patsnap - Semiconductor Materials Specialist
Requirements
• Education: Bachelor’s degree or higher in Polymer Materials, Chemical Engineering, Materials Science, or related disciplines; solid academic foundation. • Industry Experience: 3+ years of practical experience in photoresist, etch materials, or related fields, including but not limited to materials R&D, process development, failure analysis, technical intelligence, or data research. • Domain Knowledge: Thorough understanding of complete lithography and etch process flows and material systems; capable of independently evaluating material properties and their process correlations. • Data & Logical Thinking: Rigorous logical thinking with strong data awareness; able to translate complex research experience into standardized, structured data. • Communication: Clear, effective cross-functional communicator; capable of aligning data requirements and transferring domain knowledge with global team members. • Note on Coding: No advanced programming or software development experience (e.g., Python, SQL) is required. Your core value lies in your deep materials expertise and your unique ability to abstract complex scientific logic into structured data templates. • Background in process, materials, or data analysis at leading semiconductor manufacturers or top-tier material suppliers is highly desirable. • Experience developing and maintaining materials databases, process parameter libraries, or technical knowledge repositories. • Hands-on experience in patent analysis, material teardown, or failure analysis. • Familiarity with materials informatics tools, database management, or data annotation workflows. • Genuine curiosity about AI-enabled materials research and the digital application of process data. • ## What You’ll Be Building • You will be instrumental in building the foundational data framework of the Eureka Materials Agent for semiconductor reverse engineering. Converting deep lithography and etch expertise into structured, AI-ready datasets and database architectures, you will directly power advanced intelligent capabilities for materials analysis and process intelligence. • Your professional knowledge will become core, scalable assets within our AI products, broadening your career into AI-driven materials intelligence while your expertise is preserved as reusable digital infrastructure.
Responsibilities
• Domain Authority: Serve as the domain authority for lithography and etch materials within our semiconductor reverse engineering AI products. • Formulation & Tech Analysis: Deeply analyze advanced material patents, academic literature, and technical data to reconstruct material formulations, process parameters, and performance characteristics; extract structured, comparable technical data. • Template Architecture: Design and maintain unified data extraction templates and field specifications to ensure collected data meets industrial standards. • Data Engineering (Domain Level): Clean, normalize, and standardize process and materials data; build database frameworks optimized for AI model training and reasoning. • Data Governance: Establish data entry specifications, quality verification criteria, and version control rules to ensure data consistency, traceability, and long-term usability. • Comparative Insights: Conduct comparative analysis of materials and process data to identify technical traits, performance disparities, and process trade-offs. • Cross-functional Knowledge Transfer: Deliver data insights and domain logic to AI and product teams, powering intelligent search, material benchmarking, and process recommendation functions. • Tech Tracking: Track cutting-edge technologies and global market updates in lithography and etch materials; continuously enrich industry data resources. • Framework Ownership: Own materials domain data management, translating sophisticated technical logic into clear guidelines for algorithm and product teams. • Quality Assurance: Participate in data verification and AI function performance assessment, ensuring underlying data reliability from a materials science perspective.
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