A Low-Computing-Complexity Touch Signal Detection Method and Analog Front-End Circuits Based on Cross-Correlation...

Area of Science:

• Electrical Engineering
• Computer Engineering
• Human-Computer Interaction

Background:

• Large touch screen panels (TSPs) face challenges with high computing complexity and excessive data processing in traditional touch signal detection.
• Inefficient sampling methods lead to processing of numerous invalid data points, particularly in larger displays.

Purpose of the Study:

• To propose a low-computing-complexity touch signal detection method for large mutual capacitance TSPs.
• To develop analog front-end (AFE) circuits utilizing cross-correlation technology for enhanced touch detection.
• To reduce the computational load and analog-to-digital converter (ADC) acquisition requirements.

Main Methods:

• Implemented a touch signal detection method based on cross-correlation technology.
• Developed a targeted signal sampling approach, focusing only on data around touch points.
• Designed dual cross-correlation AFE circuits using a 0.11 μm process.

Main Results:

• Reduced algorithm complexity from MN to M + nN (where M=RX channels, N=TX channels, n=touch points).
• Decreased maximum ADC acquisition number from MN to 18n.
• Achieved average channel area of 0.015 mm² and power consumption of 0.227 mW.
• Attained a maximum frame rate of 384.6 Hz with 10 touch points.

Conclusions:

• The proposed cross-correlation method significantly lowers computing complexity and data acquisition needs for large TSPs.
• The developed AFE circuits effectively detect weak touch signals amidst display noise.
• This approach enables high frame rates while concurrently reducing die area and power consumption.