Abstract
This article presents multi-touch readout IC embedding finger-resistance extraction (FRE) on a capacitive touch screen panel (TSP). The proposed FRE mode is aimed to have the ability to tell users apart while sensing touch input by utilizing a unique finger’s resistance due to different bio-electrical properties. The resonance-driven FRE technique and a clamped zoom-in integrator are exploited to obtain a wide dynamic range (DR). A switched-capacitor current-controlled oscillator-based 14-bit analog-to-digital converter (ADC) was also designed, which has the benefits of low noise, compact size, and high flexibility of its resolution. The prototype chip fabricated in 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS achieved the measured signal-to-noise ratio (SNR) of 37.5 dB and DR of 50.7 dB in touch sensing and FRE modes, respectively, in a real-6.7-in capacitive TSP. By applying support vector machine learning to the FRE data, five different user-fingers were successfully classified with 97.7% accuracy after 500 learning cycles and thus demonstrating the feasibility of reliable user-differentiation on multi-user collaborative touch interfaces.
Published Version
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