WO2022027155A1

WO2022027155A1 - Device and method for fingerprint sensing

Info

Publication number: WO2022027155A1
Application number: PCT/CN2020/106478
Authority: WO
Inventors: Hiroshi Mizuhashi; Hayato Kurasawa; Koji Noguchi
Original assignee: Huawei Technologies Co., Ltd.
Priority date: 2020-08-01
Filing date: 2020-08-01
Publication date: 2022-02-10
Also published as: CN116601589A

Abstract

A device for fingerprint sensing is provied. The device includes: a Code Division Multiple (CDM) driver configured to obtain multiple sets of raw data by using one set of CDM codes multiple times; and a data processor configured to combine the multiple sets of raw data to reconstruct high resolution image data. The device improves sensing distance while maintaining high resolution.

Description

[Title established by the ISA under Rule 37.2] DEVICE AND METHOD FOR FINGERPRINT SENSING

TECHNICAL FIELD

The present invention relates to a device and a method for fingerprint sensing.

BACKGROUND

In a capacitive finger print sensor (FPS) , horizontal Tx lines are driven line by line. For example, a rectangular wave is applied to each Tx line, an electric current depending on the capacitance caused by a finger flows in each vertical Rx line, and this operation is repeated from the top Tx line to the bottom Tx line. The capacitive FPS has a limitation of the distance less than 0.2mm or 0.3mm because the electric field generated by the FPS is very weak due to a high resolution sensor. So it is difficult to use in a smartphone because it is easy to break with a thinner cover glass.

In bundle driving, the rectangular wave is applied to the Tx lines, for example, in three successive lines, and it allows for improving sensing distance. This technique is applied to hovering detection and so on. So it allows to detect information of an object that is farther away.

In Code Division Multiplex (CDM) driving, waveforms corresponding to CDM codes are applied and detected for a predetermined number of successive lines and the predetermined points of time, and it allows for improving signal levels by generating strong electric field. Strength and quality of a signal increase proportionally depending on the number of CDM codes. But the positive and negative voltages are randomly mixed by minimum sensor pitch, and electric fields cancel each other partially. So the electric field cannot reach long distance.

SUMMARY

A device and a method for fingerprint sensing are provided to improve sensing distance while maintaining high resolution, for example, enabling fingerprint detection with a thicker cover glass, by combination a bundle shift CDM driving and a Super Resolution method.

According to a first aspect, a device for fingerprint sensing is provided, where the device includes: a Code Division Multiple (CDM) driver configured to obtain multiple sets of raw data by using one set of CDM codes multiple times; and a data processor configured to combine the multiple sets of raw data to reconstruct high resolution image data.

In a possible implementation, the CDM driver includes: multiple scanners for providing CDM waveforms to respective lines according to a CDM waveform pattern; and a random sequencer for successively selecting the scanner.

In a possible implementation, the frequency of a clock to be supplied to the scanners is different from the frequency of a clock to be supplied to the sequencer.

In a possible implementation, the CDM driver includes: the CDM driver includes: multiple scanners for providing CDM waveforms to respective lines according to a CDM waveform pattern; a start selector for setting a start position of selecting the scanner; a random sequencer for successively selecting every Mth scanner; and output selectors for connecting the scanner to a corresponding line and subsequent (M-1) lines.

In a possible implementation, the CDM waveform pattern is for NM lines and NM points of time, and is generated from a CDM waveform pattern for N lines and N points of time by repeating each waveform in each column M times, and adding (M-1) columns after each column by shifting (M-1) times.

In a possible implementation, the CDM driver includes: a random sequencer for successively selecting every other scanner; an odd line driver comprising scanners for driving corresponding odd lines; an even line driver comprising scanners for driving corresponding even lines; and switches for connecting the odd lines to the corresponding scanners in the odd line driver and connecting the even lines to VGL when driving the odd lines, and connecting the even lines to the corresponding scanners in the even line driver and connecting the odd lines to the VGL when driving the even lines.

In a possible implementation, the CDM codes are m-sequence codes.

According to a second aspect, a method for fingerprint sensing is provided, where the method includes: obtaining, by a Code Division Multiple (CDM) driver, multiple sets of raw data by using one set of CDM codes multiple times; and combining, by a data processor, the multiple sets of raw data to reconstruct high resolution image data.

BRIEF DESCRIPTION OF DRAWINGS

To describe the technical solutions in the embodiments of the present invention or in the prior art more clearly, the following briefly introduces the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description show merely some embodiments of the present invention, and a person of ordinary skill in the art may still derive other drawings from these accompanying drawings without creative efforts.

