By Rao Desineni, Senior Director of Data Analytics and Factory Automation, Intel Foundry
Qian Liu, Software Application Engineering Manager, Intel Foundry
Jonathan Byrne, Principal Engineer for AI and Automation, Intel Foundry
Today’s silicon needs to work predictably, efficiently, and reliably across demanding workloads such as artificial intelligence (AI) and high-performance computing (HPC). Interface test adapters (ITAs) are the workhorses that connect our customers’ products to commercially available automated test equipment (ATE) or proprietary systems like Intel Foundry’s High Density Modular Test (HDMT) to perform a broad suite of test and validation steps. Routine inspection of the thousands of pogo pins in an ITA is critical — pins provide precise electrical contact, enabling consistent, accurate testing for defects in products before delivery to customers. To improve the inspection of ITA pins and connectors, our engineers developed the Intelligent Robotic Inspection System (IRIS), the first of its kind AI-enabled collaborative robot for use on Intel Foundry’s production floor.
IRIS provides automated computer vision inspection in four Intel Foundry assembly test manufacturing factories worldwide, reducing manual inspection time by 30% — resulting in no ITA defect-related excursions occurring after the adoption of the system.¹ These excursions are deviations in test results caused by a failure in the ITA itself, rather than a defect in the silicon. Several factors have contributed to a longstanding industry-wide challenge with ITA variability, including increasing device complexity, shrinking geometries of advanced nodes, high pin counts, and ergonomic stress placed on technicians manually performing inspections.
Computer vision systems like IRIS can analyze pin fields for contamination, wear, bent pins, or missing parts with pinpoint accuracy to address ITA variability. This in turn leads to more reliable and consistent product quality across lots and shipments for customers. The platform not only reduces errors but also provides a fully auditable review and record of every probe card.
Figure 1. IRIS accurately identifies a sunken pin defect among more than 1,700 pins in this ITA.
How IRIS Combines Robotics and AI to Improve ITA Inspections
In partnership with an industrial automation supplier, our team engineered the IRIS cobot system for high-precision inspection of ITAs. The platform features a multi-axis articulated arm with an inspection head that moves with the fluidity and range of a human operator — yet with far greater consistency. A high-resolution industrial camera capable of full 360-degree maneuverability enables IRIS to capture rich visual data from any angle. The result is a repeatable inspection architecture engineered for real-time analysis and edge-based decision automation — providing customers with data-backed assurance that components meet specifications.
The true capability of IRIS comes from its AI-driven computer vision pipeline, supported by a full machine learning operations workflow. Deep learning models, trained on thousands of labeled examples of both nominal and defective conditions, continuously evolve using human-in-the-loop feedback. Every anomaly flagged by IRIS is reviewed by a technician and labeled as a real defect, a true but unimportant positive, or false positive. These classifications feed directly into automated retraining cycles, allowing the AI models to adapt to real production variation and maintain accuracy as ITA configurations and process conditions change. If any quality drift starts to occur, it’s caught early before it affects large batches.
Figure 2. IRIS detects a mixing pin that demonstrates extremely subtle micro variances.
One of the system’s most impactful capabilities is its high-speed pin-mixing detection — a challenging and error-prone task in manual workflows. ITAs contain hundreds of 200-micron pins arranged in dense and sometimes product-specific configurations. IRIS uses multiple object-detection networks and classical computer vision techniques to identify and localize hundreds of pins in under a second, align the detected pattern against a reference map, and identify incorrect pin types or misplaced positions. Pin-mixing soft detection prevents interface test adapter damage, creating a more stable long-term supply by reducing the risk of supply disruption.
To accelerate development, we use Intel’s Geti™ AI platform to rapidly label datasets and evaluate model architectures, then integrate the resulting deep-learning models with OpenCV for geometric correction, alignment, cropping, and counting. This hybrid approach — combining adaptive neural networks with deterministic image-processing routines — provides both accuracy and explainability, which are essential for high reliability manufacturing environments.
Together, IRIS’s robotics, AI, and image processing stack establish a scalable foundation for intelligent, automated ITA inspection and next-generation test operations. Customers can rely on faster and safer ramp of new designs while reducing early production risk and scrap.
A Unified Solution Ready for Real Life
Intel Foundry enables end-to-end data connectivity across all manufacturing test steps and die-level traceability spanning the entire product lifecycle. Intel Foundry Advanced System Assembly & Test (Intel Foundry ASAT) provides packaging and testing solutions using ATE or our HDMT, depending on customer needs. We offer manufacturing test solutions for all stages of the test process, from wafer and die sort to burn-in to final and system-level testing, reducing costs and accelerating time to market.
Future versions of IRIS are expected to detect more complex and varied defect types in even more diverse-shaped test components. By investing in ITA infrastructure, we aren’t just assuring quality for our customers. We’re enabling the next era of silicon innovation by ensuring chips are validated, characterized, and ready for real life.
To learn more about Intel Foundry’s packaging and test solutions, visit intel.com/foundry or reach out to us at foundry.contact@intel.com.
Endnotes
- Based on Intel Foundry internal analysis as of December 2025.
Notices and Disclaimers
All product and service plans, roadmaps, and performance estimates are subject to change without notice. Projections about future performance and other metrics are inherently uncertain. Performance results are based on testing as of dates shown. Your costs and results may vary.
This blog contains forward-looking statements about Intel’s future plans or expectations, including its packaging technology roadmap. These statements are based on current expectations and involve many risks and uncertainties that could cause actual results to differ materially from those expressed or implied in such statements. For more information on the factors that could cause actual results to differ materially, see our most recent earnings release and SEC filings at intc.com.
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