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Advantest

Application Engineer

Reposted 3 Months Ago
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Remote
Hiring Remotely in IND
Mid level
Remote
Hiring Remotely in IND
Mid level
Develop iJTAG (IEEE 1687) based test solutions and DFT methodologies (scan, BIST, hierarchical access). Build AI/ML features for pattern compaction and failure analysis, create automation frameworks and scripts (Python/C++/Tcl/Perl/Shell), and build APIs/pipelines for large-scale ATE data ingestion (STIL/WGL) and visualization dashboards. Collaborate in fast-paced, team-oriented environment and escalate critical issues.
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Job Summary & Responsibilities
  • Lead development of iJTAG (IEEE 1687) based test solutions, including scan network modeling, instrument access, and test pattern execution.
    • Design and implement DFT methodologies including scan, BIST, and hierarchical access mechanisms.
    • Build AI/ML features in DFT tools for pattern compaction, failure prediction, and scan-failure root-cause analysis
    • Develop scalable automation frameworks in Python/C++ and streamline DFT workflows using Tcl/Perl/Shell
    • Build APIs and pipelines for Large-scale data ingestion (ATE logs, STIL/WGL patterns) and data processing and visualization dashboards
Preferred Qualifications

BE/ME Computer Science, Electronics Engineering or related professions with at least 2-5 years working experience in software development
• Understanding of Basic DFT concepts (scan chains, testability basics) and semiconductor testing fundamentals
• Strong programming expertise in C,C++ or Python and experience in data structures, algorithms, and scalable system design in Linux OS
• Strong attention to details, verbal communication and written skill sets, and ability to work with a highly diverse team-structured environment with positive attitude is mandatory
• Ability to work in a fast paced, project oriented, team environment and escalate any critical issues to the senior management.

Desired
• Exposure to ATE platforms (V93000 or similar) Linux-based development
• Basic knowledge of iJTAG / IEEE 1687 concepts
• Contributions to test optimization/yield improvement frameworks
• Interest or exposure to AI/ML data analysis, automation, or simple ML models applied to test/debug

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