FIG. 1 shows a flowchart of a method for fingerprint sensing according to an embodiment of the present invention;

FIG. 2 shows a simplified structure of a detection area of an apparatus;

FIG. 3 shows a simplified structure of a detection area;

FIG. 4 shows a diagram for explaining CDM driving;

FIG. 5 shows an example of a waveform pattern to be applied;

FIG. 6 shows a process for generating high resolution image data from raw data for one frame;

FIG. 7 shows a process for calculating three sets of low resolution image data after obtaining all the raw data;

FIG. 8 shows a process for calculating each set of low resolution image data after obtaining each part of the raw data;

FIG. 9 shows a block diagram of a CDM driver for bundle CDM driving according to an embodiment of the present invention;

FIG. 10 shows clock signals provided to the random sequencer and the scanners;

FIG. 11 shows a waveform pattern used in the random sequencer;

FIG. 12 shows a block diagram of another CDM driver for bundle CDM driving according to an embodiment of the present invention;

FIG. 13 shows operations of the CDM driver for bundle CDM driving shown in FIG. 12;

FIG. 14 shows an example of a circuit of a sensor for detecting the strength of an electric field detected by the RX lines;

FIG. 15 shows how to obtain ΔCm;

FIG. 16 shows the relationship between the number of Tx lines and the capacitance;

FIG. 17 shows SNR measurement results for TDM, CDM128, CDM256, and CDM512 driving schemes;

FIG. 18 shows measurement results of signal levels;

FIG. 19 shows measurement results of SNRs;

FIG. 20 shows a waveform pattern used in the random sequencer;

FIG. 21 shows relationship between the Tx lines used for generating electric fields and the Rx lines used for detecting the electric fields;

FIG. 22 shows relationship between the Tx lines used for generating electric fields and the Rx lines used for detecting the electric fields;

FIG. 23 shows relationship between the Tx lines used for generating electric fields and the Rx lines used for detecting the electric fields;

FIG. 24 shows a two dimensional (2D) CDM scheme according to an embodiment of the present invention;

FIG. 25 shows a voltage forming method according to an embodiment of the present invention;

FIG. 26 shows a voltage forming and CDM driving scheme according to an embodiment of the present invention; and

FIG. 27 (a) shows an application scenario for the embodiments of the present invention;

FIG. 27 (b) shows an application scenario for the embodiments of the present invention; and

FIG. 27 (c) shows an application scenario for the embodiments of the present invention.

DESCRIPTION OF EMBODIMENTS

The following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only some but not all of the embodiments of the present invention. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

FIG. 1 shows a flowchart of a method for fingerprint sensing according to an embodiment of the present invention. In Step S1, raw data for one frame is obtained by using bundle CDM driving. This bundle CDM driving performs CDM driving with a bundle of lines. Firstly, the CDM driving is explained with reference to FIG. 2 to FIG. 4. An electric field is generated by horizontal Tx lines (hereinafter, a Tx line is also simply referred to as a “line” ) and detected by vertical Rx lines. In order to simplify the description, FIG. 3 shows only Tx1 to Tx4 lines. The same rectangular wave is applied to each line, and waveforms are detected simultaneously in all Rx lines.

In this example, applying the rectangular waves and detecting waveforms are performed at four points of time, as shown in FIG. 4. FIG. 3 shows that an object is on the Tx2 line. FIG. 4 shows detected levels over time in one Rx line, specifically, at the intersections of the Rx line and the Tx1 to Tx4 lines in FIG. 3. An electric current depending on the capacitance caused by an object flows in each Rx line. In FIG. 4, since the object is on the Tx2 line, the detected level in Tx2 line is 20%less than the detected level in the Tx1, Tx3, and Tx4 lines.

The detected levels are summed at each point of time shown by dotted arrows in FIG. 4. Assuming that the detected level that changes from low level to high level without the object is 1 and the detected level that changes from high level to low level without the object is -1, the detected level that changes from low level to high level with the object is 0.8 and the detected level that changes from high level to low level with the object is -0.8. The summed levels are shown below the respective dotted arrows. These values are multiplied by a matrix corresponding to CDM codes and subtracted from the value corresponding to the number of lines. The value depending on the object is 0.8 in line TX2, and 0 in the other lines. In this way, signals for one Rx line can be obtained. Signals for one frame can be obtained by obtaining the signals for each Rx line. In this way, raw data for one frame can be obtained.

In the embodiment of the present invention, the CDM driving is performed for 21 lines. But, the number of lines is not limited. FIG. 5 shows an example of a waveform pattern to be applied to lines 1 to 21 and 21 points of time. “P” means an applied level is changed from low level to high level, and “N” means an applied level is changed from high level to low level. For each point of time in FIG. 5, raw data in each Rx line is obtained. By obtaining raw data for 21 points of time using the waveform pattern in FIG. 5, raw data for one frame (one frame bundle CDM driving data) can be obtained.

