Onsemi has unveiled its new Hyperlux ID family of indirect time-of-flight (iToF) sensors, establishing a new benchmark in depth sensing capabilities with a range of up to 30 meters – four times farther than current industry standards. The breakthrough sensor family combines high-precision depth sensing with monochrome imaging in a single device, targeting applications from facial recognition to industrial automation.
The new sensor family builds upon onsemi’s established expertise in sensing technologies, following their previous collaborations in AIoT and biometric access control. The Hyperlux ID family introduces two models: the AF0130, featuring on-chip depth processing, and the AF0131, which allows for custom depth algorithm implementation.
The sensors use onsemi’s proprietary global shutter pixel architecture and incorporate back-side illuminated (BSI) CMOS technology with 1.2-megapixel resolution and 3.5-µm pixels. This architecture enables real-time scene capture and depth processing, even in challenging lighting conditions or with fast-moving objects.
A key innovation is the dual functionality that combines monochrome imaging and depth sensing in a single sensor, eliminating the need for separate visual and spatial data sensors. This significantly reduces system complexity and costs while improving overall performance.
The technology’s applications span multiple industries, including manufacturing, where it enables precise quality control and defect detection. In robotics and automation, the sensors enhance navigation and collision avoidance capabilities. The facial recognition capabilities, meanwhile, are particularly relevant in the biometric security market, supporting applications from payment terminals to access control systems.
Both models in the family support modulation frequencies up to 200 MHz and include dual laser-driver controls with built-in eye safety features. The AF0130 model’s integrated depth processing ASIC provides immediate depth, confidence, and intensity maps, while the AF0131 offers flexibility for custom algorithm implementation.
Sources: Embedded Computing Design, Electronic Products
—
April 02, 2025 – by Ji-seo Kim
