A Token-Passing-Based Trigger-Prediction Methodology for Event-Driven ToF Sensors

This paper presents an ambient light robust methodology for event-driven (ED) time-of-flight (ToF) sensors, mainly targeting 3D detection under outdoor scenarios. Different from prior ED methodologies, we propose a trigger-prediction scheme with token-passing algorithm to effectively filter out ambient photons for improving the system performance under strong background light. Specifically, we determine the variation in distance by detecting the change in the width of timing windows in the trigger stage, while extracting the alteration of the number of photons in the multi-step prediction stage. This can lower the error rate while ensuring a fast imaging speed. Using a 180 nm CMOS process, we developed a behavior model including the device characteristics and environmental parameters for performance evaluation. Monte Carlo simulation results demonstrate that with a 10 m range, our proposed method can achieve a depth accuracy of less than 5 cm under 10 klux, and less than 25 cm under 80 klux, respectively, and an event response time of less than 384 µs under dynamic scenes.