Conditional-Reset Multi-Bit Image Sensor Readout
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image sensors face challenges in efficiently converting pixel signals into digital data while minimizing noise and power consumption, particularly in low-light conditions, due to limitations in sampling thresholds and reset mechanisms.
Innovation Solution
The implementation of a multi-bit sampling architecture with conditional reset and correlated double sampling techniques, which allows for non-destructive sampling and noise reduction by selectively resetting pixel signals only when they exceed a sampling threshold, and using progressive read-out and inter-frame integration methods to enhance signal-to-noise ratio in low-light environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel signals are reset after every sampling operation, then the dynamic range is improved, but noise is amplified and power consumption increases
Solution Approach 1:
The patent changes the reset parameter from a fixed post-sampling operation to a conditional operation based on signal threshold comparison. The read circuit compares the pixel signal against a threshold and only resets when the signal exceeds the threshold, thereby changing the reset behavior parameter dynamically based on signal conditions.
Solution Approach 2:
The patent implements feedback by having the read circuit continuously monitor pixel signals and provide conditional reset feedback based on threshold comparison. The system uses the sampled signal value to determine whether a reset operation should occur, creating a closed-loop control mechanism that adapts reset behavior to actual signal conditions.
2Measurement precision
If pixel signals are reset after every sampling operation, then the dynamic range is improved, but power consumption increases
Solution Approach 1:
The patent changes the reset operation parameter from unconditional to conditional based on threshold comparison. By modifying the reset trigger parameter to depend on signal magnitude, the system reduces unnecessary reset operations and associated power consumption while preserving dynamic range performance.
Solution Approach 2:
The patent applies partial action by performing reset operations only when necessary (when signals exceed the threshold) rather than universally after every sample. This selective approach reduces the frequency of power-consuming reset operations while maintaining adequate dynamic range for the majority of signal conditions.
3Productivity
If sampling threshold is lowered to capture more signals, then more pixels are captured, but noise increases and power consumption increases
Solution Approach 1:
The patent changes the sampling parameter from a fixed low threshold to a dynamic threshold based on signal comparison. By adjusting the effective sampling threshold to match actual signal levels, the system captures more valid signals while filtering out noise that would be captured by a uniformly low threshold.
4Device complexity
If conventional sampling architecture is used, then device complexity is low, but signal-to-noise ratio is poor in low-light conditions
Solution Approach 1:
The patent implements self-service by enabling the read circuit to autonomously perform threshold comparison and conditional reset decisions without external intervention. The read circuit serves multiple functions (sampling, comparison, conditional resetting) internally, reducing the need for additional complex external circuitry while improving signal-to-noise ratio through intelligent signal 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 improves the signal-to-noise ratio and dynamic range of image sensors, particularly in low-light conditions, by reducing noise and power consumption through selective pixel reset and inter-frame integration, resulting in higher-quality image capture.
Implementation Method 1
each configured to convert photons incident upon the photosensors ('captured light') into electric charge
Data Source
AI summary
An image sensor architecture with multi-bit sampling is implemented within an image sensor system. A pixel signal produced in response to light incident upon a photosensitive element is converted to a multiple-bit digital value representative of the pixel signal. If the pixel signal exceeds a sampling threshold, the photosensitive element is reset. During an image capture period, digital values associated with pixel signals that exceed a sampling threshold are accumulated into image data.