Capacitive Measurement Correction for Non-Regular Electrodes
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Solution Overview
Problem
Existing capacitive touch-sensitive interfaces face issues with leakage capacitance drift over time, environmental variations, and positional ambiguity, particularly when electrodes are non-regularly shaped due to connection tracks, limiting their sensitivity and accuracy in detecting objects in 3D space.
Innovation Solution
A method using a multi-variable nonlinear prediction model to correct absolute capacitance measurements by transforming actual values into probability densities, compensating for defects in electrode geometry, such as non-regular shapes caused by connection tracks, allowing for accurate detection of objects in 3D space without interference from connection tracks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If electrodes are made non-regular in shape to accommodate connection tracks, then manufacturing cost is reduced and transparency is improved, but measurement precision deteriorates due to positional ambiguity and ghosting
Solution Approach 1:
The patent creates a virtual regular electrode grid that copies the ideal measurement geometry, then uses signal processing to map the actual non-regular electrode measurements onto this virtual grid. This allows the physical electrodes to be non-regular (reducing manufacturing cost and improving transparency) while maintaining measurement precision through the virtual regular grid model.
Solution Approach 2:
The patent transforms the measurement parameters by converting capacitance values from non-regular electrodes into probability density distributions on a regular grid. This parameter transformation resolves the positional ambiguity by redistributing the measurement information across multiple grid points, eliminating ghosting while maintaining the benefits of non-regular physical electrode shapes.
2Volume of moving object
If absolute capacitance measurement is used to detect objects in 3D space, then detection range is improved, but measurement precision deteriorates due to positional ambiguity of multiple objects
Solution Approach 1:
The patent segments the capacitance measurement signal into spatial probability density distributions across the electrode grid. By dividing the measurement into multiple probability components corresponding to different spatial locations, the system can resolve multiple objects in 3D space without positional ambiguity, even when they are at different distances from the surface.
Solution Approach 2:
The patent adds a probability density dimension to the measurement, transforming scalar capacitance values into spatial probability distributions. This dimensional transformation allows the system to distinguish between multiple objects at different positions and distances, resolving the positional ambiguity that plagues traditional absolute capacitance measurement while maintaining extended detection range.
3Device complexity
If connection tracks are integrated with electrodes on the same layer, then device complexity is reduced, but measurement precision deteriorates due to interference from connection tracks
Solution Approach 1:
The patent extracts the connection track interference from the measurement signal by creating a virtual electrode model that excludes the connection track regions. The signal processing separates the useful electrode capacitance information from the parasitic connection track capacitance, allowing simple integrated construction without compromising measurement precision.
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 method enhances the accuracy and reliability of detecting objects in 3D space by eliminating the influence of connection tracks and improving sensitivity, enabling the design of electrodes on the same layer with reduced manufacturing costs and increased transparency.
Implementation Method 1
measure the variation of the capacitances appearing between electrodes and the object to be detected
Implementation Method 2
The electric field generated between the rows and the columns remains especially concentrated around the surface
Data Source
AI summary
This relates to correcting the actual absolute capacitance values of electrodes of non-optimized geometric shapes that are connected to electronic circuits. To correct the actual absolute capacitance values, a prediction model can first be determined by carrying out nonlinear regression on the basis of actual values of absolute capacitance values from the electrodes and from a probability density image from idealized electrodes for a plurality of object positions. The prediction model can then be applied to the actual absolute capacitance values to obtain a probabilities densities image for the actual absolute capacitance values, considered to be corrected absolute capacitance values used for the detection of the object.