Returning to FIG. 1, in Step S2, multiple sets of low resolution image data are generated from the one frame bundle CDM driving data by sort and calculation. The raw data for one frame includes raw data obtained for 21 points of time. As shown in FIG. 6, the raw data for one frame is divided into three sets of raw data by sorting in a predetermined time order. The first set includes raw data for the 1st, 4th, 7th, 10th, 13rd, 16th, and 19th points of time, namely, raw data for every third point of time beginning from the 1st point of time. The second set includes raw data for the 2nd, 5th, 8th, 11th, 14th, 17th, and 20th points of time, namely, raw data for every third point of time beginning from the 2nd point of time. The third set includes raw data for the 3rd, 6th, 9th, 12th, 15th, 18th, and 21st points of time, namely, raw data for every third point of time beginning from the 3rd point of time.

Then, CDM decoding process is performed for the first, second, and the third sets of raw data that include raw data for 21 lines and 7 points of time. For example, by using raw data for the 1st to 7th lines and the 7 points of time, signals for the 1st to 7th lines in each Rx line can be calculated in the same way as explained with reference to FIG. 4. By using raw data for the 8th to 14th lines and 7 points of time, signals for the 8th to 14th lines in each Rx line can be calculated. By using raw data for the 15th to 21st lines, signals for the 15th to 21st lines in each Rx line can be calculated. In this way, signals for the 1st to 21st lines in each Rx line can be obtained from each set of raw data. However, compared with using raw data for 21 lines and 21 points of time, resolution of the signals is low. After performing this CDM decoding process for the first, second, and third sets of raw data, three sets of low resolution image data are obtained in Step S2.

In Step S3, a high resolution image data is generated from the multiple sets of low resolution image data by reconstruction. Various Super Resolution techniques, also called as “Super Resolution imaging (SR) ” are available for enhancing resolution of an image.

In this embodiment, the sensing distance can be improved from 200 umCG (micro meter Cover Glass) to 400 umCG, the signal level can be improved up to 1000 times higher, and the high resolution can be kept up to 300ppi (pixel per inch) .

In this embodiment, three sets of low resolution image data are calculated after obtaining raw data for the 1st to 21st points of time and sorting this raw data, as shown in FIG. 7. However, as shown in FIG. 8, raw data may be obtained in the time order as shown in the right part of FIG. 6. In this way, the first set of low resolution image data can be calculated immediately after obtaining raw data for the 1st to 7th points of time, the second set of low resolution image data can be calculated immediately after obtaining raw data for the 8th to 14th points of time, and the third set of low resolution image data can be calculated immediately after obtaining raw data for the 15th to 21st points of time, as shown in FIG. 8. CDM decoding process takes a lot of time. This constitution enables pipeline processing of data sensing and code decoding, thereby improving sensing and calculating speed.

The following describes circuits for fingerprint sensing according to the embodiment of the present invention.

FIG. 9 shows a block diagram of a CDM driver for bundle CDM driving according to an embodiment of the present invention. The combination of bundle driving and CDM driving is realized by a simple circuit configuration using simple scanners and a random sequencer (such as m-sequence) . This circuit allows for generating a stronger electric field by bundle CDM driving. Step S1 is performed by the CDM driver, and Step S2 and Step S3 are performed by a data processor.

Scanners 1 to 21 (scanners 10 to 21 are not shown in FIG. 9) and a random sequencer 30 are driven by difference frequencies clk1 and clk2 in FIG. 10, clk1 is provided for the scanners 1 to 21, and clk 2 is provided for the random sequencer 30. The random sequencer 30 uses a waveform pattern for 7 lines and 7 points of time in FIG. 11.

Firstly, the random sequencer 30 generates CDM codes “P” , “N” , “N” , “P” , “P” , “P” , “N”shown in the leftmost column of the waveform pattern in FIG. 11 in synchronization with clk2, and the generated CDM codes are stored in each scanner in synchronization with clk1. The random sequencer 30 stores a “P” waveform in the scanners 1 to 3, stores a “N” waveform in the scanners 4 to 6, and stores waveforms in scanners 7 to 21, similarly. After all CDM codes are stored in the scanners 1 to 21, the scanners 1 to 21 drive Tx lines according to the stored codes, and waveforms in the Tx lines are detected in each Rx lines.

