Touch fingerprint sensor based on sensor cell isolation technique with pseudo direct signaling

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#### International Journal on Smart Sensing and Intelligent Systems

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Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

eISSN: 1178-5608

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VOLUME 12 , ISSUE 1 (April 2019) > List of articles

### Touch fingerprint sensor based on sensor cell isolation technique with pseudo direct signaling

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 12, Issue 1, Pages 1-9, DOI: https://doi.org/10.21307/ijssis-2019-001

Published Online: 02-May-2019

### ARTICLE

#### ABSTRACT

This paper presents a 96 × 96 fingerprint sensor implemented with 0.25 μm standard CMOS technology. The implemented fingerprint sensor is based on a pseudo-direct signaling scheme that does not require an electrode for signal transmission. And the pseudo-direct signaling was combined with sensor cell isolation scheme which controls the unwanted influx of the electric fields effectively. And a pipelined scan technique was applied for fast fingerprint image capture while maintaining SNR performance. It was fabricated with 0.25 μm standard CMOS technology and was molded with 80 μm-thick epoxy after fabrication. The fingerprint sensor was tested with ARM board combined with PC applications. The obtained fingerprint images from the fingerprint sensor were enhanced to reduce the noise of the obtained images by using filtering technique, such as Wiener, 2D Gaussian, and 2D FIR filtering. From the test results, it was concluded that the implemented fingerprint sensor with the isolation scheme worked well without bezel electrode and can be used in biometric authentication systems effectively.

## Introduction

A biometric sensor is a transducer, which converts the biometric input signal (fingerprints, voice, vein patterns, or facial traits) of its user into an electrical signal. In order to authenticate any biometric measurement, a biometric sensor is required to scan the fingerprint or voice or face in the hardware set. Fingerprint sensor solution is a convenient small and simple alternative biometric format such as smartphone, smartcards, and IoTs. The fingerprint sensor ensures high reliability and speed of biometric identification even when using large databases.

A fingerprint sensor using a bezel electrode is applied to mobile systems to achieve the maximum performance in a fingerprint recognition system by driving a signal to a finger directly through a bezel electrode (Setlak et al., 1999; www.apple.com/apple-pay/, 2014). However, the bezel electrode may increase the defect rate of the fingerprint sensor module as well as cost. And it also reduces design flexibility for the applications. So, a bezel-less type touch fingerprint sensor has been the trend to be used recently.

The fingerprint sensor using the pseudo-direct scheme with new isolation and signal transmission schemes for touch fingerprint sensor which does not use bezel electrode has been introduced (Yeo, 2017). Since the touch fingerprint sensor evaluates the capacitances formed between a sensor plate and a fingerprint, the capacitance of the fingerprint sensor is inversely proportional to the passivation film thickness formed on the fingerprint sensor chip. Generally, a bezel-less type fingerprint sensor has relatively lower image quality acquisition performance rather than those of a fingerprint sensor using bezel electrode because it is limited to transmit a signal to the finger so that the evaluation of the capacitance formed between the sensor plate and the fingerprint is hard. The pseudo-direct scheme uses sensor cells inside the chip as a bezel electrode. A large number of sensor cells are required to form large capacitances between the electrode and a finger to deliver sensor’s driver signal effectively. And the isolation scheme of the sensor cells prevents the unwanted signal inflows to the sensor cell in order to provide the stable evaluation of the capacitance formed between the sensor cell and fingerprint (Yeo, 2016).

The fingerprint sensor with pseudo-direct signaling scheme was implemented and fabricated using 0.25 μm CMOS technology. The fingerprint sensor was molded with 80 μm-thick epoxy after fabrication. The fingerprint sensor was tested with ARM board with PC applications. Several fingerprint image enhancement techniques, such as a Weiner filtering, 2D Gaussian filtering, and 2D FIR filtering, were used to reduce the noise of the obtained images. The matching performances of the filtered images were evaluated with Precise Biometrics algorithm.

The implemented fingerprint sensor worked well under the 80 μm-thick molded film and can be used in mobile applications with the best performances.

## Fingerprint sensor structure

### Fingerprint sensor circuit

Figure 1(a) shows the fingerprint sensor circuit proposed in Yeo’s (2016) study. The sensor cell is used not only as a sensor cell but also a signal transmission cell and an isolation cell that performs different functions. The sensor cell evaluates the electric fields emitted from a finger. The transmission (Tx) cell transmits the signal for sensor evaluation. The isolation sensor cell prevents unwanted signal influx emitted from the Tx cell. CMOS switch connected to EN and NMOS connected to ENISOL in sensor cell determine the function of the sensor cell. Figure 1(b) shows the implemented sensor cell layout. The sensor cell dimension is 50 μm × 50 μm, so that the resolution of the implemented fingerprint sensor is 508 pixel-per-inch (ppi).

