Materials science intern
Job Summary
We are seeking a detail-oriented candidate to structure and curate semiconductor process knowledge. You will translate complex technical content into standardised insights and collaborate cross-functionally to enhance data quality.
Key Responsibilities:
- Distill and author standardized Process Skills from annotated data and patent texts covering process objectives flow steps key parameters control points common defects alternative solutions and evolution paths.
- Participate in building a Semiconductor Process Knowledge Graph mapping relationships among processes materials equipment devices and performance metrics.
- Assist in designing process retrieval and recommendation logic; optimize knowledge presentation formats (e.g. process cards flowcharts decision trees comparison matrices).
- Work closely with product managers and algorithm teams to define knowledge-base schemas quality standards and update mechanisms.
- Track cutting-edge semiconductor process trends (e.g. Advanced Packaging GAA transistors novel memory devices power semiconductors) and continuously enrich knowledge content.
Requirements
- Currently enrolled in a Bachelors degree or higher in Materials Science and Engineering (or a related field such as Electrical or Chemical Engineering) with a solid understanding of semiconductor materials and processes.
- Excellent technical writing and knowledge distillation skills; adept at abstracting complex process logic into clear reusable structured entries.
- Familiar with full semiconductor flows (FEOL/BEOL) or a specific segment (logic memory advanced packaging compound semiconductors).
- Rigorous logical thinking; basic understanding of information architecture or databases. Knowledge of Knowledge Graphs RAG (Retrieval-Augmented Generation) or LLM applications is preferred.
- Able to seamlessly process technical materials in both languages and produce bilingual knowledge content.
Required Experience:
Intern
About Company
Patsnap empowers IP and R&D teams with advanced AI to get better answers and make faster decisions. Increase IP productivity by 75% while reducing R&D wastage by 25%.