𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗽𝗵𝗼𝘁𝗼𝗻𝗶𝗰 𝘄𝗮𝗳𝗲𝗿 𝗶𝗻𝘀𝗽𝗲𝗰𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗵𝗶𝗴𝗵-𝗺𝗶𝘅 𝗹𝗼𝘄-𝘃𝗼𝗹𝘂𝗺𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

Noblean Rik Wetzels took the stage at the Salland Engineering (Europe) B.V. Test Technology Symposium! Rik shared how we use machine learning to effectively improve photonic wafer inspection. Making inspections faster, more reliable and requiring less manual effort.

𝗢𝘂𝗿 𝗺𝗲𝘁𝗵𝗼𝗱: 𝗔 𝟮-𝘀𝘁𝗲𝗽 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗳𝗼𝗿 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗲𝗳𝗲𝗰𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻
At the symposium Rik explained how Nobleo’s machine learning tool suite analyzes microscope images and creates detailed defect maps. The approach provides immediate results that can be fine-tuned over time to provide more and more accurate and granular classification results. A user-friendly machine learning implementation to outsmarting tomorrow in wafer inspection!

𝗞𝗲𝘆 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀:
✔️ No or minimal annotation time required. Inspecting wafers faster than ever.
✔️ Automated defect detection. Removing subjectivity from quality assessments.
✔️ Real-time data driven decision-making. Manufacturers can quickly determine wafer acceptance or rejection.
✔️ Insight across production steps. By tracking defects over multiple stages, we provide valuable feedback on process quality.
✔️ Especially valuable for high-mix, low-volume manufacturing.

𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗲𝗿𝘀?
For companies producing high-mix low-volume wafers (MEMS, Photonics, Microfluidics etc), this AI-driven approach simplifies quality control and provides a clearer picture of the production process. By identifying defects at different stages, manufacturers can address issues early, improve efficiency and minimize waste. This not only ensures higher-quality wafers but also leads to a more reliable and cost-effective production process.

📩 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘁𝗲𝘀𝘁 𝗶𝘁?
Do you have microscope images that need analysis? Let’s run them through our system.
Want your wafers scanned and analyzed? Send them over, we’ll process them on our machine.

Contact us for more information, send us a message or fill out the contact form. 

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