Inspiration
Semiconductor experiments and process development generate large amounts of electrical and physical data. Detecting an unusual signal is often not the hardest part; the harder question is what that change may mean physically and what should be checked next.
SemiSense MCP was conceived as an AI-assisted bridge between raw semiconductor data and physics-informed engineering reasoning.
What it does
SemiSense MCP is a semiconductor data analysis assistant designed for the Alexa+ track.
Users can provide process or experimental parameters such as temperature, voltage, current, pressure, and other measurement data. The system can:
- screen data for abnormal patterns;
- identify which variables contribute most strongly to an anomaly;
- provide physics-informed explanations of possible mechanisms;
- distinguish possible correlations from confirmed root causes;
- recommend additional measurements or checks;
- summarize the analysis in a structured, human-readable form.
The project is designed as a decision-support tool rather than an automated replacement for engineering judgment.
How it works
The planned architecture combines:
- data preprocessing and validation;
- statistical and machine-learning based anomaly detection;
- physics-informed reasoning rules;
- an MCP interface for exposing semiconductor analysis tools to an agent;
- a lightweight web interface for demonstrating an Alexa+-style interaction.
The demonstration uses clearly labeled synthetic semiconductor data so that no proprietary fabrication or laboratory data is required.
Why MCP
Instead of building another isolated dashboard, SemiSense exposes its analysis functions as reusable tools through the Model Context Protocol. This allows an AI agent to request specific analyses, retrieve structured results, and combine them into an explanation.
Challenges
A key challenge is preventing an AI system from presenting correlation as confirmed causation. SemiSense therefore separates measured anomalies, model outputs, possible physical mechanisms, and recommended verification steps.
What's next
The next stage is to connect the MCP tools to richer semiconductor datasets, expand the physics-rule library, and evaluate the system with real experimental data where appropriate permissions are available.nspiration
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