Laser Driver Reference Design with GaN HEMT for High-Resolution LiDAR
■While it has been discussed for a long time in the automotive industry, the light detection and ranging (LiDAR) function, which accurately measures the distance of objects for autonomous driving and specifies space, is rapidly expanding to other fields. It is being employed in sensors for detecting obstacles and implementing algorithms for avoiding them in applications such as robot vacuum cleaners, automated guided vehicles (AGVs). Services for understanding highway traffic volumes and promoting route avoidance have also begun, as have services for providing map data through 3D mapping. Furthermore, using real-time point cloud data obtained by LiDAR, research on object cognition and behavior estimation algorithms is progressing in conjunction with AI , and the importance and need for high-accuracy data that can be obtained with LiDAR is increasing. Further improvement of LiDAR characteristics is expected to lead to innovations that create new services, not just in the automotive market.
■Through the development of electronic devices, ROHM will contribute to improving the characteristics of LiDAR applications, to solving social problems, and to the creation of innovations that generate new services. In this white paper, we describe the laser diodes, GaN HEMT (EcoGaN™), and GaN gate drivers that greatly contribute to improving the characteristics of LiDAR applications, and report that we have started to publish a reference design that provides them as solutions.
RLD90QZW8 、 RLD90QZW3 、 RLD90QZWD 、 RLD90QZWC 、 RLD90QZWB 、 RLD90QZWJ 、 RLD90QZWA 、 GNE1040TB 、 GNE1015TB 、 GNE1008TB 、 BD2311NVX-LB 、 REFLD002-1 、 REFLD002-2 、 BD2311NVX-C |
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High-precision high-power laser diode 、 laser diode 、 Laser Driver 、 high-speed 1-channel gate driver 、 high-speed gate driver IC 、 Board 、 GaN HEMT 、 Gate driver |
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[ High-Resolution LiDAR ][ robot vacuum cleaners ][ automated guided vehicles ][ AGVs ] |
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White Paper |
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Please see the document for details |
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CAN;TO-56;DFN5060 |
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English Chinese Chinese and English Japanese |
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August 2023 |
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Rev.002 |
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65WP004E |
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1.2 MB |
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