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Parasitic extraction techniques for touchscreens

28 Dec 2015  | Mohamed Elrefaee

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Touchscreens are becoming an integral part of smartphones, tablets, automobiles, and even consumer appliances, and their popularity is ever growing. Touchscreens remove the need for peripheral elements, such as a mouse or keyboard, enabling electronics to become more portable and lightweight, and allowing developers to increase the size of the display screen in the device. Based on a recent report published by IHS, the touch panel market is forecast to grow to about 2.1 billion units in 2015, up 15.1% year-over-year, and reaching 2.8 billion units in 2018 [1].

A touchscreen detects and locates the touch of a human finger or a passive object, such as a stylus, over a display area. There are many types of touchscreens, but the main categories are: Capacitive , Resistive, Surface Acoustic Wave and Infrared.

Because capacitive touchscreens are the most common type being used today, especially in smartphones, I'll use this type as my example. Capacitive touchscreens consist of an insulator such as glass, coated with a transparent conductor such as indium tin oxide (ITO) and containing an embedded array of capacitors that form a matrix. Because a human body is an electrical conductor, touching the surface of a touchscreen distorts the electrostatic field, measured as a change in the mutual capacitance between transmitting and receiving electrodes embedded in the touchscreen. Touch tools, such as a capacitive stylus or gloves that include conductive thread, may also be used. In the absence of any external stimulus, the capacitance values of the elements of the array should all be equal, except for the elements at the extremities of the screen, which are subject to boundary variations.

One of the major verification challenges for touchscreens is parasitic extraction. Because a finger or touch tool is essentially a big conductor sitting on top of the screen, a 3D field solver extraction tool is typically required to achieve the desired accuracy necessary to capture the subtle effect at the touch point. However, most field solvers do not have the capacity to evaluate an entire design in a timely manner, making them unacceptable for production design.

Capacity in this instance means the ability of the extraction tool to run on big designs to completion. For example, if an extraction tool runs on a design for three days and generates accurate results, it does not suffer from a capacity issue, but it may suffer from a performance issue. If another extraction tool runs on that same design, but never finishes, it has a capacity issue, which means the algorithm inside the tool is not well-suited for large designs. Capacity is simply a metric, like accuracy and performance. With field solvers, capacity is typically an issue because of the resources required to do the extensive computational work. What is needed is an extraction tool that can deliver field solver accuracy with a satisfactory turnaround time for production designs.

Extraction techniques for touchscreens

Once the layout of the capacitor array is defined, a parasitic extraction tool is used to calculate the capacitances from the physical dimensions to verify that the design will operate as expected. These types of designs are based on a pattern that is repeated all over the screen. Therefore, selecting a subset of the array from any part of the screen other than the extremities will provide an adequate representative of the full screen. When selecting a sub-array, the bigger the array is, the better, because the effect of the boundary perturbation is not dominant around the middle of the array, and the results will be more credible. A designer could run extraction on the full screen if desired, but that would incur a very long runtime, and is not usually needed in initial testing.

I'll walk through an example using the Mentor Graphics Calibre xACT 3D extraction tool, part of the Calibre xACT extraction platform. The Calibre xACT 3D tool uses a combined finite and boundary element method (FEM/BEM) [2] [3] [4], with a very efficient algorithm that not only computes quickly, but also allows the job to be distributed to multiple CPUs for faster overall runtime without losing the fundamental accuracy of the FEM/BEM technique.

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