The semiconductor industry stands at a critical inflection point. As AI-related devices are projected to represent 71% of total semiconductor revenue by 2030, the industry faces unprecedented challenges that demand innovative solutions. At the recent IMAPS CHIPcon conference, PDF Solutions’ Executive Vice President Dr. Kimon Michaels outlined how AI-powered testing platforms are addressing these challenges head-on.
The Perfect Storm: Three Converging Semiconductor Industry Transformations
The semiconductor industry is simultaneously navigating three major transformations that are reshaping how chips are designed, manufactured, and tested:
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Innovation in 3D Architecture
The move toward chiplets and complex packaging systems has fundamentally altered the semiconductor landscape. Traditional single-die approaches are giving way to sophisticated multi-die configurations that can include 4-6 dies and 2-20+ High Bandwidth Memory (HBM) components, each requiring 10-20+ test insertions throughout the manufacturing process.
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Complex Global Supply Chain
Today’s semiconductor manufacturing spans multiple countries and specialized companies, creating a globally distributed ecosystem that demands unprecedented coordination. The challenge isn’t just managing complexity—it’s doing so while maintaining quality, traceability, and efficiency across diverse manufacturing environments.
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AI for AI
Perhaps most intriguingly, the industry is leveraging artificial intelligence to manufacture the very chips that will power the next generation of AI applications. This creates opportunities for efficiency gains at every level, from design optimization to manufacturing process control.
The Testing Challenge: Where Complexity Meets Reality
These three transformations converge most dramatically in the testing phase. Modern semiconductor testing must handle:
- Advanced packaging data structures that span multiple dies and complex interconnects
- Die traceability across globally distributed supply chains requiring SEMI E142 compliance
- Massive data volumes from multiple test insertions at different manufacturing stages
- Real-time decision making for adaptive test strategies
The traditional approach of isolated testing at each manufacturing stage is no longer sufficient. What’s needed is a unified platform that can orchestrate testing across the entire supply chain.
The Four Pillars of AI-Powered Test Orchestration in semiconductor industry
To address these challenges, PDF Solutions has identified four critical capabilities that any comprehensive solution must provide:
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Integration
The platform must seamlessly connect multi-factory Manufacturing Execution Systems (MES), Product Lifecycle Management (PLM) systems, and Enterprise Resource Planning (ERP) applications. This integration enables real-time visibility and coordination across the entire manufacturing ecosystem.
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Operationalization
Beyond simple data collection, the platform must enable test process control, rules development and deployment, adaptive testing, and edge execution. This means AI models can make real-time decisions about which tests to run and which to skip based on upstream data.
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Orchestration
AI models themselves require lifecycle management: creation, training, deployment, monitoring, and retraining. The platform must orchestrate these processes across multiple facilities and test stages, ensuring models remain accurate and effective as manufacturing conditions change.
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Secure Connectivity
Given the global nature of semiconductor manufacturing and the sensitivity of the data involved, the platform must provide secure remote access control and monitoring of factory and test equipment while protecting intellectual property.
Real-World Impact: Predictive Testing in Action
The power of this approach becomes clear when examining real-world applications. In one case study, PDF Solutions demonstrated how machine learning models trained on wafer sort parametric data could predict final test failures with remarkable accuracy.
The results were compelling:
- 5 out of 7 tests could be completely skipped with zero impact on Defective Parts Per Million (DPPM)
- The remaining 2 tests showed near-zero DPPM impact
- Skip rates exceeded 88% for all tests, meaning the vast majority of units could bypass unnecessary testing
This isn’t just about reducing test time, it’s about fundamentally reimagining how testing fits into the manufacturing flow. By using Data Feed Forward (DFF) techniques, information from earlier manufacturing stages can inform testing decisions at later stages, creating a more intelligent and efficient process.
The Infrastructure Behind the Intelligence
Implementing AI-powered testing requires sophisticated infrastructure that can handle the scale and complexity of modern semiconductor manufacturing. Key components include:
Edge Computing: AI models must run in real-time at the manufacturing edge, making decisions about individual parts as they move through the production line.
Secure Data Infrastructure: The platform must handle massive volumes of sensitive manufacturing data while maintaining security and intellectual property protection.
Model Management: From development through deployment to monitoring and retraining, AI models require comprehensive lifecycle management across multiple facilities.
Autonomous Operation: The system must operate with minimal human intervention, automatically adapting to changing conditions and continuously improving performance.
Looking Forward: AI as a Continuum
The key insight from PDF Solutions’ presentation is that AI for semiconductor testing isn’t a single technology or solution, it’s a continuum of data, models, and infrastructure working together across the entire product lifecycle.
This approach recognizes that semiconductor manufacturing is inherently a data-rich environment where decisions made at one stage impact all subsequent stages. By creating a connected view of the globally distributed supply chain, manufacturers can optimize not just individual processes, but the entire system.
The Bottom Line
As the semiconductor industry continues to evolve, the companies that succeed will be those that can effectively harness AI to manage complexity, improve efficiency, and maintain quality across increasingly sophisticated manufacturing processes. AI-powered testing platforms represent a crucial step toward this future—one where intelligence is embedded throughout the manufacturing process, creating more resilient, efficient, and responsive supply chains.
The transformation is already underway. The question isn’t whether AI will revolutionize semiconductor testing, but how quickly the industry can adapt to take advantage of these new capabilities. For manufacturers ready to embrace this change, the potential benefits are substantial: reduced costs, improved quality, faster time-to-market, and the ability to navigate an increasingly complex global supply chain with confidence.
This blog post is based on the presentation “AI-Powered Testing in Semiconductor Manufacturing: A New Supply Chain Orchestration Platform” delivered by Dr. Kimon Michaels at IMAPS CHIPcon 2025.