Event-Based Sensor Photocurrent Transfer for Low-Light Interaction
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Solution Overview
Problem
Current human-computer interaction sensors face challenges in achieving fast response speed while minimizing power consumption and reducing noise-induced false events, particularly in low-light conditions.
Innovation Solution
An event-based sensor system comprising a pixel array and a controller that generates and transfers photocurrents between pixels based on noise levels, allowing for efficient activation signal generation and reduced false events through binning and subsampling modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the sensor operates continuously to achieve fast response speed, then response speed is improved, but power consumption increases
Solution Approach 1:
The sensor employs event-driven periodic sampling where pixels are activated only when changes exceed a threshold, rather than continuous operation. This allows the system to maintain fast response capability when needed while entering low-power states during stable conditions, resolving the contradiction between response speed and power consumption.
Solution Approach 2:
The sensor dynamically adjusts its operating mode based on scene activity. When motion or light changes are detected, the sensor transitions to high-speed event mode; when the scene is static, it enters subsampling or sleep mode. This dynamic adaptation allows the system to optimize the trade-off between response speed and power consumption in real-time.
2Measurement precision
If the sensor increases sensitivity to detect subtle changes, then measurement precision is improved, but noise level increases causing false events
Solution Approach 1:
The sensor merges signals from multiple pixels through binning, where adjacent pixels combine their photocurrents before threshold comparison. This merging increases the effective signal strength for detection while the combined signal averages out random noise, improving sensitivity without proportionally increasing false event rates.
Solution Approach 2:
The sensor dynamically adjusts the threshold parameter based on local noise characteristics and lighting conditions. By adapting the threshold to match actual noise levels, the system maintains high sensitivity for detecting genuine changes while filtering out noise-induced false events, resolving the contradiction between precision and noise.
3Reliability
If the sensor uses binning to reduce noise, then reliability is improved, but device complexity increases
Solution Approach 1:
The sensor implements binning by segmenting the pixel array into groups that share common readout circuits and threshold comparison units. Each bin processes signals independently, allowing noise reduction through combination while maintaining modular architecture that limits the increase in overall device complexity.
Solution Approach 2:
The sensor design uses universal circuit blocks that can function both as individual pixel circuits and as combined bin circuits. The same hardware infrastructure supports both single-pixel and multi-pixel binning operations, reducing the complexity increase that would otherwise result from adding dedicated binning circuitry.
4Use of energy by moving object
If the sensor operates in subsampling mode to reduce power consumption, then power consumption is reduced, but response speed decreases
Solution Approach 1:
The sensor dynamically switches between subsampling mode and full-event mode based on detected activity levels. During low-activity periods, subsampling reduces power consumption; when motion or significant changes are detected, the system transitions to full-event mode to ensure fast response, thus resolving the contradiction between power savings and response speed.
Solution Approach 2:
In subsampling mode, the sensor performs periodic sampling at reduced frequency rather than continuous full-rate sampling. This periodic operation significantly reduces power consumption while still capturing essential events, and the system can increase sampling frequency when needed to maintain response capability.
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
The system operates with low power consumption, high speed, and reduced false events, enabling effective human-computer interaction even in low-light environments by selectively transferring photocurrents and adjusting thresholds for accurate signal output.
Implementation Method 1
a photodiode configured to generate a photocurrent based on incident light
Data Source
AI summary
An event-based sensor includes: a pixel array configured to output activation signals in response to an input to the pixel array; and a controller configured to output a control signal for supplying a first photocurrent generated in a first pixel of the pixel array to a second pixel of the pixel array.