Touch Controller Noise Magnitude Sensing via Trigonometric Multipliers
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Solution Overview
Problem
Existing mutual capacitance sensing techniques for capacitive touch screens face challenges in consistently and efficiently determining noise levels, as noise data is not always available and can vary significantly from frame to frame, necessitating improved noise sensing methods.
Innovation Solution
A touch screen controller that performs trigonometric manipulations on sampled data at multiple frequencies during noise sensing sub-frames to determine noise magnitude values, and selects the sampling frequency with the lowest standard deviation for subsequent touch data sensing, using both sine and cosine multipliers to process imaginary and real noise data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise sensing is performed by sampling row conductors without force signal at multiple frequencies, then noise magnitude can be determined, but noise data is not available every data frame and noise levels vary significantly from frame to frame
Solution Approach 1:
The patent performs noise sensing during idle periods between touch sensing operations, preparing noise magnitude data in advance for future touch sensing frames. This preliminary noise characterization allows the system to have noise data ready before it is needed, improving both availability consistency and measurement precision without interfering with touch sensing operations.
Solution Approach 2:
The system continuously performs noise sensing across multiple frames and continuously updates noise magnitude values, ensuring that noise data is always available and current. This continuous monitoring approach eliminates gaps in noise data availability and provides consistent, up-to-date noise information for ongoing touch sensing operations.
2Measurement precision
If noise sensing is performed at multiple frequencies to determine optimal sampling frequency, then touch data accuracy can be improved, but processing complexity and time increase
Solution Approach 1:
The patent samples at multiple frequencies (excessive action) to ensure accurate noise characterization, but then uses this data strategically to select only the optimal frequency for subsequent touch sensing. By performing the exhaustive multi-frequency sampling during idle periods and using the results to guide future single-frequency operations, the system achieves high accuracy without continuous time loss.
Solution Approach 2:
The system performs the computationally intensive multi-frequency noise sensing in advance during idle periods, so that when touch sensing is needed, the optimal frequency has already been determined. This preliminary frequency selection based on pre-collected noise data eliminates the need to repeat multi-frequency sampling during time-critical touch sensing operations.
3Measurement precision
If trigonometric manipulations are performed on sampled data to separate real and imaginary noise components, then noise magnitude determination accuracy improves, but device complexity increases
Solution Approach 1:
The patent replaces complex hardware-based frequency analysis with software-based trigonometric manipulations of sampled data. By using mathematical operations (sine and cosine multiplications followed by summation) to separate real and imaginary noise components, the system achieves accurate noise magnitude determination without requiring complex physical signal processing hardware, thus improving precision while managing device complexity through computational methods.
Data Source
AI summary
A touch screen controller includes drive circuitry driving force lines with a force signal in a touch data sensing mode and not driving the force lines in a noise sensing mode, sense circuitry sensing touch data at the sense lines in the touch data sensing mode and sensing noise data at the sense lines during the noise sensing mode. Processing circuitry: a) samples the noise data, b) performs trigonometric manipulations of the noise data to produce imaginary noise data and real noise data, and c) determines a noise magnitude value of the noise data as a function of the imaginary noise data and the real noise data. In the noise sensing mode, (a)-(c) are performed for each of a plurality of possible sampling frequencies to be used in the touch data sensing mode in order to determine which sampling frequency is to be used in the touch data sensing mode.