Next, the random sequencer 30 generates CDM codes “N” , “P” , “N” , “N” , “P” , “P” , “P” shown in the second column from the left of the waveform pattern in FIG. 11 in synchronization with clk2, and the generated CDM codes are stored in each scanner in synchronization with clk1. The random sequencer 30 stores an “N” waveform in the scanners 1 to 3, stores a “P” waveform in the scanners 4 to 6, and stores waveforms in the scanners 7 to 21 similarly. After all CDM codes are stored in the scanners 1 to 21, the scanners 1 to 21 drive Tx lines according to the stored codes, and waveforms in the Tx lines are detected in each Rx lines.

The subsequent procedures are performed similarly, and raw data corresponding to the waveforms on the 4th to 21th points of time in FIG. 4 are obtained.

FIG. 12 shows a block diagram of another CDM driver for bundle CDM driving according to an embodiment of the present invention. The combination of bundle driving and CDM driving is realized by a simple circuit configuration using a block scanner, a random sequencer (such as m-sequence) , and a sensing block selector. This circuit allows for faster fingerprint image generation.

This circuit includes scanners 1 to 21 (scanners 10 to 21 are not shown) , a random sequencer 30, a start selector 40, and output selectors 101 to 121 (output selectors 110 to 121 are not shown) . The start selector 40 sets a start position of selecting the scanner, and the random sequencer 30 successively selects every third scanner. Each output selector connects the scanner to corresponding line and two subsequent lines, for example, when the scanner 1 is selected, the output selector 101 connects the scanner 1 to the 1st to 3rd lines.

FIG. 13 shows operations of the CDM driver for bundle CDM driving shown in FIG. 12. The waveform pattern shown in the right part of FIG. 6 is applied to the lines. The left, middle, and right part of FIG. 13 show operations for obtaining raw data for the 1st, 2nd, and 3rd images in FIG. 7, respectively. The random sequencer 30 uses the waveform pattern for 7 lines and 7 points of time in FIG. 11.

Firstly, referring to the left part of FIG. 13, the start selector 40 turns the left switch ON in response to a signal sel1, thereby

scanners

1, 4, 7, 10, 13, 16, and 19 are driven in this order, namely, every third scanner are driven from the scanner 1. The output selectors 101 to 121 turn the upper switches ON in response to the signal sel1. The random sequencer 30 uses the waveforms in the leftmost column in FIG. 11. Thereby, the scanner 1 outputs a “P” waveform on the 1st to 3rd lines, the scanner 4 outputs an “N” waveform on the 4th to 6th lines, the scanner 7 outputs an “N” waveform on the 7th to 9th lines, the scanner 10 outputs a “P” waveform on the 10th to 12th lines, the scanner 13 outputs a “P” waveform on the 13th to 15th lines, the scanner 16 outputs a “P” waveform on the 16th to 18th lines, and the scanner 19 outputs an “N” waveform on the 19th to 21st lines. The waveforms on the lines are detected in each Rx line.

Then, the random sequencer 30 uses the waveforms in the second column from the left in FIG. 11, and drives the same scanners. Thereby, the scanner 1 outputs an “N” waveform on the 1st to 3rd lines, the scanner 4 outputs a “P” waveform on the 4th to 6th lines, the scanner 7 outputs an “N” waveform on the 7th to 9th lines, the scanner 10 outputs an “N” waveform on the 10th to 12th lines, the scanner 13 outputs a “P” waveform on the 13th to 15th lines, the scanner 16 outputs a “P” waveform on the 16th to 18th lines, and the scanner 19 outputs a “P” waveform on the 19th to 21st lines. The waveforms on the lines are detected in each Rx line.

The random sequencer 30 successively uses the waveforms in the third to seventh columns from the left in FIG. 11, the same scanners outputs corresponding waveforms on the lines, and the waveforms on the lines are detected in each Rx line. In this way, the raw data for the 1st image is obtained.

Secondly, referring to the middle image of FIG. 13, the start selector 40 turns the middle switch ON in response to a signal sel2, thereby

scanners

2, 5, 8, 11, 14, 17, and 20 are driven in this order, namely, every third scanner are driven from the scanner 2. The output selectors 101 to 121 turn the middle switches ON in response to the signal sel2. The random sequencer 30 uses the waveforms in the leftmost column in FIG. 11. Thereby, the 1st line is connected to VGL (voltage corresponding to OFF voltage for a gate driver) , the scanner 2 outputs a “P” waveform on the 2nd to 4th lines, the scanner 5 outputs an “N” waveform on the 5th to 7th lines, the scanner 8 outputs an “N” waveform on the 8th to 10th lines, the scanner 11 outputs a “P” waveform on the 11th to 13th lines, the scanner 14 outputs a “P” waveform on the 14th to 16th lines, the scanner 17 outputs a “P” waveform on the 17th to 19th lines, and the scanner 20 outputs an “N” waveform on the 20th to 21st lines. The waveforms on the lines are detected in each Rx line.

