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Aehr Test wins $6M follow-on AI processor burn-in orders

October 7, 2026 7:32 AM EDT

Aehr Test Systems (NASDAQ: AEHR) announced approximately $6 million in follow-on production orders from an unnamed hyperscale customer for package-level burn-in of a next-generation AI processor used in data center training and inference applications.

The orders include additional Sonoma high-power package-level test and burn-in systems, burn-in modules, device-specific sockets, and production software enhancements. The equipment will be deployed at one of the customer's outsourced semiconductor assembly and test partners in Taiwan, with deliveries scheduled over the next six months.

This is the second AI processor from the same customer to use Aehr's Sonoma platform for production burn-in. The orders are intended to expand capacity ahead of an expected production ramp of the second device in calendar 2027. The customer has indicated the second processor is expected to reach production volumes exceeding those of the first processor, which is already in high-volume production.

The next-generation processor requires independently controlled thermal zones within each package to achieve the operating conditions needed for production burn-in and early-life failure screening. Aehr says the Sonoma platform's multi-zone thermal control capabilities support independent temperature management across different areas of a device while delivering the high current needed to electrically stress the processor.

"These follow-on production orders demonstrate the continued expansion of our relationship with this hyperscale customer and its confidence in Sonoma to support successive generations of increasingly powerful and complex AI processors," said Gayn Erickson, President and Chief Executive Officer of Aehr Test Systems.

Aehr also said it is in discussions with the customer regarding reliability qualification and production burn-in requirements for future AI device generations, covering both its Sonoma package-level and FOX-XP wafer-level burn-in platforms.



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