Fundamental knowledge of software engineering - Initial experience with Matlab/Simulink - Very good knowledge of German and English
Thorben Schey
Modern system development is characterized by increased customer requirements and greater market and time pressure. The innovations required for this are created on the one hand by a higher proportion of software in products and on the other hand by the networking of more and more previously independent systems, resulting in heterogeneous and therefore more complex IT structures overall. This is also reflected in the automotive industry, where new business models are being developed via software-defined vehicles. Modern E/E architectures enable the vehicle to communicate with its environment as well as collect data during operation, which can then be used by manufacturers to improve driving or comfort services. To realize such a data loop, automated analysis of the software in operation is key. A key challenge here is to link the events occurring in the system in order to be able to determine the cause of any errors. Conventional approaches fail to take into account temporal behavior as well as contextual information, which can be provided by a system engineer. Therefore, a reinforcement learning approach is to be developed in the context of this thesis, which can incorporate the knowledge of the system engineer as well as information about updates of the system into the automated linkage.
- Analysis of existing approaches for root cause analysis - Development of an own approach in the context of a software-defined DevOps environment - Integration into an analysis platform for distributed cloud systems - Evaluation of the approach using an own data set and comparison with conventional methods
- Very good conceptual skills - Prior knowledge of deep learning and Markov chains - Basic knowledge of software engineering and IT systems - Good programming skills - Very good English skills
Matthias Weiss
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
The aim of the project is to identify the different drivers of complexity within this project and quantify it with appropriate measures. We focus on the change of the software part. Specifically, we want a methodology that predicts the complexity of changing a given software module. In order to assist the management of the Digital Twin, an assistant system is to be created, which assesses the software complexity based on the described aspects. The feasibility of the assistant system will be evaluated on the software stack of the Digital Twin. Since we will have five different implementations of the same problem from the lab courses, there will be test data available to check the assessment results for plausibility.
The Master Project should first analyze the literature for drivers of complexity and established complexity measurement methods in order to derive a methodology that identifies the complexity drivers and quantifies them. Moreover, within the project, an assistant system will be developed that assesses the complexity and visualizes the results. The assistant system will be evaluated using different variants of the Digital Twin.
Independent, scientific work Very good math and programming skills Good English skills
Golsa Ghasemi