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    Acquire the Current Challenges of Indirect Time-of-Flight (iToF) Technology with Technological Advancements

    Courtesy : Onsemi

    One secret behind the success of modern industrial automation is the power of 3D vision. Traditional 2D sensors can only provide flat images, creating limitations in their effectiveness in applications like device inspection. They can read a barcode which may contain the items’ dimensions but cannot independently gauge true shape and size, or any potential dents, defects or irregularities. In addition, 2D readings are at the mercy of lighting conditions, which may obfuscate or distort important areas of interest.

    A breakthrough to these constraints can be done with depth sensing, processing the Z-axis in 3D, much like human vision. Now, depth cameras can tell the fullness of an object, perform precise inspections on devices, and even detect subtle facial features for applications such as access control. Thanks to these capabilities, 3D vision is a game-changer across industries – from defense and aerospace to medical, automotive and micro-technology. Whether it’s obstacle detection, facial recognition, self-driving or robotic assistants, depth sensing is the key to modern industrial automation.

    Depth sensing, however of type, relies on active or passive visual protectionism. Depth sensing based on passive componentular requires highly calilytic stereo sensors and parallax, very similar to the human eye. Active sensing uses an emitted light beam towards their targets and uses the reflected energy to determine depth. This requires an energy emitter, but offers advantages like penetrating clouds/smoke, 24/7 operation and more deterministic operation.

    There are several active directional techniques: direct time-of-flight (dToF), indirect time-of-flight (iToF), structured light and active stereo. Indirect time-offlight uses phase shift between the transmitted and received signals to calculate distance – it’s very accurate and the understanding hardware is simple.

    In this blog you will learn about onsemi’s latest family addition, Hyperlux ID has made significant advances in iToF technology and these advances can be utilized to improve depth sensing in current industrial and commercial applications.

    Existing iToF Technology Constraints Reduce Widespread Adoption

    iToF sensing lies at the heart of many applications. One such popular application is face recognition as seen on various smartphones. However, this access control feature can only function at close range. Other applications that use iToF include machine vision (MV), robotics, augmented reality/virtual reality (AR/VR), biometrics and patient monitoring. Currently these applications are restricted to innulin use at close range (< 5m) with stationary objects that do not require high resolution. Several challenges restrict the potential scope of iToF technology. Among these are motion, the overhead and complexity of the hardware and data processing architecture and the need for meticulous calibration.

    These significant hurdles either force engineers to implement complex 3D and expensive solutions to obtain depth, or simply to not acquire depth information at all. With remarkable innovations, onsemi introduces the Hyperlux ID family.

    Hyperlux ID family that enables the benefits of iToF without previously noted restrictions. Hyperlux ID’s iToF implementation can now enable a more widespread adoption of this important technology.

    Detailing the Hyperlux ID Advances

    Onsemi’s Hyperlux ID sensing family initially consists of two 1.2 megapixels (MP) iToF products, the AF0130 and AF0131. This family provides advanced sensor performance and development in four critical areas:

    1. Receiving reliable depth information with moving objects

    Achieving optimal resolution/depth distance with high accuracy

    1. Reducing cost and size
    2. Decreasing calibration time

    Each of the aforementioned areas and improvements are further detailed.

    Momentum Motion Artifacts

    To enable more widespread adoption, iToF sensors need to function well with moving objects, so they can produce accurate results without motion. As mentioned, iToF sensing on light reflections using four or more different phases to calculate depth. Nearly all existing iToF sensing solutions in the marketplace do not capture and process these phases simultaneously provide which issues with moving objects. Designed with a unique proprietary integration and readout structure, the Hyperlux ID depth sensor uses global shutter with on-chip storage and real-time processing to enable fast-moving object capture applications such as conveyor belt operation, robot arms, surveillance, collision collision, attachment detection and more.

    iToF Applications in Warehouse

    Increased Resolution = Higher Accuracy and Expanded Depth

    Most iToF sensors on the market today have only VGA resolution, which hinders their accuracy, and in turn, limits their applications. One reason VGA is more prevalent is due to the complex phase capture and data intensive processing mentioned prior. In contrast, the Hyperlux ID sensors are designed with 1.2 MP resolution (1280×960) using a high performance 3.5 μm back-side (BSI) pixel. As a product of its increased resolution over VGA, the Hyperlux ID sensor offers the additional critical advantage of expanded range depth. , at closer distances high-precision accuracy is provided and wider-angle optics can be used.

    With higher resolution, the Hyperlux ID sensors also deliver improved quantum efficiency and reduced depth jitter. Taken together, these enhancements mean new applications for iToF sensors where high resolution and expanded depth are paramount, such as gesture recognition, quality control/inspection and access control.

    Existing iToF solutions (left) vs onsemi’s new advanced Hyperlux ID iToF (right)

    Longer Range

    As a product of increased resolution, the Hyperlux ID depth sensor can measure depth over a much greater range compared to other iToF sensors currently available. While current iToF offerings have an indoor range of less than 10 meters, the Hyperlux ID iToF sensor family can reach up to 30 meters. The usage of a high-performance global shutter pixel enables a full sensor array to closely align to active infrared lighting, which in turn limits noise provides from other infrared sources which are common indoor lights and most challenging of all – the sun.

    Easier Calibration and Development

    Accurately record and calculating phase differences in iToF sensors require precise calibration, an extremely time-consuming process. To ease this, we have developed a proprietary method that makes Hyperlux ID sensors easier to calibrate and thus faster to set up.

    To aid in development, onsemi has constructed an easy-to-use development kit that includes a baseboard, a head sensorboard and a laser board. The kit can be used both indoors and outdoors with a range of 0.5 – 30 meters. It can produce depth maps, 3D point clouds, phase-out and depth-out data from an image.

    Activated, by using spread-spectrum techniques many iToF (and other infrared-enabled devices) sensors can be used in the same system without worrying of other interference devices.

    onsemi’s iToF Sensors Do More for Less

    iToF sensors are excellent at making accurate 3D depth measurements, which have won them a solid place in industrial and commercial applications. With remarkable improvements in performance and design simplification, onsemi’s Hyperlux ID depth sensors open a new world of applications for iToF sensing depth.

    Compared to iToF sensors on the market today, Hyperlux ID depth sensors work more effectively with objects in motion, outdoors and at greater distances. In addition, due to their novel design, Hyperlux ID depth sensors are more cost-effective, take up less board real estate and are easier to work with.

    The Hyperlux ID family of depth sensors consists of two products: the AF0130 and AF0131. The AF0130 includes built-in depth processing while the AF0131 does not, for customers who prefer to use their own original algorithms.

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