Hamamatsu Photonics unveils microscope for AI chip failure analysis

Hamamatsu Photonics is streamlining the hunt for defects in increasingly complex AI chips with the iPHEMOS-DDX, a new inverted emission microscope. The system consolidates seven distinct analysis techniques, PEM, Thermal/LIT, LSM, OBIRCH, DALS, EOP/EOFM and TD Imaging, into one platform, aiming to accelerate failure analysis workflows. Hamamatsu designed the iPHEMOS-DDX for flexible integration, enabling direct docking to existing testers from three directions. The company will publicly debut the iPHEMOS-DDX at ISTFA 2026, held in San Antonio, Texas, from October 4 through October 8.

iPHEMOS-DDX Microscope Targets AI Chiplet & 3D Package Failure

The iPHEMOS-DDX microscope supports direct docking to existing testers via connections in three directions, a feature designed to minimize disruption during integration into current workflows. This flexibility addresses a key challenge in advanced semiconductor failure analysis, where rapid setup and compatibility with existing equipment are paramount. Hamamatsu Photonics developed the system to analyze increasingly complex packages, including AI chiplets and High Bandwidth Memory, where identifying failure locations is becoming significantly more difficult.

According to Hamamatsu, this integration is a direct response to the demands of generative AI’s growth, which is driving both performance gains and structural complexity in AI chips. In 2024, Hamamatsu acquired NKT Photonics, renaming it Hamamatsu Photonics A/S in June 2026 to serve as its laser and fiber business unit. NEDO selected Hamamatsu in 2025, awarding approximately 3 billion yen over three years to Hamamatsu, AIST and RIKEN for ultra-fast camera and spatial light modulator development for neutral-atom quantum computers.

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With a keen intuition for emerging technologies, The Neuron brings over 5 years of deep expertise to the AI conversation. Coming from roots in software engineering, they've witnessed firsthand the transformation from traditional computing paradigms to today's ML-powered landscape. Their hands-on experience implementing neural networks and deep learning systems for Fortune 500 companies has provided unique insights that few tech writers possess. From developing recommendation engines that drive billions in revenue to optimizing computer vision systems for manufacturing giants, The Neuron doesn't just write about machine learning—they've shaped its real-world applications across industries. Having built real systems that are used across the globe by millions of users, that deep technological bases helps me write about the technologies of the future and current. Whether that is AI or Quantum Computing.

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