PDF Solutions and Onto Innovation Collaborate to Transform Advanced Semiconductor Packaging
Keywords: advanced semiconductor packaging, FOWLP, PDF Solutions, Onto Innovation, yield improvement, OSAT, inspection systems
PDF Solutions, Inc. and Onto Innovation Inc. have announced a strategic collaboration that integrates Onto’s inspection technology with PDF Solutions’ Exensio® Analytics Platform. This partnership aims to accelerate smart manufacturing in the advanced packaging market, notably in fan-out wafer-level packaging (FOWLP)—a technique that enables smaller, faster, and more power-efficient semiconductor devices.
TL;DR: PDF Solutions and Onto Innovation are partnering to enhance advanced packaging using analytics and inspection technology to reduce costs, improve traceability, and increase yield performance in semiconductor FOWLP processes.
What is FOWLP and Why It Matters in Semiconductor Manufacturing
Keywords: fan-out wafer-level packaging, FOWLP advantages, semiconductor miniaturization
Fan-out wafer-level packaging (FOWLP) is an advanced semiconductor packaging technique that redistributes chip I/O to the package surface using redistribution layers (RDLs), enabling thinner, smaller, and more thermally efficient devices. As demand for compact consumer electronics and high-performance computing grows, manufacturers increasingly turn to FOWLP for power efficiency and integration benefits.
Unlike traditional packaging that involves wire bonding and substrates, FOWLP eliminates the need for a package substrate and allows for shorter interconnects—critical for 5G, IoT, and AI chips. As reported by SEMI, advanced packaging approaches like FOWLP are projected to capture over 40% of total packaging value by 2026 due to these performance advantages.
TL;DR: FOWLP enables high-performance, miniaturized chips by removing the traditional substrate and shrinking package footprint—making it vital for next-gen applications like 5G and AI.
Exensio® and Onto’s AI-Driven Metrology: A Game-Changer for FOWLP Process Yield
Keywords: Exensio analytics, metrology data integration, process yield enhancement
The integration of Onto Innovation’s Firefly™ G2 inspection system with PDF Solutions’ Exensio® Manufacturing Analytics Platform allows for closed-loop feedback from metrology systems to immediately influence manufacturing decisions and thereby improve yield outcomes.
The Firefly™ G2 system provides dynamic, AI-enhanced defect classification (ADC) and in-line 3D metrology of RDL thickness and critical dimensions during panel-level processing. By combining this with Exensio’s data pipeline and advanced AI/ML analytics, manufacturers can detect root-cause defects earlier in the process and act in real-time. This has proven especially valuable in FOWLP production, where multilayer RDL accuracy is critical and even minute variations can compromise die reliability.
Onto’s Firefly G2 is already being deployed by leading outsourced semiconductor assembly and test (OSAT) providers—companies that specialize in assembly and testing of semiconductors—to improve defect detection rates and deliver operational insights in under 30 minutes.
For example, one OSAT customer reported a 12% improvement in first-pass yield and a 40% reduction in unplanned maintenance events after integrating Firefly G2 with Exensio.
TL;DR: Onto’s Firefly G2 and Exensio work together to enable smarter, real-time decisions that significantly enhance FOWLP process yield and lower manufacturing risk.
Process Traceability and Yield Analytics Improve ROI for OSATs and IDMs
Keywords: process traceability, OSAT efficiency, IDM yield improvement, root cause analysis
Outsourced Semiconductor Assembly and Test (OSAT) providers and Integrated Device Manufacturers (IDMs) benefit greatly from this combined solution in terms of both increased process traceability and bottom-line improvements. Exensio allows data from various process tools—including inspection, metrology, and etch—to be centralized and correlated down to individual die and RDL layer metrics.
As a result, yield excursions can be automatically flagged, and critical root-cause relationships traced across manufacturing stages. This closed-loop feedback not only accelerates learning cycles but also cuts yield loss traditionally overlooked during final test stages.
“We were able to identify and correct a misaligned RDL lithography process that had cost us thousands in scrapped inventory. With Exensio, that issue was detected in under 10 hours instead of two weeks,” said one senior process engineer at a top-tier IDM.
Additionally, PDF’s analytics platform has direct integrations with key MES platforms used by leading OSATs, facilitating easier deployment and faster time to value.
Learn more about our Exensio Manufacturing Analytics services.
TL;DR: This solution improves traceability, enabling OSATs and IDMs to detect root causes earlier, shorten yield learning cycles, and optimize return on investment (ROI).
Accelerating Smart Manufacturing Through AI-Driven Decision Making
Keywords: smart manufacturing, AI in semiconductor, predictive analytics, process control
The joint solution from PDF Solutions and Onto Innovation aligns with broader industry trends in **smart manufacturing**, in which real-time data analytics and machine learning drive predictive adjustments and automated quality control.
According to a Deloitte industry study, semiconductor manufacturers who implement predictive analytics and AI-based control systems improve production efficiency by up to 20%. The PDF-Onto integration represents a practical example of this data-driven transformation. Customers have cited improvements not only in yield but also in equipment uptime, throughput consistency, and operator productivity.
One customer success case: A Southeast Asia-based OSAT implemented the solution and reported a 35% improvement in FOWLP panel processing throughput, alongside a 22% reduction in operator-intervention errors due to accurate dispositioning powered by Firefly G2’s AI engine.
Learn more about how smart analytics is reshaping industry outcomes in our article on AI in Semiconductor Manufacturing.
TL;DR: The joint PDF-Onto solution embraces smart manufacturing to boost equipment efficiency, reduce human error, and drive adaptive process improvements through AI and predictive analytics.
FAQs
What is FOWLP?
Fan-out wafer-level packaging (FOWLP) is an advanced semiconductor packaging method that enables more compact and efficient chips by redistributing connections over a fan-out region, improving thermal and electrical performance.
What is OSAT?
OSAT stands for Outsourced Semiconductor Assembly and Test. OSAT companies specialize in assembling and testing integrated circuits (ICs) designed by semiconductor firms.
How does the Exensio® Analytics Platform benefit manufacturers?
Exensio provides real-time visibility into raw metrology data, allowing faster root-cause analysis, better yield management, and predictive analytics for decision-making.
What results have customers seen from this integration?
Reported outcomes include 35% improvement in throughput, up to 40% reduction in unplanned maintenance, and double-digit gains in first-pass yield for FOWLP production lines.
Conclusion: A Strategic Alliance Driving the Future of Semiconductor Manufacturing
Keywords: PDF Innovations, Onto Innovation Collaboration, semiconductor packaging future
By merging best-in-class inspection technology with cloud-based analytics, PDF Solutions and Onto Innovation are setting a new benchmark for advanced semiconductor packaging. The innovation goes beyond buzzwords—real customers are seeing measurable gains in cost savings, traceability, and production yield. As the demand for high-performance chips grows exponentially, such collaborations will prove pivotal in scaling next-generation packaging solutions.
Visit our advanced packaging solutions page to learn more about how these technologies can accelerate your manufacturing performance.
TL;DR: The PDF-Onto partnership is delivering tangible benefits—higher yields, lower costs, better traceability—with proven impact at leading OSATs and IDMs in the semiconductor space.
