Intern - AI‑Driven Process Development Deposition & Ion implantation

Role Overview

As an AI‑Driven Process Development Intern, you will apply data analytics, machine learning, and statistical methods to accelerate thin‑film deposition and ion implantation process development for advanced semiconductor technologies.

This role bridges process engineering and AI, focusing on extracting physics‑aware insights from process, inline, electrical, and metrology data to improve process optimization, variability control, and manufacturability for CVD, ALD, PVD metals and ion implant modules.

You will work closely with process development engineers to translate data‑driven findings into actionable process knobs, learning experiments, and risk mitigation strategies across the technology development lifecycle.

Key Responsibilities
AI & Data Analytics for Process Development

1. Perform exploratory data analysis (EDA) on large‑scale process, tool, inline, metrology (XRR, XRD, CD, thickness, Rs), and electrical datasets to identify trends, correlations, and process sensitivities.
2. Apply machine learning models (regression, classification, clustering, time‑series) to support: Process window optimization, Variability reduction and excursion detection and Predictive learning for film properties and implant behavior

3. Develop physics‑informed features (dose, pressure, temperature, power, gas flow, energy, tilt, etc.) relevant to CVD / ALD / PVD / ion implant processes.

Statistical Modeling & Anomaly Detection

1. Conduct statistical analysis including regression, hypothesis testing, multivariate analysis, and DOE interpretation.
2. Apply anomaly and drift detection techniques to identify tool‑to‑tool differences, chamber aging effects, and early signals of process instability.
3. Support FOAK / learning line data analysis and rapid feedback during early process bring‑up.

Visualization & Communication

1. Create clear visualizations, dashboards, and summaries to communicate insights to: Process engineers, Integration partners and 3. Technical leadership

2. Translate complex AI outputs into explainable narratives, including feature importance, root‑cause hypotheses, and process recommendations.

Collaboration & Project Delivery

1. Collaborate with process engineering, equipment, integration, and data science teams to scope impactful intern projects.
2. Develop clean, reusable data pipelines for data extraction, cleaning, and feature engineering.
3. Deliver a complete intern project package, including:

Code / notebooks, Documentation, Presentation‑ready technical summary with clear impact to process development