The random sequencer 30 successively uses the waveforms in the second to seventh columns from the left in FIG. 11, the same scanners outputs corresponding waveforms on the lines, and the waveforms on the lines are detected in each Rx line. In this way, the raw data for the 2nd image is obtained.

Thirdly, referring to the right part of FIG. 13, the start selector 40 turns the right switch ON in response to a signal sel3, thereby

scanners

3, 6, 9, 12, 15, 18, and 21 are driven in this order, namely, every third scanner are driven from the scanner 3. The output selectors 101 to 121 turn the lower switches ON in response to the signal sel3. The random sequencer 30 uses the waveforms in the leftmost column in FIG. 11. Thereby, the 1st and 2nd lines are connected to the VGL, the scanner 3 outputs a “P” waveform on the 3rd to 5th lines, the scanner 6 outputs an “N” waveform on the 6th to 8th lines, the scanner 9 outputs an “N” waveform on the 9th to 11th lines, the scanner 12 outputs a “P” waveform on the 12th to 14th lines, the scanner 15 outputs a “P” waveform on the 15th to 17th lines, the scanner 18 outputs a “P” waveform on the 18th to 20th lines, and the scanner 21 outputs an “N” waveform on the 21st line. The waveforms on the lines are detected in each Rx line.

The random sequencer 30 successively uses the waveforms in the second to seventh columns from the left in FIG. 11, the same scanners outputs corresponding waveforms on the lines, and the waveforms on the lines are detected in each Rx line. In this way, the raw data for the 3rd image is obtained.

Here, an example of a read-out circuit connected to the RX lines will be explained below. FIG. 14 shows an example of a circuit of a sensor for detecting strength of an electric field detected by the RX lines. At the left side of the circuit shown in FIG. 14, a 20V rectangular waveform is applied to a Tx line, capacitance ΔCm of the sensor is calculated with the equation ΔCm=Cm ridge –Cm valley, as shown in FIG. 15. Cm means Tx-Rx mutual capacitance. The capacitance when performing CDM with four Tx lines is greater than the capacitance between one Tx line and one Rx line, as shown in FIG. 16. The Cm ridge is capacitance when a ridge of the surface of a finger is placed over the Tx line and the Rx line, and the Cm valley is capacitance when a valley of the surface of the finger is placed over the Tx line and the Rx line. An electric current depending on the ΔCm is output from the sensor to an analog front end (AFE) . The AFE includes an integrator, a gain amplifier (Gain AMP) , and an analog-to-digital converter (ADC) . An integrated waveform, which is output from the integrator, is amplified, and converted into a digital signal.

In the embodiments according to the present invention, high order CDM driving schemes can achieve high SNRs. FIG. 17 shows SNR measurement results for TDM, CDM128, CDM256, and CDM512 driving schemes. 60 micro meter sensor pitch and 500 micro meter tester pitch were used. FIG. 18 shows measurement results of signal levels, and FIG. 19 shows measurement results of SNRs. The measured signal levels and the SNRs approximately increase in proportion to CDM orders.

The following describes another embodiment of the CDM driving scheme with reference to FIG. 20 to FIG. 23. In FIG. 21 to FIG. 23, the number of the scanners are 32, and the waveform pattern shown in FIG. 20 is used.

Before explaining an actual operation, in order to make it easier to understand, how to obtain signals for only 1st Rx output is explained below. The 1st Rx output is turned ON by a switch (SW) for the 1st Rx output.

With reference to FIG. 21, operations at the 1st and 4th points of time are explained below:

At the 1st point of time, Rx line bundles 3, 4, 6, 8, 9, and 10 (Rx lines connected to Tx lines shown as

Bundles

3, 4, 6, 8, 9, and 10 via SW, and the same applies to the following) in “FIG. 21” , which correspond to row numbers of “P” in the “1st column” in FIG. 20, are driven with “P” waveforms, and the electronic fields detected by the 1st Rx output are summed as a positive value. After that, Rx line bundles 1, 2, 5, and 7 in “FIG. 21” , which correspond to row numbers of “N” in the “1st column” in FIG. 20, are driven with “N” waveforms, and the electronic fields detected by the 1st Rx output are summed as a negative value. The sum of the positive value and the negative value is an Rx output at the 1st point of time. Obtaining this Rx output is similar to obtaining each “Rx output” as shown by corresponding dotted arrow in FIG. 4.