##### Figure 1

Fingerprint sensor cell (A) circuit for isolation scheme of sensor cell (B) implemented layout of the sensor cell.

Figure 2 shows the top view of the 25 fingerprint sensor cell array of the implemented fingerprint sensor. Each sensor pixel size is 50 μm × 50 μm. As an example, the center sensor cell is for evaluation, and the adjacent eight sensor cells are isolation cell for preventing unwanted electric fields emitted directly from the Tx cells as if building an isolation wall. The implemented fingerprint sensor can change the wall of isolation cells variably. The wider the wall, it clearly blocks the unwanted signal emitted from the Tx cells.

##### Figure 2

Top view layout of the 25 fingerprint sensor cell array with fingerprint sensor with isolation sensor cell scheme, an evaluation sensor cell, isolation sensor cells, and transmission sensor cells.

In general, the fingerprint sensor for the mobile application should be molded to ensure reliability. Thicker the passivation film, it is hard to deliver a signal to a finger through molding or coating film. Therefore, the wall of isolation sensor cells should be required because the small electric fields from Tx cells can affect a lot of influence on sensor cell evaluation.

### Fingerprint sensor structure

A 96 × 96 array fingerprint sensor was implemented and fabricated with 0.25 μm standard CMOS technology. A fingerprint sensor of 96 × 96 sensor cell array with pseudo-direct signaling scheme, as described in the previous section, was implemented. And the pipelined scan architecture was also applied to the fingerprint sensor (Yeo, 2016). Figure 3 shows the implemented 96 × 96 array fingerprint sensor with a pipelined scan driver architecture. The fingerprint sensor is consisted of 96 × 96 sensor cells array with YDEC and XMUX, eight shift ring counter. The eight shift ring counters generate pipelined scan signals which provide parallel scan of the maximum eight sensor cells simultaneously. Two-stage of 8 by 12 analog XMUX selects a proper column output for the proper evaluation sensor cell. An 8-bit ADC, converts the sensing signal output with 256 gray level, provides the superior fingerprint image quality with 508 ppi resolution.

##### Figure 3

The implemented fingerprint sensor with pipelin scan driver hitecture.

The sensed signal is fed into an 8-bit ADC through an analog XMUX. The ADC data can be serialized by SPI protocol module or stored in memory, which are not shown in Figure 3. The converted serial data by SPI are interfaced with ARM system and PC for the performance evaluation.

The eight column signal generators, V_CK1~8, generate a reset and an evaluation signals for the column sensor cells. Every eight column cell receives the same clock signal generated from the column signal generator. Therefore, the depth of the pipelined scan can be extended up to 8 which means the eight sensor cells can be evaluated simultaneously.

The eight shift ring counter chooses column clock generators at every 16-clock interval. It is designed that the evaluation of the sensor cell is performed every 16 clocks. Therefore, the pipelined evaluation of the fingerprint sensor cell can reduce the fingerprint image capture time effectively without SNR degradation (Yeo, 2016).

Figure 4 shows the example of the pipelined scan fingerprint sensor driver’s simple timing diagrams (Yeo, 2015a). This example shows the three pipelined scan mode in which the three sensor cells are evaluated at the same time. V_CK1 generates a reset signal for the first sensor cell and an evaluation starts. After the evaluation of first sensor cell has started, V_CK2 generates a reset signal for the second sensor cell and an evaluation of the second sensor cell proceeds simultaneously. V_Ck3 works in the same way. Because there are eight V_CKs in the logic, the ninth sensor cell receives a reset signal generated from V_CK1. Consequently, the evaluation of the sensor cells is performed at every 16 clock after the initial latency, which depends on the depth of the pipelined scan. As shown in Figure 4, the sensing signal is integrated, so the output voltage of the sensor cell linearly increases.

##### Figure 4

The timing diagram and output voltage characteristics of the pipelined scan driver for the fingerprint sensor.

