Large-scale capacitive touch panels : sensor pattern design, sampling and interpolation

• Main Author: Akhtar, Humza
• Other Authors: Suguna Thanagasundram
• Format: Theses and Dissertations
• Language: English
• Published: 2017
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: http://hdl.handle.net/10356/72701
• Institution: Nanyang Technological University

A touch panel is an input device for human computer interaction. It consists of a network of sensors, a sampling circuit and a controller for detecting and locating a touch input. Touch input can come from either finger or stylus depending upon the type of touch technology. These touch panels provide an intuitive and collaborative workspace so that people can perform various tasks without the use of traditional input devices like keyboard and mouse, and with the use of their fingers. Our research studies and tries to improve upon the traditional design and algorithms for capacitive touch technology. We begin by briefly describing the types of touch technology and observe that according to market research, capacitive touch will dominate the touch panel industry in years to come. We differentiate between touch panels and touch screens based on size, and steer the direction of this thesis towards touch panels after identifying three unanswered research questions that come up while designing large-scale capacitive touch panels. We discuss role of large-scale touch panels in Earth science education and explain the motivation for doing research on large-scale panels. The first question asks about the best geometrical design for a capacitive touch sensor. Capacitive sensors are arranged on a touch panel, whether it is a smart phone or a wall mount display in a grid formation. Industries use their own patented designs for manufacturing grid patterns and the efficiency of a touch panel relies on the geometry and arrangement of sensors on the grid. We study various designs patented by industry over the last few years and propose a methodology for testing and comparing different geometrical designs in a simulation environment. Such methodology was not explored in academic literature at the time of this research and it opened up an avenue for further work by other researchers in this field. By treating touch panels as binary images, we study their Fourier transform and create a hybrid sensor by combining magnitude and phase of two different designs. We also propose geometrical changes to increase the sensing area of the existing designs and test the sensitivity of sensor patterns using ANSYS Method Of Moments (MOM) capacitance solver. The second question deals with the transformation of small-scale touch panel to a large-scale one. We look at the sampling circuit of a traditional capacitive touch panel and identify that for large panels, efficient sampling is required in order to avoid latency. Considering compressive sensing as the answer to our sampling problem, we propose algorithms which use structured binary matrices for an efficient touch signal recovery, and show their implementation and memory constraints. It is also shown that such algorithms under-perform in the presence of noise. Looking at the capacitive touch sampling problem carefully, one realizes that the touch signal is highly structured and therefore we propose modified greedy algorithms for capacitive touch sampling which are not only efficient, but accurate in the presence of noise. This work further bridges the gap between two independent but actively researched fields, that is, compressive sensing and capacitive touch sensing. The third and the last question is related to subpixel interpolation procedure at the touch controller. Industry is using simple three-point interpolation schemes but as is the case with sensor design, no one has explored the possibility for an efficient interpolation algorithm designed specific to capacitive touch signals. We apply various one dimensional, two dimensional and iterative subpixel interpolators for the case of a human finger covering more than one sensor in both row and column direction while touching the panel, and compare them with each other in terms of efficiency and accuracy. Finally, at the end, we propose a full framework for capacitive touch panel including an improved sensor, an efficient sampling algorithm and an accurate subpixel interpolator.