At the 4th point of time, Rx line bundles 1, 4, 5, 7, 9, and 10 in “FIG. 21” , which correspond to row numbers of “P” in the “2nd column” in FIG. 20, are driven with “P” waveforms, and the electronic fields detected by the 1st Rx output are summed as a positive value. After that, Rx line bundles 2, 3, 6, and 8 in “FIG. 21” , which correspond to row numbers of “N” in the “2nd column” in FIG. 20, are driven with “N” waveforms, and the electronic fields detected by the 1st Rx output are summed as a negative value. The sum of the positive value and the negative value is an Rx output at the 4th point of time.

With reference to FIG. 22, operations at the 2nd and 5th points of time are explained below:

At the 2nd point of time, Rx line bundles 3, 4, 6, 8, 9, and 10 in “FIG. 22” , which correspond to row numbers of “P” in the “1st column” in FIG. 20, are driven with “P” waveforms, and the electronic fields detected by the 1st Rx output are summed as a positive value. After that, Rx line bundles 1, 2, 5, and 7 in “FIG. 22” , which correspond to row numbers of “N” in the “1st column” in FIG. 20, are driven with “N” waveforms, and the electronic fields detected by the 1st Rx output are summed as a negative value. The sum of the positive value and the negative value is an Rx output at the 2nd point of time.

At the 5th point of time, Rx line bundles 1, 4, 5, 7, 9, and 10 in “FIG. 22” , which correspond to row numbers of “P” in the “2nd column” in FIG. 20, are driven with “P” waveforms, and the electronic fields detected by the 1st Rx output are summed as a positive value. After that, Rx line bundles 2, 3, 6, and 8 in “FIG. 22” , which correspond to row numbers of “N” in the “2nd column” in FIG. 20, are driven with “N” waveforms, and the electronic fields detected by the 1st Rx output are summed as a negative value. The sum of the positive value and the negative value is an Rx output at the 5th point of time.

With reference to FIG. 23, operations at the 3rd and 6th points of time are explained below:

At the 3rd point of time, Rx line bundles 3, 4, 6, 8, 9, and 10 in “FIG. 23” , which correspond to row numbers of “P” in the “1st column” in FIG. 20, are driven with “P” waveforms, and the electronic fields detected by the 1st Rx output are summed as a positive value. After that, Rx line bundles 1, 2, 5, and 7 in “FIG. 23” , which correspond to row numbers of “N” in the “1st column” in FIG. 20, are driven with “N” waveforms, and the electronic fields detected by the 1st Rx output are summed as a negative value. The sum of the positive value and the negative value is an Rx output at the 3rd point of time.

At the 6th point of time, Rx line bundles 1, 4, 5, 7, 9, and 10 in “FIG. 23” , which correspond to row numbers of “P” in the “2nd column” in FIG. 20, are driven with “P” waveforms, and the electronic fields detected by the 1st Rx output are summed as a positive value. After that, Rx line bundles 2, 3, 6, and 8 in “FIG. 23” , which correspond to row numbers of “N” in the “2nd column” in FIG. 20, are driven with “N” waveforms, and the electronic fields detected by the 1st Rx output are summed as a negative value. The sum of the positive value and the negative value is an Rx output at the 6th point of time.

By repeating the above procedures for 3rd to 11th columns in FIG. 20, Rx outputs at the 7th to 33rd points of time can be obtained.

FIG. 24 shows a two dimensional (2D) CDM scheme according to an embodiment of the present invention. In this embodiment, four Tx lines and four Rx outputs are used, however, this scheme can be applied to any number of Tx lines and Rx outputs. In FIG. 24, the positions of intersections of four Tx lines and four Rx outputs are numbered from 1 to 16, and these positions are referred to as points 1 to 16. In the following description, it is assumed that Read (+, ij) (i = 1, 2, 3, and 4 and j = 1, 2, 3, and 4) and Read (–, ij) (i = 2, 3, and 4 and j = 2, 3, and 4) are, for example, memory areas to store detected values.

At time t1, Tx1 to Tx4 lines are driven with “P” waveforms, and the electric fields are detected by Rx1 to Rx4 lines. At time t11, the sum of the detected values by Rx1 to Rx4 lines is obtained as Read (+, 11) , a value 0 is obtained as Read (–, 11) , and Read (+, 11) –Read (–, 11) is output as Rx output at time t11. At time t12, the sum of the detected values by Rx1 and Rx3 lines is obtained as Read (+, 12) , the sum of the detected values by Rx2 and Rx4 lines is obtained as Read (–, 12) , and Read (+, 12) –Read (–, 12) is output as Rx output at time t12. At time t13, the sum of the detected values by Rx1 and Rx2 lines is obtained as Read (+, 13) , the sum of the detected values by Rx3 and Rx4 lines is obtained as Read (–, 13) , and Read (+, 13) –Read (–, 13) is output as Rx output at time t13. At time t14, the sum of the detected values by Rx1 and Rx4 lines is obtained as Read (+, 14) , the sum of the detected values by Rx2 and Rx3 lines is obtained as Read (–, 14) , and Read (+, 14) –Read (–, 14) is output as Rx output at time t14.

