Grant: Collaborative Research and Development Grants

LiDAR (Light Detection and Ranging), is a surveying method that measures distance to a target by illuminating the target with pulsed laser light and detects the reflected pulses with a sensor. If costs can be lowered and functionality improved, the market for LiDAR in automobiles and unmanned vehicle is expected to surpass USD$2.5 Billion by 2030. An approach to this challenge is to create "fast agile" LiDAR platforms based on photonic integrated circuits, integrated with advanced Artificial Intelligence (AI) systems. Such systems could support modules that would "attend to" objects of importance in the field of view allowing for high-resolution identification of critical items. LiDAR will not be simply a passive information gathering element but an actively controlled part of an integrated sensing aware AI platform. This grant has two overarching goals. Firstly, it proposes to generate a viable PIC prototype for the implementation of a "fast agile" LiDAR with the necessary functionality to enable an "attention module" within an AI infrastructure. Secondly, this project proposes to develop a sophisticated simulation platform for PIC based LiDAR. The proposed research will form a foundation for the complete system level design of the LiDAR PIC. The first goal will improve the functionality of the LiDAR by employing depletion-based phase shifter arrays and subwavelength grating couplers to improve beam steering range, resolution and scanning speed. The second goal will be complimentary to the first by providing analysis and optimization capabilities. This work will address the multi-physics and multi-scale issues present in LIDAR design and optimization, dealing with systems by incorporating electrical, optical, semi-conductor and thermal devices and effects. The impact of the physical layer on system design and constraints will also be addressed. The simulation work will build on the existing simulation frameworks of OptiSPICE and Atar. Experimental verification of the modeling using prototype devices are anticipated. Two prototype generations are expected by the end of the CRD program.