The output voltage of the one-time integration is determined by the capacitance formed between the sensor plate and the fingerprint as expressed in the following equation (Yeo, 2015b):

##### (1)
$ΔVout(t)≅−CfingerCfinger+CSVdd$
where C finger is either C ridge or C valley in Figure 1.

The voltage difference between the output voltages of a ridge and a valley can be expressed in the following equation (Yeo, 2015):

##### (2)
$Vdiff=(CridgeCridge+CS−CvalleyCvalley+CS)Vdd$

Obviously, the sensing output voltage after n-time signal integrations results in the following equation (Yeo, 2015b):

##### (3)
$Vsense=n⋅Vdiff$

Figure 5 shows the full chip layout of the implemented 96 × 96 fingerprint sensor. The dimension of the full chip is 5,000 μm × 6,000 μm, which is mostly occupied by 96 × 96 sensor array. The sensor array is considered as an active sensing area and its size is 4,800 μm × 4,800 μm. YDEC with sensor cell driver logic and an analog XMUX are placed around chip. An ADC, clock generator logics, and memories are placed at the bottom of the chip.

##### Figure 5

The implemented full chip layout of the 96 × 96 fingerprint sensor with standard CMOS 0.25 μm technology.

## Fingerprint sensor and test environment

Figure 6(a, b) shows the fabricated fingerprint sensor chip and package with epoxy mold, respectively. The fingerprint sensor chip has 96 × 96 sensor array with 508 ppi resolution. The chip size is 5 mm × 6 mm with active sensing area is 4.8 mm × 4.8 mm. The package is with 80 μm-thick epoxy mold. The LGA of 9 mm × 9 mm package has 18 pins including power, ground, SPI interface pins, and test pins.

##### Figure 6

Fabricated fingerprint sensor (A) die photo, (B) LGA package front and back.

Figure 7 shows an evaluation board for the fingerprint sensor. The fingerprint sensor chip operates at 1.8 V~2.5 V. An evaluation board is a demonstration system to enable users to evaluate the core functionality of standalone modules quickly and easily. It is consisted of an ARM process board, a fingerprint sensor board, and an interface board between the ARM board and the fingerprint sensor board. The evaluation board communicates with PC applications for image capture and processing. SPI communication is used for controlling the fingerprint sensor and for the fast image data transmission.

##### Figure 7

Fingerprint sensor test evaluation environment.

Figure 8 shows the PC interface program. It shows the real time captured fingerprint image and its histogram. The acquired image can be saved as a graphic format. The depth of the pipelined scan can be adjusted using option tab. And it offers the other options for the adjusting the hardware parameters to get best fingerprint image.

##### Figure 8

PC interface program.

## Experimental results

### Image enhancement

Figure 9(a, b) shows the obtained 96 × 96 fingerprint images from the fingerprint sensor chip with 80 μm-thick epoxy mold and its histogram equalized image of the original image. The histogram equalization uses dynamic threshold for binarization of the image to enhance the fingerprint image so that it can increase the contrast of the original image.

##### Figure 9

Captured fingerprint image (A) original (B) histogram equalization.

Wiener filter based on local statistics estimated from a local neighborhood of each pixel, which commonly used filtering technique to reduce noise (Greenberg et al., 2002). Figure 10(a, b) shows the fingerprint Wiener filtered fingerprint image with local neighborhood size of 3 × 3, 5 × 5, respectively.

##### Figure 10

Wiener filtered fingerprint image (A) local size of 3 × 3, (B) local size of 5 × 5.

Figure 11 shows the weights of 2D 3 × 3, 5 × 5 Gaussian filter. The 2D Gaussian filter can be simplified with weighted FIR filter. The weights are roughly calculated as shown in Figure 9(a, b).

##### Figure 11

Weight of 2D Gaussian filter (A) 3 × 3, (B) 5 × 5.

Figure 12(a, b) shows the fingerprint 2D Gaussian filtered fingerprint image. The weights of the local neighborhood size of 3 × 3, 5 × 5 are shown in Figure 10.

##### Figure 12

2D Gaussian filtered fingerprint images (A) local size of 3 × 3, (B) local size of 5 × 5.

Figure 13 shows the calculation method using trigonometric function to determine the weights of the FIR filter for the 50 µm thick coated film. In this paper, it is assumed that the electric fields from the sensor electrodes to the finger are roughly proportional to only the distance between the sensor electrode and fingerprint. As shown in Figure 12, the distance between the ridges directly above the sensor electrode is 50 µm while the distance to the adjacent ridge can be calculated as roughly 50 µm/cos α .

##### Figure 13

Weight estimation of 2D FIR filter for 50 µm-thick molded fingerprint sensor.

Figure 14(a, b) shows the calculated weights of the 2D FIR filter for the touch fingerprint sensor with 50 µm-thick coated film. The local size are 3 × 3, 5 × 5, respectively.

##### Figure 14

Weight of 2D FIR filter (A) 3 × 3, (B) 5 × 5.

Figure 15(a, b) shows 2D FIR filtered fingerprint images. The weights of the local neighborhood size of 3 × 3, 5 × 5 are shown in Figure 13.