At time t2, Tx1 and Tx3 lines are driven with “P” waveforms, Tx2 and Tx4 lines are driven with “N” waveforms, and the electric fields are detected by Rx1 to Rx4 lines. At time t21, the sum of the detected values by Rx1 to Rx4 lines is obtained as Read (+, 21) , a value 0 is obtained as Read (–, 21) , and Read (+, 21) –Read (–, 21) is output as Rx output at time t21. At time t22, the sum of the detected values by Rx1 and Rx3 lines is obtained as Read (+, 22) , the sum of the detected values by Rx2 and Rx4 lines is obtained as Read (-, 22) , and Read (+, 22) –Read (–, 22) is output as Rx output at time t22. At time t23, the sum of the detected values by Rx1 and Rx2 lines is obtained as Read (+, 23) , the sum of the detected values by Rx3 and Rx4 lines is obtained as Read (-, 23) , and Read (+, 23) –Read (–, 23) is output as Rx output at time t23. At time t24, the sum of the detected values by Rx1 and Rx4 lines is obtained as Read (+, 24) , the sum of the detected values by Rx2 and Rx3 lines is obtained as Read (-, 24) , and Read (+, 24) –Read (-, 24) is output as Rx output at time t24.

At time t3, Tx1 and Tx2 lines are driven with “P” waveforms, Tx3 and Tx4 lines are driven with “N” waveforms, and the electric fields are detected by Rx1 to Rx4 lines. At time t31, the sum of the detected values by Rx1 to Rx4 lines is obtained as Read (+, 31) , a value 0 is obtained as Read (–, 31) , and Read (+, 31) –Read (–, 31) is output as Rx output at time t31. At time t32, the sum of the detected values by Rx1 and Rx3 lines is obtained as Read (+, 32) , the sum of the detected values by Rx2 and Rx4 lines is obtained as Read (–, 32) , and Read (+, 32) –Read (–, 32) is output as Rx output at time t32. At time t33, the sum of the detected values by Rx1 and Rx2 lines is obtained as Read (+, 33) , the sum of the detected values by Rx3 and Rx4 lines is obtained as Read (–, 33) , and Read (+, 33) –Read (–, 33) is output as Rx output at time t33. At time t34, the sum of the detected values by Rx1 and Rx4 lines is obtained as Read (+, 34) , the sum of the detected values by Rx2 and Rx3 lines is obtained as Read (–, 34) , and Read (+, 34) –Read (–, 34) is output as Rx output at time t34.

At time t4, Tx1 and Tx4 lines are driven with “P” waveforms, Tx2 and Tx3 lines are driven with “N” waveforms, and the electric fields are detected by Rx1 to Rx4 lines. At time t41, the sum of the detected values by Rx1 to Rx4 lines is obtained as Read (+, 41) , a value 0 is obtained as Read (–, 41) , and Read (+, 41) –Read (–, 41) is output as Rx output at time t41. At time t42, the sum of the detected values by Rx1 and Rx3 lines is obtained as Read (+, 42) , the sum of the detected values by Rx2 and Rx4 lines is obtained as Read (–, 42) , and Read (+, 42) –Read (–, 42) is output as Rx output at time t42. At time t43, the sum of the detected values by Rx1 and Rx2 lines is obtained as Read (+, 43) , the sum of the detected values by Rx3 and Rx4 lines is obtained as Read (–, 43) , and Read (+, 43) –Read (–, 43) is output as Rx output at time t43. At time t44, the sum of the detected values by Rx1 and Rx4 lines is obtained as Read (+, 44) , the sum of the detected values by Rx2 and Rx3 lines is obtained as Read (–, 44) , and Read (+, 44) –Read (–, 44) is output as Rx output at time t44.

The signals at points 1 to 16 (hereinafter, referred to as s1 to s16) can be calculated using following equations (1) and (2) , namely, the right side matrix of equation (1) , which is data after Rx CDM decoding, can be obtained by calculating the left side multiplication, and the right side matrix of equation (2) , which is data after Tx CDM decoding, can be obtained by calculating the left side multiplication:

FIG. 25 shows a voltage forming method according to an embodiment of the present invention. The upper part of FIG. 25 shows a cross-sectional view perpendicular to the Tx lines. Every other line are used as Fx lines. For example, even Tx lines are used for AC driving and negative voltage is applied to odd Tx lines (Fx lines) . This negative voltage may be DC voltage, or may be reverse voltage of a Tx line neighboring to the left or right (Fx line) , as shown in FIG. 25.

