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First-Arrival Differential Counting for SPAD Array Design
Mel White1, Tianyi Zhang1, Akshat Dave2
1Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
Sensors (Basel, Switzerland)
|December 9, 2023
Summary
This study introduces a new single-photon detector architecture. It captures relative, not absolute, scene information, enabling higher pixel density and reduced data for applications like HDR imaging and LiDAR.
Area of Science:
- Photon detection
- Image sensor technology
- Compressed sensing
Background:
- Traditional single-photon detectors require extensive circuitry for absolute measurements.
- Existing methods face limitations in pixel density and data throughput.
- High Dynamic Range (HDR) imaging and Light Detection and Ranging (LiDAR) demand efficient photon detection.
Purpose of the Study:
- To propose a novel single-photon detecting array architecture.
- To capture relative intensity or timing information instead of absolute values.
- To enable higher pixel packing factors and reduce data throughput.
Main Methods:
- Developed a novel architecture for single-photon detecting arrays.
- Focused on capturing relative information between pixels or pixel groups.
- Utilized differential measurements for inherent data compression.
- Explored physical implementations of compressed sensing (e.g., Haar wavelets).
Main Results:
- Achieved significantly higher pixel packing factors compared to per-pixel Time-to-Digital Converter (TDC) approaches.
- Demonstrated reduced data throughput due to the compressive nature of differential measurements.
- Successfully applied the technique to High Dynamic Range (HDR) imaging.
- Showcased applicability for Light Detection and Ranging (LiDAR) systems.
Conclusions:
- The novel architecture offers a more efficient approach to single-photon detection.
- Relative information capture reduces hardware complexity and data handling requirements.
- The technique shows promise for advanced imaging and sensing applications, including HDR imaging and LiDAR.