##### Figure 15

2D FIR filtered fingerprint images (A) 3 × 3, (B) 5 × 5.

### Performance results

The performances of the fingerprint sensor were tested with the images obtained from the implemented fingerprint sensor. The implemented fingerprint sensor chip operates at 1.8 V~2.5 V and its operating frequency is about 10 MHz. The power consumption in fingerprint image capture operation condition is less than 3 mA and the image capture time is less than 20 ms with the help of the pipelined scanning fingerprint sensor cells technique.

The matching performances of the fingerprint sensor, such as false acceptance ratio (FAR) and false rejection ratio (FRR), were evaluated by using Precise Biometrics’ fingerprint software (https://precisebiometrics.com/wp-content/uploads/2014/11/White-Paper-Understanding-Biometric-Performance-Evaluation.pdf). The software provides an optimal solution for fingerprint matching in mobile systems. FAR indicates a measure of the likelihood that a fingerprint sensor will incorrectly approve an access attempt by unauthorized users. FAR is typically expressed as the ratio of the number of false acceptances divided by the number of authentication attempts. FRR indicates a measure of the likelihood that a fingerprint sensor will incorrectly reject an access attempt by an authorized user. FRR typically is stated as the ratio of the number of false recognitions divided by the number of identification attempts. In general, FAR and FRR are related to each other and are inversely proportional to each other. Therefore, only lower FAR or lower FRR might be misleading about the performance of the fingerprint sensor.

In total, 250 fingerprints have been captured and each fingerprint image was captured in three different directions, 0 degree, 45 degree, and 90 degree. The acquired fingerprint images were stored to build an information database. In the matching process, the captured images were passed to the software and a matching analysis was performed by comparing with the established database. Figure 16 shows the FAR versus FRR results of the fingerprint sensor. FRR is about 3% at FAR = 10−5 and FRR is about 2% at FAR=10−4, which are an appropriate performance that can be applied to the mobile systems.

##### Figure 16

The matching performance of the fingerprint sensor (FAR vs. FRR).

Table 1 summarizes the performances of the implemented fingerprint sensor and the comparisons with other fingerprint sensors. The array size of the other fingerprint sensors is more than three times larger than the implemented fingerprint sensor. And they did not test in molded state. The performances of the implemented fingerprint sensor in this work show that it is suitable for mobile applications.

##### Table 1

Performance summary and comparisons.

## Conclusions

A 96 × 96 array touch fingerprint sensor with the pipelined scan technology was designed and implemented with 0.25 μm standard CMOS technology. The fabricated fingerprint sensor was packaged with 80 μm-thick epoxy mold. The captured image of superior quality with 256 gray level in every pixel provides 508 ppi resolution data for fingerprint recognition algorithm. The implemented pipelined scan driver architecture enables fast image capture time with ultra-low power consumptions. The normal image capture time is less than 20 ms regardless of pipelined scan mode. The fingerprint sensor operates at 1.8 V~2.5 V with power consumptions of 3 mA. The fingerprint image enhancement techniques, such as histogram equalization, Wiener filtering, and FIR filtering technique, were evaluated for the fingerprint image enhancement.

It is concluded that the proposed fingerprint sensor worked well under the thick mold film without bezel electrode and is convenient to be embedded to mobile system for realizing one-to-one verification enabling fast, secure, highly accurate, and simple. Therefore, it can be optimized for use in mobile, smart card, and IoT applications with the best performances.

## Acknowledgements

This work was supported by Hanshin University Research Grant.

## References

1. Greenberg, S., Aladjem, M. and Kogan, D.. 2002. Fingerprint image enhancement using filtering techniques. Real-Time Imaging 8 3: 227–36.
[CROSSREF]
2. Hassan, H. and Kim, H.. 2018. CMOS capacitive fingerprint sensor based on differential sensing circuit with noise cancellation. Sensors 18 7.
[CROSSREF]
3. Jung, S., Nam, J., Yang, D. and Lee, M.. 2005. A CMOS integrated capacitive fingerprint sensor with 32-bit RISC microcontroller. IEEE Journal Solid State Circuits 40: 1745–50.
[CROSSREF]
4. Setlak, D. R., Vonno, N. W. V., Lowther, R. and Gebauer, D.. 1999. Electric field fingerprint sensor having enhanced feature and related methods. US Patent No. 5940526.
5. Shimamura, T., Morimura, H., Shigematsu, S., Nakanishi, M. and Machida, K.. 2010. Capacitive-sensing circuit technique for image quality improvement on fingerprint sensor LSIs. IEEE Journal Solid State Circuits 45: 1080–7.
[CROSSREF]
6. Yeo, H.. 2015a. Capacitive fingerprint sensor circuit using pseudo-direct scheme for mobile applications. International Journal of Multimedia and Ubiquitous Engineering 10 2: 237–48.
[CROSSREF]
7. Yeo, H.. 2015b. A new fingerprint sensor based on signal integration scheme using charge transfer circuit. International Journal of Bio-Science and Bio-Technology 7 1: 29–38.
[CROSSREF]
8. Yeo, H.. 2016. A fingerprint sensor with pipelined scan driver for mobile applications. International Journal of Multimedia and Ubiquitous Engineering 11 8: 359–66.
[CROSSREF]
9. Yeo, H.. 2017. Fingerprint sensor circuit and sensor cell isolation scheme for pseudo-direct signaling. International Journal of Control and Automation 10 6: 149–60.
[CROSSREF]

