By: Shitendra Bhattacharya, Regional Director, Emerson’s Test and Measurement Business Group
The semiconductor industry is constantly under pressure to deliver higher-performance silicon to meet the demands of modern technology. The Covid-19 pandemic highlighted the vulnerabilities in the semiconductor supply chain but did not diminish the demand for innovative silicon, driving continual advancements in product development.
For semiconductor companies, this period of transformation necessitates evolving processes to stay ahead. A crucial area requiring innovation is post-silicon validation, a phase that has not kept pace with advancements seen in pre-silicon and production processes. Post-silicon validation, encompassing up to 60 percent of the total engineering effort in new product development (NPD), is the final opportunity to identify and correct design flaws before the product reaches customers. Missing a bug at this stage can have significant consequences, yet the time allocated for validation is often shortened due to delays in upstream processes.
The role of post-silicon validation
Post-silicon validation involves testing the silicon after it has been fabricated. It is essential because it verifies that the silicon operates correctly under various conditions and meets all specifications. Despite significant improvements in pre-silicon and production stages, the methodologies for post-silicon validation have lagged behind.
Below are a few software-centric approaches that can be taken for validation:
Automation in validation labs
Automation in post-silicon validation labs is crucial for meeting complex requirements within shorter cycle times. Automation stretches test equipment budgets and reduces the time needed for comprehensive characterisation. However, many tools used are either custom-built or off-the-shelf general-purpose tools that require customisation and upkeep. Leading organisations have further enhanced efficiency by adopting a software-connected approach, standardizing test measurement processes, and applying digital transformation efforts across labs globally.
Standardization for efficiency
Standardisation in validation labs helps engineers meet schedule challenges, increase test coverage, and ensure product quality. Effective standardisation can automate over 80 percent of repetitive tasks, saving engineers valuable time. It also offers the flexibility needed to perform various measurements for final product validation.
A standardized hardware infrastructure can significantly reduce hardware development and debugging time and costs. This approach involves using a base motherboard and plug-in daughter cards for each device, promoting reuse across a family of products. Benefits include reduced hardware design time, quick turnaround for device-specific daughtercards, and the ability to connect instruments close to the device under test (DUT) without redesigning or maintaining excess inventory.
Similarly, standardizing software in validation labs allows engineers to focus on critical validation tasks rather than developing and debugging software. This infrastructure supports process-voltage-temperature (PVT) characterisation, where measurements are automated across various inputs and conditions. Standardizing on an open and scalable automated infrastructure enables engineers to concentrate on high-value tasks and minimizes time spent on software development and debugging.
Building the next-generation lab involves starting small and scaling to new teams after each success. Standardizing tools across simulation, validation, and production stages allows code reuse, saving time and effort. This approach enables the seamless transfer of solutions across teams, enhancing organisational efficiency.
Digital transformation in semiconductor labs
Digital transformation involves adopting and implementing digital technology to improve efficiency and results. For semiconductor companies, this means faster product introductions, shorter R&D cycles, and automation and data analytics across workflows. Machine Learning (ML) and Artificial Intelligence (AI) advancements can further enhance these processes.
Effective data management is crucial as design, validation, and test engineers generate substantial amounts of data. Properly managing and leveraging this data improves productivity, providing better visibility from design verification to production. Centralised data repositories and standard methods for capturing and storing data enable data mining for insights and training machine learning models for future applications.
The future of post-silicon validation
The semiconductor industry’s increasing demands, silicon complexity, and shrinking time-to-market necessitate optimised resources and innovation. Modernising post-silicon validation with data management and machine learning tools is essential. How companies address this segment of NPD will largely determine their competitiveness in the semiconductor market.
The vision of next-generation labs as catalysts for change is emerging. Through standardization and digitalisation, engineers can unlock new productivity levels, automate repetitive tasks, and focus on high-value validation. Digital transformation will continue to support a future where data becomes a strategic asset, enabling deeper insights, faster decision-making, and enhanced collaboration across the organisation.