Post-silicon validation remains one of the most time-critical and resource-intensive phases in the semiconductor development cycle. Traditional approaches—often manual, sequential, and disconnected from design—struggle to keep pace with today’s rapidly evolving product requirements. This paper introduces TestOps: an automation-first, AI-enabled methodology inspired by DevOps principles, designed to modernize post-silicon validation.
In contrast to the typical flow where test automation is developed in silos and updated reactively, TestOps establishes a connected validation infrastructure that spans from specification definition through test execution and reporting. Validation and test automation activities begin in parallel with design, supported by workflows that are dynamically responsive to changes—allowing the infrastructure to evolve alongside the product.
AI plays a central role throughout this workflow—parsing evolving specifications, generating test plans and sequences, identifying coverage gaps, and analyzing silicon data to highlight anomalies or suggest root causes. By dramatically reducing manual effort, improving reuse, and accelerating debug, AI shifts the engineer’s focus from infrastructure triage to high-value problem solving.
This approach enables test environments and automation frameworks to be lab-ready before first silicon arrives. Upon device arrival, pre-qualified test sequences can be executed immediately, with high confidence in bench stability. Debug efforts are focused entirely on device behavior—not infrastructure issues—enabling bring-up of functional silicon in under a week, even for complex products.
This paper outlines the architecture, tools, and workflow components required to implement TestOps effectively. We also address common challenges—such as managing specification drift, ensuring version synchronization, and establishing feedback loops for continuous improvement—and propose practical strategies to overcome them.
By adopting TestOps, engineering teams can compress validation cycles, improve cross-functional collaboration, and establish a reusable infrastructure for future designs. This approach sets a scalable foundation for next-generation post-silicon validation—driven by automation, powered by AI, and aligned with modern product development needs.