Fingerprint Sensor Calibration Using Multi-Brightness Offset Data
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Solution Overview
Problem
Fingerprint sensors in display devices face challenges in accurately calibrating images due to noise and errors caused by deviations in the optical path and photosensor alignment, leading to suboptimal fingerprint recognition.
Innovation Solution
A calibration method that generates calibration data through white and dark calibration operations, applying offsets to photosensors using a skin color reflector and a black reflector, and includes a noise checking process using a reflector with irregularities to ensure accurate calibration data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration is performed without multiple brightness levels, then the calibration process is simple, but the calibration precision is insufficient due to noise and optical path deviations
Solution Approach 1:
The calibration process is segmented into multiple brightness levels (first, second, and third brightness values) and multiple calibration operations. Each operation calibrates at a specific brightness level, and the results are combined to achieve comprehensive calibration across the full brightness range, improving precision while managing complexity through systematic division
Solution Approach 2:
The patent performs more calibration operations than conventional methods by conducting multiple calibration operations at each brightness level. This excessive action ensures that noise and optical path deviations are compensated through redundant measurements and averaging, thereby improving calibration precision
2Manufacturing precision
If single brightness level calibration is used, then the calibration time is short, but the fingerprint recognition accuracy is reduced due to noise and errors
Solution Approach 1:
The calibration process maintains continuous useful action by performing multiple calibration operations without interruption across different brightness levels. Each operation contributes to the final calibration data, ensuring that the cumulative effect of all operations maximizes fingerprint recognition accuracy while optimizing the use of calibration time
3Reliability
If no noise checking process is implemented, then the calibration process is faster, but the calibration data contains noise and errors from optical path deviations
Solution Approach 1:
The noise checking process implements feedback by evaluating the quality of calibration data obtained from each calibration operation. The system checks whether the calibration data meets predetermined quality criteria, and only accepts data that passes the noise check, ensuring high reliability while maintaining efficiency through selective acceptance of valid data
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
Improves the precision of fingerprint sensor calibration by reducing noise and errors, enhancing the accuracy of fingerprint recognition and maintaining optimal brightness levels.
Implementation Method 1
a first reflector having a flat reflective surface on the first surface of the substrate so that the first reflector overlaps the sensor layer in a thickness direction. The first reflector may comprise a skin color reflector and a black reflector, and the white calibration may be performed using the skin color reflector, and the dark calibration may be performed using the black reflector.
Implementation Method 2
a light-emitting element layer located on the light-blocking layer and having a plurality of light-emitting elements
Implementation Method 3
A photo-sensing type of fingerprint sensor may comprise a light source and a photosensor. The photosensor may receive reflected light generated by a user's fingerprint, and a fingerprint detector may detect the fingerprint by generating and processing an original image based on the reflected light.
Data Source
AI summary
Provided herein are a calibration method for a fingerprint sensor and a display device using the calibration method, where, in the calibration method for a fingerprint sensor, the fingerprint sensor includes a substrate, a light-blocking layer located on a first surface of the substrate and having openings formed in a light-blocking mask, a light-emitting element layer located on the light-blocking layer and having a plurality of light-emitting elements, and a sensor layer located on a second surface of the substrate and having a plurality of photosensors; and the calibration method includes generating calibration data through white calibration and dark calibration, and applying offsets to the plurality of photosensors using the calibration data.