SPAD Proximity Sensor Histogram Segmentation for Crosstalk Reduction
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Solution Overview
Problem
Light-based proximity sensors in electronic devices face challenges in distinguishing between reflected light from external objects and crosstalk, making it difficult to accurately determine proximity due to variable cross-talk caused by internal reflections and foreign substances on the sensor's cover layer.
Innovation Solution
The use of a SPAD pixel array with split sections to generate separate histograms for near-field and far-field signals, along with variable bin sizes and integration times, allows for better differentiation of cross-talk from actual near-field signals, enabling more accurate proximity detection by weighting and combining multiple histograms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a light-based proximity sensor is used to detect external objects, then proximity detection capability is provided, but crosstalk from internal reflections makes it difficult to distinguish reflected light from external objects
Solution Approach 1:
The patent segments the detected light signals into different categories based on their time of flight characteristics. By dividing the detection process into near-field and far-field signal analysis with different histogram bin sizes, the system can distinguish between crosstalk (typically near-field) and actual external object reflections (far-field), thereby resolving the contradiction between providing proximity detection and eliminating crosstalk interference.
2Measurement precision
If variable bin sizes and integration times are used to differentiate near-field and far-field signals, then near-field target detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic processing parameters where the histogram bin size and integration time are adjusted based on the detected signal characteristics. For near-field targets, smaller bin sizes are used to capture rapid signal changes, while for far-field targets, larger bin sizes are applied. This dynamic adaptation improves detection accuracy without requiring permanently complex hardware, as the complexity is managed through flexible software-controlled processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of near-field target detection by reducing the impact of cross-talk, leading to improved proximity sensing capabilities in electronic devices.
Implementation Method 1
A light-based proximity sensor may have a light source such as an infrared light-emitting diode and may have a light detector
Implementation Method 2
When the electronic device is near an external object, the emitted light may be reflected from the object and detected by the light detector
Implementation Method 3
SPAD pixel array with split sections to generate separate histograms for near-field and far-field signals
Data Source
AI summary
An electronic device may include a proximity sensor under a cover layer. The proximity sensor may include a light-emitter, such as an infrared light source, and a light-detector, such as an array of single-photon avalanche diodes (SPADs). The SPADs may measure light that has reflected from an external object. However, some of the light may be reflected by the cover layer, creating cross-talk. To distinguish between the cross-talk and signals from the external object, processing circuitry may histogram measurements from the SPADs. In particular, the processing circuitry may histogram near-field and/or far-field measurements into different histograms. The measurements may be weighted and/or gated prior to histogramming. In this way, cross-talk may be distinguished from the near-field and far-field signals.