Post-silicon validation is a critical phase in the semiconductor product lifecycle, where real silicon devices are rigorously tested against design specifications under various operating conditions. As chips grow increasingly complex and time-to-market pressures intensify, traditional validation approaches—reliant on manual planning, scripting, and analysis—struggle to meet the required levels of speed, scalability, and accuracy. This paper introduces a novel framework to modernize post-silicon validation using a GenAI-powered Agentic TestOps Ecosystem—a collection of specialized AI agents designed to automate, augment, and intelligently orchestrate validation workflows end-to-end.
Unlike conventional automation tools or scripting frameworks, these agents are built on large language models (LLMs) and domain-specific knowledge. Each agent acts as an autonomous and collaborative digital assistant with a unique role: planning validation schedules, configuring instruments, generating test scripts from specifications, validating results against tolerance bands, analyzing anomalies, and generating compliance-ready reports. The agents have access to shared memory and contextual understanding, enabling them to reason over test plans, historical logs, instrument metadata, and DUT configurations.
A key innovation in this approach is the transformation of post-silicon validation into a dynamic, adaptive, and goal-driven process. For instance, if a test case fails due to an environmental variation or measurement deviation, agents can autonomously reconfigure the instrument setup, adjust test parameters, regenerate scripts, or recommend retest strategies—all with minimal human intervention. This drastically reduces the time spent on debugging, triaging, and rerunning tests, while improving traceability and reproducibility.
The agentic ecosystem also supports continuous learning and closed-loop optimization. Feedback from previous validation cycles, user input, and observed anomalies are used to fine-tune the agents’ behavior over time. This leads to incremental improvements in test coverage, test quality, and turnaround time.
By introducing AI-native agents that combine reasoning, language understanding, and tool integration, the proposed ecosystem enables a scalable and intelligent layer of automation tailored specifically for semiconductor validation. It offers a practical path to increasing test throughput, engineering efficiency, and product quality without disrupting existing lab infrastructure. The result is a modernized, agile validation environment equipped to meet the demands of next-generation silicon.
