TOUCH DETECTION METHOD

• US 20180121021A1
• Filed: 08/31/2017
• Published: 05/03/2018
• Est. Priority Date: 10/28/2016
• Status: Active Grant

• Current Assignee: Silicon Integrated Systems Corporation (Silicon Integrated Systems Corporation Taiwan)
• Original Assignee: Silicon Integrated Systems Corporation (Silicon Integrated Systems Corporation Taiwan)
• Inventors: YU, Hsin-Hsuan, KUO, Chin-Hua
• Granted Patent: US 10,444,903 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 3/04186 Touch location disambiguation
• CPC Class Codes: G06F 3/044 by capacitive means
• CPC Class Codes: G06F 3/0443 using a single layer of sensing electrodes

Abstract

A touch detection method is applied to a touch panel including sensing units and includes: detecting sensing values corresponding to the sensing units; selecting a first sensing value that is a local maximum among the sensing values; determining a first sensing unit corresponding to the first sensing value among the sensing units; selecting from the sensing units a second sensing unit located at the first side of the first sensing unit; selecting from the sensing values a second sensing value corresponding to the second sensing unit; and generating a touch coordinate according to the first sensing value, the second sensing value, a touch sensing value distribution model, and the coordinate of the first sensing unit.

Claims

1. A touch detection method, applied to a touch panel comprising sensing units, and the touch detection method comprising:

detecting sensing values corresponding to the sensing units;

selecting a first sensing value that is a local maximum among the sensing values;

determining, among the sensing units, a first sensing unit corresponding to the first sensing value;

selecting, from the sensing units, a second sensing unit located at a first side of the first sensing unit;

acquiring, from the sensing values, a second sensing value corresponding to the second sensing unit; and

generating a touch coordinate according to the first sensing value, the second sensing value, a distribution model of touch sensing values and a coordinate of the first sensing unit.

2. The touch detection method according to claim 1, further comprising:

selecting, from the sensing units, a third sensing unit located at a second side of the first sensing unit, which is opposite to the first side; and

acquiring, from the sensing values, a third sensing value corresponding to the third sensing unit;

wherein generating the touch coordinate is further based on the third sensing value and a coordinate of the third sensing unit.

3. The touch detection method according to claim 2, wherein the distribution model of touch sensing values is a parabolic model, and generating the touch coordinate comprises:

determining a polynomial function according to the first sensing value, the second sensing value, the third sensing value, the coordinate of the first sensing unit, the coordinate of the second sensing unit and the coordinate of the third sensing unit, wherein the polynomial function is used to express a relationship between the coordinate of each of the sensing units and the corresponding sensing value; and

setting a coordinate, which corresponds to the maximum sensing value in the polynomial function, as the touch coordinate.

4. The touch detection method according to claim 1, further comprising:

selecting, from the sensing units, a fourth sensing unit adjacent to the second sensing unit, wherein the fourth sensing unit and the first sensing unit are respectively at two opposite sides of the second sensing unit; and

acquiring, from the sensing values, a fourth sensing value corresponding to the fourth sensing unit;

wherein generating the touch coordinate is further based on the fourth sensing value and the coordinate of the fourth sensing unit.

5. The touch detection method according to claim 2, wherein the distribution model of touch sensing values is a normal distribution model.

6. The touch detection method according to claim 4, wherein the distribution model of touch sensing values is a normal distribution model.

7. The touch detection method according to claim 1, wherein the distribution model of touch sensing values is a group consist of a normal distribution model, a multinomial distribution model or a polynomial function model.

8. The touch detection method according to claim 1, wherein the distribution model of touch sensing values is a lookup table, and generating the touch coordinate comprises:

obtaining a coordinate compensation value from the lookup table according to at least the first sensing value and the second sensing value; and

determining the touch coordinate according to the coordinate of the first sensing unit and the coordinate compensation value.

9. The touch detection method according to claim 8, wherein the lookup table is established according to a normal distribution model, a multinomial distribution model or a polynomial function model.

10. The touch detection method according to claim 2, wherein the distribution model of touch sensing values is a lookup table, and generating the touch coordinate comprises:

obtaining a coordinate compensation value from the lookup table according to at least the first sensing value and the second sensing value; and

determining the touch coordinate according to the coordinate of the first sensing unit and the coordinate compensation value.

11. The touch detection method according to claim 10, wherein the lookup table is established according to a normal distribution model, a multinomial distribution model or a polynomial function model.

12. The touch detection method according to claim 4, wherein the distribution model of touch sensing values is a lookup table, and generating the touch coordinate comprises:

obtaining a coordinate compensation value from the lookup table according to at least the first sensing value and the second sensing value; and

determining the touch coordinate according to the coordinate of the first sensing unit and the coordinate compensation value.

13. The touch detection method according to claim 12, wherein the lookup table is established according to a normal distribution model, a multinomial distribution model or a polynomial function model.