Call for Proposal
Download the Call for Proposal
This call is not for individual papers. The AI in Test (AIT) Workshop invites session proposals that examine how AI may transform major areas of semiconductor test. AIT expects to accept approximately 4-5 session proposals; each session should address a broad application area rather than a narrow point solution. Example areas include:
- Test-program requirements, generation, revision, maintenance, and evolution
- Test-data analytics, yield learning, debug support, and engineering reporting
- Test infrastructure, data services, MCP interfaces, and AI-ready workflow platforms
- Test operations, manufacturing optimization, scheduling, material flow automation and productivity improvement
- DFT, validation, silicon bring-up, diagnosis, reliability, and failure analysis
- Supply-chain, documentation, knowledge management, and cross-organization workflows
- AI agents, engineering copilots, app-based agents, and skill/workflow ecosystems for test engineering
Submission: Prospective organizers are invited to submit a 1-2 page PDF session proposal at the designated EasyChair platform. Submissions should include:
- Session title; organizer names, affiliations, and contact information
- Session abstract and description of the proposed topic and motivation
- Proposed session structure, such as invited talks, panel discussion, moderated debate, demonstrations, or audience interaction
- Expected speakers or speaker profiles, if known; expected takeaways for workshop participants
(Sessions are expected to be 60 to 90 minutes in duration)
A session proposal should define a topic and explain how the session will guide discussion around the following questions:
- Technology assessment: What AI technologies are applicable to this area, and what is their current maturity?
- Investment strategy and ROI: Where should companies invest, and how should return be measured beyond token cost/speedup?
- Quality and trust: How should AI-generated results be evaluated for correctness, reliability, reproducibility, and engineering trust?
- Duplication versus real progress: When do similar AI solutions indicate duplication, independent validation, or necessary adaptation?
- Workforce impact: How may job content change, which tasks may be automated or augmented, and what new jobs may emerge?
- Future skills: What skills will be required for AI-native semiconductor-test organizations?
Note on scope: the objective of this workshop is not to reach definitive answers or predict the future impact of AI on semiconductor testing. The AI landscape is evolving too rapidly, and many technologies are still in their early stages of adoption. Instead, the goal is to help the community identify the right questions to ask, the appropriate evaluation criteria to apply, and the key uncertainties that organizations must consider when making technology, investment, and workforce decisions. Session organizers are encouraged to present evidence, observations, experiences, competing viewpoints, and open challenges rather than attempting to advocate a single conclusion.
Key dates:
Submission deadline: July 24, 2026 August 17, 2026 (Extended deadline)