### FIGURES & TABLES

Figure 1

Fingerprint sensor cell (A) circuit for isolation scheme of sensor cell (B) implemented layout of the sensor cell.

Figure 2

Top view layout of the 25 fingerprint sensor cell array with fingerprint sensor with isolation sensor cell scheme, an evaluation sensor cell, isolation sensor cells, and transmission sensor cells.

Figure 3

The implemented fingerprint sensor with pipelin scan driver hitecture.

Figure 4

The timing diagram and output voltage characteristics of the pipelined scan driver for the fingerprint sensor.

Figure 5

The implemented full chip layout of the 96 × 96 fingerprint sensor with standard CMOS 0.25 μm technology.

Figure 6

Fabricated fingerprint sensor (A) die photo, (B) LGA package front and back.

Figure 7

Fingerprint sensor test evaluation environment.

Figure 8

PC interface program.

Figure 9

Captured fingerprint image (A) original (B) histogram equalization.

Figure 10

Wiener filtered fingerprint image (A) local size of 3 × 3, (B) local size of 5 × 5.

Figure 11

Weight of 2D Gaussian filter (A) 3 × 3, (B) 5 × 5.

Figure 12

2D Gaussian filtered fingerprint images (A) local size of 3 × 3, (B) local size of 5 × 5.

Figure 13

Weight estimation of 2D FIR filter for 50 µm-thick molded fingerprint sensor.

Figure 14

Weight of 2D FIR filter (A) 3 × 3, (B) 5 × 5.

Figure 15

2D FIR filtered fingerprint images (A) 3 × 3, (B) 5 × 5.

Figure 16

The matching performance of the fingerprint sensor (FAR vs. FRR).

### REFERENCES

1. Greenberg, S., Aladjem, M. and Kogan, D.. 2002. Fingerprint image enhancement using filtering techniques. Real-Time Imaging 8 3: 227–36.
[CROSSREF]
2. Hassan, H. and Kim, H.. 2018. CMOS capacitive fingerprint sensor based on differential sensing circuit with noise cancellation. Sensors 18 7.
[CROSSREF]
3. Jung, S., Nam, J., Yang, D. and Lee, M.. 2005. A CMOS integrated capacitive fingerprint sensor with 32-bit RISC microcontroller. IEEE Journal Solid State Circuits 40: 1745–50.
[CROSSREF]
4. Setlak, D. R., Vonno, N. W. V., Lowther, R. and Gebauer, D.. 1999. Electric field fingerprint sensor having enhanced feature and related methods. US Patent No. 5940526.
5. Shimamura, T., Morimura, H., Shigematsu, S., Nakanishi, M. and Machida, K.. 2010. Capacitive-sensing circuit technique for image quality improvement on fingerprint sensor LSIs. IEEE Journal Solid State Circuits 45: 1080–7.
[CROSSREF]
6. Yeo, H.. 2015a. Capacitive fingerprint sensor circuit using pseudo-direct scheme for mobile applications. International Journal of Multimedia and Ubiquitous Engineering 10 2: 237–48.
[CROSSREF]
7. Yeo, H.. 2015b. A new fingerprint sensor based on signal integration scheme using charge transfer circuit. International Journal of Bio-Science and Bio-Technology 7 1: 29–38.
[CROSSREF]
8. Yeo, H.. 2016. A fingerprint sensor with pipelined scan driver for mobile applications. International Journal of Multimedia and Ubiquitous Engineering 11 8: 359–66.
[CROSSREF]
9. Yeo, H.. 2017. Fingerprint sensor circuit and sensor cell isolation scheme for pseudo-direct signaling. International Journal of Control and Automation 10 6: 149–60.
[CROSSREF]