AI-assisted development and validation for semiconductor design intelligence - ON-1265
Project type: ResearchDesired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences, Statistics / Actuarial sciences
Company: Advanced Micro Consulting Inc. (AMCI)
Project Length: 6 months to 1 year
Preferred start date: As soon as possible.
Language requirement: English
Location(s): ON, Canada
No. of positions: 2
Desired education level: Undergraduate/BachelorMaster's
Open to applicants registered at an institution outside of Canada: No
About the company:
The proposed project will investigate how AI can help semiconductor engineers interpret design-report evidence while improving the reliability of the software and workflows used to deliver that intelligence. The initial focus is MachAI Lens, which compares timing results across builds and operating scenarios using reports from existing EDA tools.
With an academic supervisor and AMCI engineering guidance, the researcher(s) would define a focused use case, prototype selected AI-assisted functionality, and build repeatable validation methods. Proposed tasks include preparing permitted or synthetic report examples; comparing AI-generated explanations against source evidence and expert review; measuring incorrect or unsupported outputs; and developing regression checks for selected product and GUI workflows. A related option is evaluating AI-assisted prompting, code review and debugging against agreed quality and development-time baselines.
Expected outputs are a documented prototype or workflow improvement, a reproducible evaluation/test suite, measured results and recommendations for further development. Research questions include how to preserve traceability to evidence, evaluate usefulness to engineers and improve reliability across differing report formats and scenarios. Scope, research methodology, deliverables and program stream will be finalized with Mitacs and the academic partner. Only authorized, de-identified or synthetic data would be used, subject to confidentiality and IP arrangements.
Describe the project.:
The proposed project will investigate how AI can help semiconductor engineers interpret design-report evidence while improving the reliability of the software and workflows used to deliver that intelligence. The initial focus is MachAI Lens, which compares timing results across builds and operating scenarios using reports from existing EDA tools.
With an academic supervisor and AMCI engineering guidance, the researcher(s) would define a focused use case, prototype selected AI-assisted functionality, and build repeatable validation methods. Proposed tasks include preparing permitted or synthetic report examples; comparing AI-generated explanations against source evidence and expert review; measuring incorrect or unsupported outputs; and developing regression checks for selected product and GUI workflows. A related option is evaluating AI-assisted prompting, code review and debugging against agreed quality and development-time baselines.
Expected outputs are a documented prototype or workflow improvement, a reproducible evaluation/test suite, measured results and recommendations for further development. Research questions include how to preserve traceability to evidence, evaluate usefulness to engineers and improve reliability across differing report formats and scenarios. Scope, research methodology, deliverables and program stream will be finalized with Mitacs and the academic partner. Only authorized, de-identified or synthetic data would be used, subject to confidentiality and IP arrangements.
Required expertise/skills:
Proposed profile: computer science, electrical/computer engineering or a related discipline, with strong software development, testing and analytical skills. Relevant skills include Python or comparable programming, Git, structured/unstructured data processing, experimental design, evaluation metrics and technical documentation. Interest or experience in AI/LLMs, prompt evaluation and human-reviewed AI output is valuable.
Assets: semiconductor design, static timing analysis, Synopsys/Cadence report familiarity, automated GUI/regression testing and AI-assisted development tools such as Claude Code or ChatGPT. Prior EDA experience would be helpful but need not be mandatory for every role. Candidates should communicate clearly and respect confidential engineering data. Final requirements will follow the agreed project scope.
