[NanoIC topic] AI-Driven semiconductor test data analytics

The mission We are
aiming to reduce the "Time to Market" in our semiconductor R&D by
revolutionizing how we analyze test data. Currently, our engineers manually
parse complex JSON files containing parametric extraction and electrical
curves. Inspired by
recent literature on AI-Driven Semiconductor Test Data Analytics , we
want to build a system that combines a Data Translator with an
Interactive AI Agent . Your goal is to create a python-based framework where an
engineer can ask natural language questions (e.g., "Show me the defect
pattern on Wafer X" or "Compare the yield of Lot A vs Lot
B" ) and receive instant statistical insights and visual diagnostics. Key responsibilities
Literature review & strategy: Start by analyzing
state-of-the-art papers (such as Wang et al. on IEA-Plot and recent
TPOR frameworks). Benchmark the pros and cons of using Knowledge Graphs
vs. Vector RAG vs. SQL Agents for our specific data topology. 
Universal data translator: Design
a pipeline to ingest strandardized JSON data files (curves, parameters, flags)
and standardize them into a unified format (Parquet?) suitable
for AI querying.
Agentic AI development: Build a "Code
Interpreter" agent using Python (LangChain/LlamaIndex) and local LLMs
(Llama 3, Mistral) on our GPU servers. The agent must be capable of
writing code to perform statistical aggregations (PCA, Mean, Sigma)
without hallucination.
Advanced visualization: Go beyond basic charts.
Implement an automated plotting module capable of generating   distribution curves, and correlation plots based on the chat context.
Prototyping: Wrap this technology in a
user-friendly web interface (Streamlit) for immediate feedback
from R&D engineers. It's possible that the student considers an integration in Azure or in Copilot studio but a stand alone solution is prefered. Decision will be decided after point 1 ( Literature review & strategy )  What you will learn
Research-to-Production: How to take concepts from
academic papers and implement them in
a real industrial environment.
Agentic workflows: Mastering the intersection of
LLMs and deterministic code execution.
Semiconductor domain knowledge: Understanding the Parametric Chip/Device MOSFET testing.
High-Performance computing: Utilizing local GPU
infrastructure for secure, private model inference. Requirements
Master’s
student in CS, Data Science, AI, or EE. Ph.D exchange. Understanding of LLM architectures (transformers, LSTM) and AI fundamentals is a must. Familiarity with advanced LLMs architecture is preferred. Experience in already trained LLM models locally is preferred.
Strong Python skills:  Experience in deep learning frameworks (tensorflow, pytorch) and related libraries such as pandas, numpy, is a must. 
Academic mindset: You are comfortable reading
IEEE/research papers and extracting the methodology to apply it to code.
GenAI Knowledge: Understanding of RAG
(Retrieval Augmented Generation) and LLM limitations.
Familiarity with Git and
Linux/Unix environments. Nice to Have
Knowledge of Knowledge
Graphs (Neo4j, NetworkX).
Experience processing spatial
data (heatmaps/wafer maps). How to
Apply Please
send your CV. Bonus: In your email, briefly
mention one challenge you foresee in applying LLMs to numerical scientific data Type of internship : Master internship, PhD internship Duration : 6 Required educational background : Computer Science University promotor : Siegfried Mercelis (UAntwerpen) Supervising scientist(s) : For further information or for application, please contact Jerome Mitard ( [email protected] ) The reference code for this position is 2026-INT-074 . Mention this reference code in your application. Only for self-supporting students. Applications should include the following information: resume motivation current study Incomplete applications will not be considered