FIG. 26 shows a voltage forming and CDM driving scheme according to an embodiment of the present invention. The combination of voltage forming and CDM driving is realized by a simple circuit configuration including even/odd line CDM drivers and a random sequencer (such as m-sequence) . Each odd line is connected to a scanner in an odd line CDM driver 51 via a switch, and the VGL via another switch. Each even line is connected to a scanner in an even line CDM driver 52 via a switch, and the VGL via another switch. In FIG. 26, since the scales of the left part and the right part are different, they are not connected with solid lines.

When odd line CDM is performed, in response to a signal odd sel, the odd lines are connected to the corresponding scanners in the odd line CDM driver 51, and the even lines are connected to the VGL. The random sequencer 30 drives the scanners in the odd line CDM driver 51 to output waveforms according to the waveform pattern. A fingerprint image is calculated from raw data obtained from the odd lines. When even line CDM is performed, in response to a signal even sel, the even lines are connected to the corresponding scanners in the even line CDM driver 52, and the odd lines are connected to the VGL. The random sequencer 30 drives the scanners in the even line CDM driver 52 to output waveforms according to the waveform pattern. A fingerprint image is calculated from raw data obtained from the even lines. After the fingerprint images are obtained from the odd lines and the even lines, a full finger print image is generated from these fingerprint images using a Super-Resolution (SR) technique.

FIG. 27 shows application scenarios for the embodiments of the present invention. It is possible to provide a smooth user interface and enhanced security by applying this technology to Global FPS. FIG. 27 (a) shows seamless screen unlocking by touching any area of its display, FIG. 27 (b) shows App direct login with authentication by touching icon, and FIG. 27 (c) shows high security payment via multi-finger authentication.

In the embodiments according to the present invention, a combination of bundle CDM driving and a Super Resolution method allows for improving a sensing distance while maintaining high resolution. Fingerprint can be detected with a thicker cover glass. The present invention can be applied to Global-FPS for in-cell PD (photodiodes manufactured in a display device) solution without an in-pixel amplifier system for in-cell PD. The same approach as a capacitive sensor is also effective for optical sensor. When photodiodes are used for fingerprint sensing, the amount of current is weak. However, signals can be intensified by the driving method of the present invention without using an amplifier.

What is disclosed above are merely exemplary embodiments of the present invention, and is certainly not intended to limit the protection scope of the present invention. A person of ordinary skill in the art may understand that all or some of the processes that implement the foregoing embodiments and equivalent modifications made in accordance with the claims of the present invention shall fall within the scope of the present invention.

Claims

A device for fingerprint sensing, comprising:

a Code Division Multiple (CDM) driver configured to obtain multiple sets of raw data by using one set of CDM codes multiple times; and

a data processor configured to combine the multiple sets of raw data to reconstruct high resolution image data.
The device according to claim 1, wherein the CDM driver comprises:

multiple scanners for providing CDM waveforms to respective lines according to a CDM waveform pattern; and

a random sequencer for successively selecting the scanner.
The device according to claim 2, wherein the frequency of a clock to be supplied to the scanners is different from the frequency of a clock to be supplied to the sequencer.
The device according to claim 1, wherein the CDM driver comprises:

multiple scanners for providing CDM waveforms to respective lines according to a CDM waveform pattern;

a start selector for setting a start position of selecting the scanner;

a random sequencer for successively selecting every Mth scanner; and

output selectors for connecting the scanner to a corresponding line and subsequent (M-1) lines.
The device according to claim 4, wherein the CDM waveform pattern is for NM lines and NM points of time, and is generated from a CDM waveform pattern for N lines and N points of time by repeating each waveform in each column M times, and adding (M-1) columns after each column by shifting (M-1) times.
The device according to claim 1, wherein the CDM driver comprises:

a random sequencer for successively selecting every other scanner;

an odd line driver comprising scanners for driving corresponding odd lines;

an even line driver comprising scanners for driving corresponding even lines; and

switches for connecting the odd lines to the corresponding scanners in the odd line driver and connecting the even lines to VGL when driving the odd lines, and connecting the even lines to the corresponding scanners in the even line driver and connecting the odd lines to the VGL when driving the even lines.
The device according to any one of claims 1 to 6, wherein the CDM codes are m-sequence codes.
A method for fingerprint sensing, comprising:

obtaining, by a Code Division Multiple (CDM) driver, multiple sets of raw data by using one set of CDM codes multiple times; and

combining, by a data processor, the multiple sets of raw data to reconstruct high resolution image data.