In the rapidly expanding field of augmented reality (AR), the precise measurement of diffractive gratings used in AR devices is crucial for optimizing their performance and user experience. Therefore, the choice of measurement technique can significantly impact the accuracy and efficiency of the process. While Scanning Electron Microscopes (SEMs) have been a popular choice, Littrow diffractometers are emerging as the superior alternative for evaluating AR diffractive gratings. In this blog post, we will delve into the reasons why Littrow diffractometers should be your go-to choice for assessing the performance of diffractive gratings in AR devices.
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Illuminating AR: Why Littrow Diffractometers Excel in Evaluating Diffractive Gratings for Augmented Reality Devices
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Machine vision-based cosmetic defect detection enhanced with AI - Make or break your used smartphones business case
State-of-the-art cosmetic defect detection with AI is a key enabler for making profitable business in the refurbishment of smartphones. It is accurate, relentless, and objective and it beats humans in repeatability.
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OptoFidelity FUSION, the All-in-one functional tester for refurbished smartphones, received its Q4/2022 SW release in December.
The new release includes:
- GPS test
- LiDAR camera test for iPhones
- Capability to export image and sound data from tests to the customer's external server for postprocessing and/or archiving
- Multiple improvements in compatibility, test sequence optimization, and safety features
Since OptoFidelity launched the FUSION robot for functional testing of smartphones we have constantly been developing it to meet the needs of our customers. Over the years FUSION has been used to test millions of smartphones with excellent repeatability.
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You may have seen our BUDDY robot at the exhibitions and in our marketing content. But what is the story behind the robot? Read about our development and what drives us further to enable a smarter future.
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When testing the Motion-to-photon (MTP) latency of a Head-mounted display (HMD), variables like CPU/GPU load and network bandwidth can affect the result. Therefore, it is quite common that users would like to utilize certain visual content with the HMD during the test, to better match the actual conditions where the device is being used. But there is a fundamental challenge with additional content as the image on the screen is the exact source for the virtual world viewer orientation and location information. And to be able to get that information from an image, certain reference content needs to be viewed.