Touch Sensor Noise Prediction Using Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Noise in touch sensors caused by the driving of display panels reduces the accuracy of touch detection, as existing technologies fail to accurately predict and compensate for these noise components.
Innovation Solution
A display device and computing system that utilize an artificial neural network to predict noise in touch sensors and compensate for it by adjusting the sensed data, incorporating a display driver that generates predicted noise data using an artificial neural network and a touch controller that converts and compensates touch sensing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a touch sensor is mounted on or within a display panel, then the touch sensor can detect user input actions, but noise is generated in the touch sensor due to the driving of the display panel which reduces touch detection accuracy
Solution Approach 1:
The system performs preliminary noise prediction by generating predicted noise data corresponding to the input image data before touch detection occurs. The display driver generates this predicted noise data using an artificial neural network based on the display driving signals, allowing the noise to be compensated in advance, thereby improving touch detection accuracy while maintaining display operation.
Solution Approach 2:
An artificial neural network is introduced as an intermediary component between the display driver and touch controller. This neural network processes the relationship between display driving signals and touch sensor noise, generating predicted noise data that mediates the harmful interaction between display operation and touch sensing, enabling effective noise compensation.
2Measurement precision
If an artificial neural network is used to predict noise in the touch sensor, then touch detection accuracy is improved, but the device complexity increases
Solution Approach 1:
The artificial neural network is integrated into the existing display driver and touch controller architecture, allowing it to serve multiple functions: processing display driving signals, predicting noise based on image data, and generating compensation data. This multi-functionality approach reduces overall system complexity by consolidating noise prediction capabilities within existing components rather than adding separate dedicated hardware.
Data Source
AI summary
A display device includes a display panel, a touch sensor, a display driver and a touch controller. The display driver drives the display panel based on input image data, and generates predicted noise data corresponding to the input image data by using an artificial neural network. The touch controller receives a touch sensing signal from the touch sensor by driving the touch sensor, converts the touch sensing signal that is an analog signal into touch sensing data that are digital data, and compensates the touch sensing data based on the predicted noise data.