Master Thesis – Agentic AI for Stimulus Creation During Coverage Closure

Master Thesis – Agentic AI for Stimulus Creation During Coverage Closure

Category Embedded Systems, Electrical Engineering or Computer Science

Scope Two Master Thesis students, 30 hp each

Background Axis has a long history of developing integrated circuits for our products. The series of ARTPEC chips is a cornerstone in most Axis products, as discussed here: Axis System-On-Chip, and ARTPEC-9. The ASIC department is working on developing new generations of the ARPTEC SoC using the most modern chip technology available. See also The history of ARTPEC.

In ASIC verification, constrained-random testing can exercise much of a design efficiently. However, reaching the remaining coverage targets often requires engineers to create targeted stimulus manually. This involves analyzing coverage reports, specifications, constraints, and testbench code, followed by repeated implementation, compilation, simulation, and debugging.

This thesis project will explore how an Agentic AI solution can support stimulus creation during coverage closure, which is essential bug-free hardware modules in the final SoC.

Goal The goal is to adapt and tune the Agentic AI pipeline for coverage-directed stimulus creation and evaluate whether it can reduce engineering effort or elapsed time while producing valid, reproducible tests.

Working as a pair, you will:

• Investigate state-of-the-art methods for using agentic AI in ASIC Verification.
• Establish a benchmark for the existing AI pipeline and reconstruct the historical manual effort for the selected modules.
• Choose the coverage scope after an initial analysis, with functional coverage as the likely primary focus and code coverage included where appropriate.
• Tune the pipeline through improvements to prompts, context selection, retrieval of relevant code and examples, stimulus-output formats, and iterative tool feedback.
• Evaluate the final pipeline on held-out coverage targets, comparing its results with the initial pipeline and the original manual solution.

The expected outcome is a tuned Agentic AI pipeline, a documented benchmark history, and an evidence-based assessment of where AI-assisted stimulus creation is useful—and where it falls short. Model-weight training or fine-tuning is not required; the main focus is on adapting the pipeline around the language model.

Who are you? For this Thesis proposal we target students with a strong interest in Embedded Electronics Engineering, with a focus hardware development, including design and verification of digital solutions and a system on chip.

Type of Employment Thesis Worker (Fixed Term)

Posting End Date 2026-11-27

Certain roles at Axis require background checks, which means applicable verifications will be done in these recruitments. Notice will be provided before we take any action.

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