Mutually Coupled Time-to-Digital Converters (TDCs) for Direct Time-of-Flight (dTOF) Image Sensors

Direct time-of-flight (dTOF) image sensors require accurate and robust timing references for precise depth calculation. On-chip timing references are well-known and understood, but for imaging systems where several thousands of pixels require seamless references, area and power consumption limit the use of more traditional synthesizers, such as phase/delay-locked loops (PLLs/DLLs). Other methods, such as relative timing measurement (start/stop), require constant foreground calibration, which is not feasible for outdoor applications, where conditions of temperature, background illumination, etc. can change drastically and frequently. In this paper, a scalable reference generation and synchronization is provided, using minimum resources of area and power, while being robust to mismatches. The suitability of this approach is demonstrated through the design of an 8 × 8 time-to-digital converter (TDC) array, distributed over 1.69 mm2, fabricated using TSMC 65 nm technology (1.2 V core voltage and 4 metal layers—3 thin + 1 thick). Each TDC is based on a ring oscillator (RO) coupled to a ripple counter, occupying a very small area of 550 µm2, while consuming 500 µW of power, and has 2 µs range, 125 ps least significant bit (LSB), and 14-bit resolution. Phase and frequency locking among the ROs is achieved, while providing 18 dB phase noise improvement over an equivalent individual oscillator. The integrated root mean square (RMS) jitter is less than 9 ps, the instantaneous frequency variation is less than 0.11%, differential nonlinearity (DNL) is less than 2 LSB, and integral nonlinearity (INL) is less than 3 LSB.(OLD)Applied Quantum Architecture

Guided direct time-of-flight Lidar for self-driving vehicles

Self-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capacity for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. This research introduces a pioneering ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centres around a dToF sensor in which the exposed time widow of independent pixels can be dynamically adjusted. A pair of vision cameras are used in this demonstrator to provide the guiding depth estimates. The implemented guided dToF demonstrator successfully captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over 25 times, while the total laser cycles in each frame are reduced by at least 6 times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar