KR20240008766A - Graphene-based sensor, measuring device and analyzing method using graphene-based sensor - Google Patents

Abstract

A graphene-based sensor, a measuring device and an analysis method using the same are disclosed. A graphene-based sensor includes a substrate, a doped region formed by doping a partial region of the substrate with an impurity, an insulator layer laminated on the substrate except for a portion of the doped region, a graphene layer laminated on the doped region, and graphene. It includes a first electrode connected to the pin layer and a second electrode connected to the doped region.

Description

Graphene-based sensor, measuring device and analyzing method using graphene-based sensor}

Embodiments of the present invention relate to a graphene-based sensor, a measuring device and an analysis method using the same.

There are various types of sensors, including biosensors that detect biological substances such as enzymes, antibodies, and DNA, gas sensors that detect the concentration of gas, and pressure sensors that detect pressure. Among these, biosensors are largely divided into optical, electrochemical, and piezoelectric types. The optical method uses optical methods such as color development, fluorescence, chemiluminescence, and surface plasmon resonance, and the electrochemical method uses the interaction between the analyte and the bioreceptor into electrochemical signals such as potential, current, charge, conductivity, and impedance. It is a way to measure. The piezoelectric method is a method of measuring mass change. Electrochemical biosensors have advantages over other methods in terms of portability and miniaturization. Both labeled and non-labeled methods are possible, and since the detection signal is also an electrical signal, the signal conversion process is easier than the optical method. Electrochemical sensors can be implemented based on transistors to accurately measure changes in minute electrical signals.

Graphene is being actively researched as a new material that can replace semiconductors. Graphene has a two-dimensional flat crystal structure in the shape of a hexagonal honeycomb, and is thin, light, and has excellent durability and conductivity, making it used in various fields. Recently, a new device (varistor) has appeared using graphene, which is functionally a transistor but structurally similar to a diode.

The technical problem to be achieved by embodiments of the present invention is to provide a graphene-based sensor that can detect changes in minute electrical signals displayed by a sample.

Another technical problem to be achieved by embodiments of the present invention is to provide a measurement device using a graphene-based sensor.

Another technical problem to be achieved by embodiments of the present invention is to provide a method of analyzing measured values of a sensor array including a plurality of graphene-based sensors using an artificial neural network.

An example of a graphene-based sensor according to an embodiment of the present invention to achieve the above technical problem includes: a substrate; a doped region formed by doping a partial region of the substrate with an impurity; a nonconductor layer laminated on the substrate except for a portion of the doped region; A graphene layer stacked on the doped region; A first electrode connected to the graphene layer; and a second electrode connected to the doped region.

An example of a measuring device according to an embodiment of the present invention to achieve the above technical problem includes a graphene-based sensor in which a graphene layer is stacked on a doped region formed on a substrate; and an output unit that converts the output current of the graphene-based sensor into voltage and outputs it.

An example of an analysis method according to an embodiment of the present invention to achieve the above technical problem is an analysis method using a sensor array in which graphene-based sensors are arranged in one dimension or two dimensions, wherein a plurality of the sensor array Receiving a plurality of measurement values from a graphene-based sensor; And obtaining an analysis result by inputting the plurality of measurement values into an artificial neural network, wherein the artificial neural network determines the type of sample as a measurement value that appears when the sample reacts with the material of the reaction layer of each graphene-based sensor. , is learned using training data labeled with analysis results including at least one of intensity, amount, or concentration.

According to an embodiment of the present invention, a graphene-based sensor has a greater reaction sensitivity than a general sensor, so it can accurately measure changes in minute electrical signals due to interactions between antigen-antibody, enzyme, DNA, etc. Additionally, graphene-based sensors can be applied not only to the bio field but also to various fields that measure light, gas, and pressure.

1 is a diagram showing the structure of a first embodiment of a graphene-based sensor according to the present invention;
Figure 2 is a diagram showing the structure of a second embodiment of the graphene-based sensor according to the present invention;
3 is a diagram showing the structure of a third embodiment of the graphene-based sensor according to the present invention;
4 is a diagram showing the structure of a fourth embodiment of a graphene-based sensor according to the present invention;
5 is a diagram showing the structure of a first embodiment of a sensor array according to the present invention;
6 is a diagram showing the structure of a second embodiment of the sensor array according to the present invention;
7 is a diagram showing the structure of a third embodiment of a sensor array according to the present invention;
8 and 9 are diagrams showing the structure of the fourth and fifth embodiments of the sensor array according to the present invention;
10 is a diagram showing the structure of a sixth embodiment of a sensor array according to the present invention;
11 is a diagram showing the structure of a seventh embodiment of a sensor array according to the present invention;
12 is a diagram showing the structure of an eighth embodiment of a sensor array according to the present invention;
13 is a diagram showing an example of current change in a graphene-based sensor according to an embodiment of the present invention;
14 and 15 are diagrams showing an example of a measuring device using a graphene-based sensor according to an embodiment of the present invention;
Figure 16 is a diagram showing an example of the detailed configuration of the feedback unit and control unit of the measuring device according to an embodiment of the present invention;
17 to 23 are diagrams showing various examples of a measuring device using a sensor array according to an embodiment of the present invention;
24 is a diagram showing an example of use of a measuring device according to an embodiment of the present invention;
25 and 26 are diagrams showing an example of implementation of a measuring device according to an embodiment of the present invention;
Figure 27 is a diagram illustrating an example of a method for analyzing measured values of a sensor array using an artificial neural network according to an embodiment of the present invention.

Hereinafter, a graphene-based sensor, a measuring device and an analysis method using the same according to an embodiment of the present invention will be examined in detail with reference to the attached drawings.

1 is a diagram showing the structure of a first embodiment of a graphene-based sensor according to the present invention.

Referring to FIG. 1, the graphene-based sensor includes a substrate 100, an insulator layer 110, a graphene layer 120, a first electrode 130, a second electrode 140, and a doped region 160. .

There is a doped region 160 formed by doping impurities in the substrate 100. In one embodiment, the substrate may be a semiconductor layer made of a general semiconductor material such as silicon, germanium, or compound semiconductor, rather than a two-dimensional material. The doped region 160 may be doped n-type or p-type. In one embodiment, the doping region 160 may be formed of two regions 150 and 155 having different doping concentrations. For example, the doped region 160 is doped with a first doped region 150 doped at a first concentration (for example, 1*10 ⁸ /cm3 to 5*10 ¹⁹ /cm3) and a second concentration (>=th). It may include a second doped region 155 doped at a concentration of 1) (for example, 10 ¹⁹ /cm 3 or more). In another embodiment, the entire doped region 160 may be doped at the same concentration. However, for convenience of explanation, hereinafter, the doped region 160, which includes two regions 150 and 155 doped at different concentrations, will be illustrated and explained. The area and depth of the doping region 160 may vary depending on the embodiment.

The insulator layer 110 is formed on the substrate 100 except for a portion of the doped region 160. For example, the insulator layer 110 does not exist above part or all of the first doped region 150 and is exposed on the substrate 100 . In one embodiment, the insulator layer 110 may be composed of various oxides, such as silicon oxide, hafnium oxide, and aluminum oxide.

The graphene layer 120 made of graphene is laminated on the doped region 160 that is not covered by the insulator layer 110 among the doped regions 160. For example, the graphene layer 120 may have a structure that is stacked over the insulator layer 110 and the doped region 160.

The first electrode 130 is connected to the graphene layer 120. The second electrode 140 is connected to the doped region 160 below the substrate 100. In another embodiment, the upper surface of the substrate 100 may further include a via electrode 170 connected to the second electrode 140 on the lower surface of the substrate. The via electrode 170 may penetrate the insulator layer 110 and the substrate 100 and be connected to the second electrode 140 below the substrate 100. The connection structure of the first electrode 130 and the second electrode 140 of this embodiment is only an example to help understand the present invention, and is not limited to this embodiment. Examples of various connection structures of the first electrode 130 and the second electrode 140 are shown in FIGS. 2 to 4.

The surface of the graphene layer 120 may further include a reaction layer 180 formed of a material that reacts with the sample or a structure that senses pressure. Depending on the type of sample (e.g., light, biomaterial, gas, etc.), the types of materials that make up the reaction layer may vary. For example, biomaterials (DNA, antigens, antibodies, or enzymes, etc.), quantum dots (e.g., lead sulfide quantum dots), or polymer films are applied to the graphene layer 120 to form the reaction layer 180, or applied under pressure. A reaction layer 180 composed of a structure that measures can be laminated on the graphene layer 120. In another embodiment, the reaction layer 180 may be composed of several materials or layers. Depending on the type of material of the reaction layer 180 located on the surface of the graphene layer 120, the graphene-based sensor of this embodiment is an optical sensor that detects light, a biosensor that detects biomaterials, gas, temperature, humidity, etc. It can be used in various forms, such as environmental sensors that detect.

Figure 2 is a diagram showing the structure of a second embodiment of the graphene-based sensor according to the present invention.

Referring to FIG. 2, the graphene-based sensor includes a substrate 200, an insulator layer 210, a graphene layer 220, a first electrode 230, a second electrode 240, and a doped region 260. . The substrate 200, the insulator layer 210, the graphene layer 220, and the first electrode 230 have the same structure as that of FIG. 1. The depth and area of the doped region 260 may be the same as those of the first embodiment of FIG. 1 or may be partially modified.

The first electrode 230 is the same as the first embodiment of FIG. 1, but the second electrode 240 is different from the first embodiment of FIG. 1. The second electrode 240 is formed at a position spaced apart from the first electrode 230 and penetrates the insulator layer 210 and the substrate 200 to form a doped region 260 (e.g., a first doped region 250 ) and/or connected to the second doped region 255).

Figure 3 is a diagram showing the structure of a third embodiment of a graphene-based sensor according to the present invention.

Referring to FIG. 3, the graphene-based sensor includes a substrate 300, an insulator layer 310, a graphene layer 320, a first electrode 330, a second electrode 340, and a doped region 360. . The configuration of this embodiment is the same as that of the first embodiment of FIG. 1 except for the first electrode 330. The graphene-based sensor may further include a reaction layer 380.

While the first embodiment of FIG. 1 has a structure in which the first electrode 130 is located on one side of the graphene layer 120, the first electrode 330 of this embodiment has a shape (e.g., surrounding the circumference of the graphene layer 320). For example, a square shape). As another example, the first electrode 230 of the second embodiment may be modified to surround the graphene layer 220 like the first electrode 330 of the present embodiment.

This embodiment shows two via electrodes 370 connected to the second electrode 340, but the number of via electrodes 370 can be varied depending on the embodiment. In another embodiment, the via electrode 370 may be omitted.

Figure 4 is a diagram showing the structure of a fourth embodiment of a graphene-based sensor according to the present invention.

Referring to FIG. 4, the graphene-based sensor includes a substrate 400, an insulator layer 410, a graphene layer 420, a first electrode 430, a second electrode 440, and a doped region 460. . Compared to the first to third embodiments, the present embodiment further includes a plurality of electrodes 432, 434, 470, and 472. For convenience of explanation, this embodiment is illustrated by showing additional electrodes 432, 434, 470, and 472 based on the structure of the first embodiment (structure including via electrodes). However, this embodiment is based on the structure of the second or third embodiment. It can be implemented.

The third electrode 432 and the fourth electrode 434 are formed in contact with the graphene layer 420 at a position spaced apart from the first electrode 430. The third electrode 432 and the fourth electrode 434, together with the first electrode 430, are connected to the bonding error of the first electrode 430 (for example, between the first electrode 430 and the graphene layer 420). The third electrode 432 or the fourth electrode 434 may be used to check a junction error, etc.), or in case of a junction error of the first electrode 430.

The fifth electrode 472 and the sixth electrode 474 penetrate the insulator layer 410 and the substrate 400 and are respectively connected to the second electrode 440 formed below the substrate 400. Accordingly, when a junction error, etc. of the via electrode 470 occurs, the fifth electrode 472 or the sixth electrode 474 can be used. Additionally, it can be confirmed whether the via electrode 470 is normally connected to the second electrode 440 through the via electrode 470 and the fifth electrode 472 (or sixth electrode 474).

Depending on the embodiment, it may include only some of the third to sixth electrodes 432, 434, 470, and 472. For example, it can be modified in various ways, such as including only the third electrode 432 or only the third electrode 432 and the fifth electrode 472.

A sensor array can be created by arranging a plurality of graphene-based sensors of the first to fourth embodiments. The sensor array may all include graphene-based sensors of the same structure, or may include a mixture of the graphene-based sensors of the first to fourth embodiments. However, for convenience of explanation, hereinafter, we will look at a sensor array in which graphene-based sensors all of the same structure are arranged.

Figure 5 is a diagram showing the structure of a first embodiment of a sensor array according to the present invention.

Referring to Figure 5, the sensor array is a structure in which a plurality of graphene-based sensors are arranged in one dimension. The graphene-based sensor constituting the sensor array is based on the structure of the third embodiment of FIG. 3. That is, the graphene-based sensor includes a substrate 500, an insulator layer 510, a graphene layer 520, a first electrode 530, a second electrode 540, and a doped region 560. The sensor array may further include an output terminal 532 that outputs measured values (i.e., electrical signals such as current or voltage) of each graphene-based sensor. The output terminal 532 may correspond to the fourth electrode 434 shown in FIG. 4 .

The second electrodes 540 of the plurality of graphene-based sensors constituting the sensor array are common electrodes connected to each other. In other words, a common electrode 590 may be formed in the vertical direction across a plurality of graphene-based sensors on the back of the substrate 500. This embodiment shows the via electrode 570, but the via electrode 570 may be omitted depending on the embodiment.

The reaction layers 580 of the plurality of graphene-based sensors constituting the sensor array may all include the same material or may include different materials (A1, A2, and A3). For example, a sensor array that detects the wavelength or intensity of light can be implemented by using different quantum dot materials as the reaction layer 580 of each graphene-based sensor. Alternatively, biomolecules with different degrees of reaction to biomolecules (e.g., at least one of DNA, RNA, antigen, antibody, and enzyme) are configured as the reaction layer 580 of each graphene-based sensor to determine the type of biomolecule. and/or a sensor array that detects the quantity may be implemented. For example, the sensor array can distinguish between coronaviruses and avian influenza viruses, or among coronaviruses, SARS-CoV-1 and SARS-CoV-2.

Alternatively, a sensor array that detects the type and/or amount of gas can be implemented by forming the reaction layer 580 of each graphene-based sensor with molecules that react to specific gas molecules to different degrees. Alternatively, the reaction layer 580 of the sensor array can be formed by combining quantum dots, biomolecules, and materials that react to gas molecules, so that light, biomolecules, gas molecules, etc. can be detected simultaneously through one sensor array.

In cases where the materials or structures constituting the reaction layer 580 of each graphene-based sensor of the sensor array are different from each other, the thickness of the insulator layer 510 varies depending on the material of the reaction layer 580 of each graphene-based sensor. It can be implemented at a preset height to prevent mixing.

Figure 6 is a diagram showing the structure of a second embodiment of the sensor array according to the present invention.

Referring to FIG. 6, the sensor array includes a plurality of graphene-based sensors. Each graphene-based sensor constituting the sensor array includes a substrate 600, an insulator layer 610, a graphene layer 620, a first electrode 630, a second electrode 640, and a doped region 660. . The graphene-based sensor of this embodiment is a partially modified structure of the second embodiment of FIG. 2.

The graphene layers 620 of a plurality of graphene-based sensors are connected to each other. In other words, the graphene layer 620 of the sensor array is stacked across a plurality of graphene-based sensors. Therefore, the first electrode 630 connected to the graphene layer 620 is a common electrode for a plurality of graphene-based sensors. In the case of the sensor array of FIG. 5, the second electrode 540 is a common electrode, whereas in this embodiment, the first electrode 630 is a common electrode. The second electrode 640 is present in each graphene-based sensor. Additionally, each graphene-based sensor may have an additional output terminal 642 that outputs measured values.

In another example, the insulator layer 610 may be formed in steps (see section B-B'). This embodiment shows two examples according to the height of the nonconductor layer 610. For example, the area of the insulator layer 610 where the first electrode 630 is present may be formed to be higher than the surrounding area. In this case, the sample can be prevented from flowing out of the sensor array.

Figure 7 is a diagram showing the structure of a third embodiment of a sensor array according to the present invention.

Referring to FIG. 7, the sensor array includes a plurality of graphene-based sensors. Each graphene-based sensor constituting the sensor array includes a substrate 700, an insulator layer 710, a graphene layer 720, a first electrode 730, a second electrode 740, and a doped region 760. .

The doped region 760 is formed to be long in the vertical direction of the substrate 700 across the plurality of graphene-based sensors. For example, when the doped region 760 is composed of a plurality of regions with different concentrations, the second doped region 755 across all of the plurality of graphene-based sensors and the graphene layer of each graphene-based sensor It includes a plurality of first doped regions (750, 751, 752) corresponding to (720).

The second electrode 730 penetrates the insulator layer 710 and is connected to the doped region 760, as in the embodiment of FIG. 2. This embodiment shows a structure including two second electrodes 740 and 742, but the number of second electrodes 740 and 742 may vary depending on the embodiment. Since the second electrode 740 is connected to the doped region 760 that crosses all of the plurality of graphene-based sensors, the second electrode 740 becomes a common electrode for the plurality of graphene-based sensors. The first electrode 730 is present in each graphene-based sensor. It may further include an output terminal 732 that outputs the measured value of each graphene-based sensor.

In another example, the insulator layer 710 may be formed in steps (see section B-B'). This embodiment shows two examples according to the height of the insulator layer 710. For example, the area of the nonconductor layer 610 where the second electrode 740 is present may be formed to be higher than the surrounding area, as in the embodiment of FIG. 6 . In another embodiment, the lower surface of the substrate 700 may further include separate additional electrodes 794 and 798 connected to the doped region 760.

Figures 8 and 9 are diagrams showing the structure of the fourth and fifth embodiments of the sensor array according to the present invention.

Referring to FIGS. 8 and 9, the sensor array includes output units 850 and 950 on substrates 800 and 900. Each graphene-based sensor constituting the sensor array includes a substrate (800,900), an insulator layer (810,910), a graphene layer (820,920), a first electrode (830,930), a second electrode (840,940), and a doping region (860,960). . A reaction layer (880,980) may be further included on the surface of the graphene layer (820,920). The structure of the sensor array in FIG. 8 is a partially modified structure of the structure in FIG. 6, and the structure of the sensor array in FIG. 9 is a partially modified structure of the structure in FIG. 7.

The output units 850 and 950 may be implemented as semiconductor circuits on the substrates 800 and 900. The output units 850 and 950 include terminals 872 and 972 and output terminals 972 and 974 that receive address signals. The output units 850 and 950 are connected to the output terminals 870 and 932 of the plurality of graphene-based elements of the sensor array. When the output units 850 and 950 receive an address signal, they selectively output the input signal (i.e., measured value) through the output terminal of the corresponding graphene-based device among the plurality of graphene-based devices. For example, the output units 850 and 950 may be implemented as a multiplexer circuit. Various embodiments of the output units 850 and 950 are shown in FIGS. 14 to 23.

Figure 10 is a diagram showing the structure of a sixth embodiment of a sensor array according to the present invention.

Referring to Figure 10, the sensor array is a structure in which graphene-based sensors are arranged in the form of n*m (n,m is a natural number of 2 or more). Each graphene-based sensor includes a substrate 1000, a non-conductor layer 1010, a graphene layer 1020, a first electrode 1030, a second electrode 1040, and a doped region 1060. In addition to this, each graphene-based sensor of the sensor array may be implemented in the structure of various embodiments shown in FIGS. 1 to 4.

The graphene layer 1020 is stacked across a plurality of graphene-based sensors in rows. For example, the graphene layers of the three graphene-based sensors located in the first row are connected to each other. Therefore, a common first electrode 1030 exists in each row.

The doping region 1060 exists across a plurality of graphene-based sensors in rows. For example, the doped regions of the three graphene-based sensors located in the first row are connected to each other, the doped regions of the three graphene-based sensors located in the second row are connected to each other, and the doped regions of the three graphene-based sensors located in the third row are connected to each other. The doped areas of the pin-based sensor are connected to each other. Therefore, a common second electrode 1040 exists in each row. For example, as shown in FIG. 7 , a second electrode 1040 passing through the insulator layer 1010 and connected to the doped region 1060 exists in each row.

Measurement values of a desired graphene-based sensor among a plurality of graphene-based sensors can be output through a plurality of first electrodes (1030, 1031, 1032) and a plurality of second electrodes (1040, 1041, 1042). For example, the B2 sensor can be operated by applying a signal to the first electrode in the first row and the second electrode in the second column.

A first multiplexer 1050 and a second multiplexer 1060 may be included to selectively control the operation of each graphene-based sensor and selectively output measured values of each graphene-based sensor. In one embodiment, the first multiplexer 1050 and the second multiplexer 1060 may be implemented as semiconductor circuits on the substrate 1000. The first multiplexer 1050 may be connected to a plurality of second electrodes 1040, 1041, and 1042, and the second multiplexer 1050 may be connected to output terminals (not shown) of a plurality of graphene-based sensors.

For example, when the first multiplexer 1050 receives an address signal, it receives and outputs a signal from the column corresponding to the address signal among the plurality of second electrodes 1040, 1041, and 1042, and the second multiplexer 1060 When an address signal is input, the signal for the corresponding row is input and output.

Figure 11 is a diagram showing the structure of a seventh embodiment of a sensor array according to the present invention.

Referring to FIG. 11, the structure of the plurality of graphene-based sensors 1110 and two multiplexers 1120 and 1140 of the sensor array is the same as that of FIG. 10. This embodiment further includes an amplifier 1140. The amplifier 1140 is connected to the second multiplexer 1130 and amplifies the output signal of the second multiplexer 1130. The sensor array including the amplifier 1140 of this embodiment may be implemented in the form of a chip 110.

Figure 12 is a diagram showing the structure of an eighth embodiment of a sensor array according to the present invention.

Referring to FIG. 12, the sensor array includes a plurality of graphene-based sensors 1260. The structure of the sensor array may be any one of the structures shown in FIGS. 5 to 11. Sensor array 1260 also includes a storage unit 1240 and a channel unit 1250.

The storage portion 1250 is formed by etching the substrate 1200 to a certain depth and a certain width. A plurality of graphene-based sensors are connected to the storage unit 1240 through the channel unit 1250. The channel portion 1250 may be formed by etching a portion of the insulator layer 1210 between the plurality of graphene-based sensors 1260 and the storage portion 1240 to a certain depth and width.

The depth and width of the channel unit 1250 are formed to be smaller than those of the storage unit 1240, so that only substances of a certain size or less among the samples stored in the storage unit 1240 are exposed to a plurality of graphene through the channel unit 1250. It can be transmitted to the base sensor 1260. For example, when the sample is blood, the channel portion 1250 is formed to be less than a certain width (for example, less than 6㎛) so that only substances other than red blood cells and white blood cells are transmitted to the graphene-based sensor 1260. can do.

This embodiment shows a structure that further includes a storage unit 1240 and a channel unit 1250 in the sensor array structure. However, the configuration of the storage unit 1240 and the channel unit 1250 is examined in FIGS. 1 to 4. It can also be applied to single graphene-based sensors.

Using a sensor array, the frequency or intensity of light, bio molecules, gas molecules, or pressure can be detected. For example, the reaction layer located on the surface of the graphene layer of each graphene-based sensor can be composed of antibodies from various viruses or bacteria to detect the type of antigen possessed by the sample. Alternatively, if the reaction layer is composed of antigens made up of specific proteins from various viruses or bacteria, the type of antibody possessed by the sample can be detected. Alternatively, the type of pathogen can be detected by forming a reaction layer with antibodies that react to different degrees to various pathogens. Alternatively, if various enzymes are formed as a reaction layer, various types of substances that react with enzymes can be detected.

In another example, the reaction layer of the graphene-based sensor constituting the sensor array is composed of molecules that react to specific gas molecules to different degrees, so that the type and amount of gas molecules can be detected. In another example, the reaction layer can be made into a structure that responds to pressure, and the intensity of pressure can be detected.

In another example, the reaction layer of a plurality of graphene-based sensors constituting a sensor array is composed of nucleic acid, so that the sequence of nucleic acid can be detected. For example, a sensor array capable of detecting up to 1 base pair can be implemented by making the material of the reaction layer consist of a single-stranded nucleic acid of about 20 base-pairs. For example, a nucleic acid having a complementary sequence to a first nucleic acid having a specific sequence is configured as a reaction layer of the first graphene-based sensor, and a nucleic acid in which some base pairs of the first nucleic acid are mutated. 2 A nucleic acid having a complementary sequence to the nucleic acid can be configured as the reaction layer of the second graphene-based sensor. When a sample with concentrations of the first nucleic acid and the second nucleic acid are C1 and C2, respectively, is input to the sensor array, the measured values (S1, S2) of the sensor array are displayed as follows.

Here, M _nm refers to the size of the signal displayed by the graphene-based sensor when a nucleic acid having a sequence of n is combined with a complementary sequence of a sequence of m. M _nm can be determined experimentally in advance and defined. From the above equation, the concentrations of the first nucleic acid and the second nucleic acid in the sample can be calculated as follows.

In other words, if the measured values (S1, S2) of the sensor array are known, the concentrations of the first and second nucleic acids in the sample can be known. The above example is explained using two types of nucleic acids, but three or more types of nucleic acids can be detected using the same method.

As another example, the concentration of various substances (calcium, sodium, blood sugar, etc.) in the blood can be measured. The reaction layer of the graphene-based sensor that makes up the sensor array can be composed of various enzymes or channels to detect the concentration of ions, blood sugar, etc. in the blood. For example, the reaction layer of the first graphene-based sensor is composed of glucose oxidase, and the reaction layer of the second graphene-based sensor is composed of a potassium channel, so that the glucose concentration in the blood and Potassium concentration can be measured. In addition, the sensor array can be configured to include various enzymatic reactions and various channels to measure the concentration of various substances in the sample at the same time.

Figure 13 is a diagram showing an example of a current change in a graphene-based sensor according to an embodiment of the present invention.

Referring to FIG. 13, the output currents 1300 and 1310 of the graphene-based sensor change depending on the degree of reaction of the reaction layer. Additionally, the output current (1310, 1310) of the graphene-based sensor may vary depending on the type of material constituting the reaction layer. However, since the sensitivity of the output currents (1300, 1310) of the graphene-based sensor of this embodiment is greater than the output currents (1350, 1360) of the conventional transistor-based sensor, changes in minute electrical signals can be accurately measured. By using changes in the output currents 1300 and 1310, the wavelength or intensity of light, the type or amount of bio molecules, the type or amount of gas molecules, or the intensity of pressure, etc. can be detected.

The output current (1300, 1310) of a graphene-based sensor changes on a logarithmic scale, so the measurement range is very wide, making it difficult to measure it accurately. Therefore, Figures 14 to 23 present a method for measuring the output currents 1300 and 1310 of a graphene-based sensor by converting them into a voltage within a certain range (for example, 0 to 10 V, etc.).

Figures 14 and 15 are diagrams showing an example of a measuring device using a graphene-based sensor according to an embodiment of the present invention.

Referring to FIGS. 14 and 15 , the measuring device includes a graphene-based sensor 1410 and an output unit 1420. Graphene-based sensors 1410 and 1510 may be implemented in the form of chips 1400 and 1500. The output units 1420 and 1520 include pre-amplifiers 1430 and 1530 and feedback units 1440 and 1540. Depending on the embodiment, the measuring device may further include control units 1450 and 1550.

The preamplifier units 1430 and 1530 convert the output current of the graphene-based sensors 1410 and 1510 into voltage. In other words, the pre-amplifier units 1430 and 1530 output a voltage within a certain range when receiving current input. In one embodiment, the preamplifier units 1430 and 1530 include an amplifier and a variable resistor, and the output voltage of the amplifier can be adjusted to be within a certain range (for example, 0 to 10 V) by adjusting the variable resistor. The structure of the pre-amplifier units (1430, 1530) of this embodiment is only an example to aid understanding, and various conventional circuit structures that convert current input into voltage and output are used in the pre-amplifier units (1430, 1530) of this embodiment. can be used

The feedback units 1440 and 1540 feedback control the output voltage of the preamplifier units 1430 and 1530 to match the set point of the control unit 1450 and output the output. As seen in Figure 13, the graphene-based sensors (1410, 1510) have different graphs of output currents (1300, 1310) depending on the reaction material of the reaction layer, so the feedback units (1440, 1540) pre-amplify according to the set value. Adjust the output voltage of the units 1430 and 1530. For example, if the range of the output voltage of the preamplifier units (1430, 1530) is 0~10V and the set value is 1V, the feedback units (1440,1540) adjust the output voltage of the preamplifier units (1430, 1530) to 0~1V. Adjust to the value between and output. The feedback units 1440 and 1540 that adjust the range of the output voltage according to the set value may be configured as an analog circuit or implemented as a digital signal processor (DSP). An example of the feedback unit 1440 implemented with DSP is shown in FIG. 16.

The connection between the preamplifier units 1430 and 1530 and the graphene-based sensors 1410 and 1510 may be implemented in various forms. For example, referring to FIG. 14, the (-) terminal of the amplifier may be connected to the output terminal of the graphene-based sensor 1410, and the (+) terminal of the amplifier may be connected to the output of the feedback unit 1440. Referring to FIG. 15, the (-) terminal of the amplifier may be connected to the output terminal of the graphene-based sensor 1510, and the (+) terminal of the amplifier may be connected to ground. At this time, the remaining electrodes of the graphene-based sensor 1510 may be connected to the output of the feedback unit 1540.

Figure 16 is a diagram showing an example of the detailed configuration of a feedback unit and a control unit of a measuring device according to an embodiment of the present invention.

Referring to FIG. 16, the feedback unit 1600 is implemented with DSP. When the feedback unit 1600 receives a voltage in a certain range (first range), it feedback controls the value of the output voltage of the pre-amplifier preamplifiers 1430 and 1530 to fall within a certain range (second range < first range). It is output by adjusting the voltage. Various conventional circuit structures that feedback control the output voltage of the pre-amplifier units 1430 and 1530 to output a voltage within a certain range can be applied to the feedback unit 1600 of this embodiment, and the feedback unit 1600 of this embodiment is It is not limited to structure. The measuring device may further include a display unit 1620, a communication unit 1630, an input unit 1640, etc. along with a control unit 1610.

17 to 23 are diagrams showing various examples of a measuring device using a sensor array according to an embodiment of the present invention.

Referring to FIG. 17, the measurement device includes a plurality of output units (1720, 1722, 1724, 1726) that output measured values from a plurality of graphene-based sensors (1710, 1712, 1714, 1716) included in the sensor array 1700. ) includes. Each output unit 1720, 1722, 1724, and 1726 may be implemented in the structure of FIG. 14 or 15. For example, if the sensor array 1700 includes four graphene-based sensors 1710, 1712, 1714, and 1716, four output units 1720, 1722, 1724, and 1726 are connected to each graphene-based sensor 1710. ,1712,1714,1716). Since an output unit is required equal to the number of graphene-based sensors included in the sensor array 1700, there is a disadvantage in that the size of the measuring device increases.

Referring to Figures 18 and 19, the measurement device includes an output unit to which multiplexers 1800 and 1900 are added. The embodiment of FIG. 17 must include as many output units as the number of graphene-based sensors included in the sensor array 1700, but this embodiment only needs to include one output unit. The multiplexers 1800 and 1900 selectively receive output current from a plurality of graphene-based sensors of the sensor array 1700 and output voltage. FIG. 18 shows a structure that connects the output unit 1420 of FIG. 14 and the multiplexer 1800, and FIG. 19 shows a structure that connects the output unit 1520 of FIG. 15 and the multiplexer 1900.

Referring to Figures 20 and 21, this is a case where the sensor array and multiplexers 2000 and 2100 are implemented on one chip. In this case, the sensor array selectively outputs measured values from a plurality of graphene-based sensors through the multiplexers 2000 and 2100, so the output units 2010 and 2110 may be configured as one. The output unit 2010 of FIG. 20 has the same configuration as the output unit 1420 of FIG. 14, and the output unit 2110 of FIG. 21 has the same configuration as the output unit 1430 of FIG. 15.

Referring to Figures 22 and 23, the sensor array, multiplexers (2210, 2310), and preamplifiers (2220, 2320) are implemented as one chip (2200, 2300). The output units 2230 and 2330 include a feedback unit and a control unit. FIG. 22 is implemented based on the structure of the output unit 1420 of FIG. 14, and FIG. 23 is implemented based on the structure of the output unit 1520 of FIG. 15.

Figure 24 is a diagram showing an example of use of a measuring device according to an embodiment of the present invention.

Referring to FIG. 24, the measurement device 2410 may include a communication unit that can be used by connecting to the smartphone 2400. For example, the measurement device 2410 may be connected to a smartphone through an earphone terminal, charging terminal, or Bluetooth. Various examples of measuring device 2410 are shown in FIGS. 14 to 23.

The smartphone 2400 can display the voltage value received from the measuring device 2410 on the screen or transmit it to an external device. In another embodiment, the smartphone 2400 may analyze the voltage value received from the measuring device 2410 and display the results obtained on the screen or transmit them to an external device. For example, software implementing the analysis method of FIG. 27 may exist in the smartphone 2400.

In another embodiment, information that can identify the measuring device, such as a QR code or barcode, may exist on one side of the measuring device 2410. If a QR code exists in the measuring device 2410, the smartphone 2400 recognizes the QR code through the camera and stores the value of the QR code and the voltage value (or analysis result) of the measuring device 2410 or externally. You can transfer it to your device.

Figures 25 and 26 are diagrams showing an implementation example of a measuring device according to an embodiment of the present invention.

Referring to FIG. 25, the measuring device 2500 can be implemented in a small, portable size. On one side of the measuring device 2500, there is an insertion hole 2510 through which a diagnostic strip can be inserted. The diagnostic strip 2510 is in the form of a thin and long rod, and the diagnostic strip 2510 with the sample on it can be inserted into the insertion hole 2510 of the measuring device.

In one embodiment, the measurement device 2500 may transmit the measured value of the graphene-based sensor to an external device such as a smartphone, as shown in FIG. 24. As another example, the measurement device 2500 may further include an internal circuit that can analyze the measured value of the graphene-based sensor using the method of FIG. 27, etc. Additionally, the measuring device 2500 may further include a display capable of displaying analysis results or a communication unit capable of transmitting analysis results to the outside through a wired or wireless network.

Referring to FIG. 26, the measuring device 2600 includes a plurality of insertion ports 2610 through which a plurality of diagnostic strips can be inserted, and can simultaneously analyze a plurality of diagnostic strips inserted through each insertion port 2610. It includes a plurality of graphene-based sensors. A plurality of graphene-based sensors included in the measuring device 2600 may exist separately for each insertion hole 2610, or may exist in a salpin sensor array structure.

The measurement device 2600 may include an internal circuit capable of analyzing output values of a plurality of graphene-based sensors using the method of FIG. 27, etc. The measuring device 2600 may further include a display unit that displays the analysis results and a communication unit that can transmit the analysis results to an external device.

In another embodiment, quantitative analysis of a sample can be performed using the measuring devices 2500 and 2600 of FIG. 25 or 26. As shown in Figure 25, if there is one insertion port 1510 in the measuring device 2500, a plurality of diagnostic strips containing standard samples of different concentrations and diagnostic strips of specimens are sequentially inserted into the insertion port of the measuring device 2500. By inserting this into (2510), measurements from the graphene-based sensor for each diagnostic strip can be obtained. Alternatively, if there are a plurality of insertion ports 2610 in the measuring device 2600 as shown in FIG. 26, diagnostic strips of standard samples and specimens of a plurality of different concentrations can be simultaneously inserted into the insertion port 1610 of the measuring device 2600. By inserting it, the measurement values of the graphene-based sensor for multiple diagnostic strips can be obtained at once. The measuring devices 2500 and 2600 determine changes in measured values of the graphene-based sensor over time (for example, changes in current or voltage) for standard samples of different concentrations. In addition, the measuring devices 2500 and 2600 can determine the concentration or strength of the sample by comparing the change in the measured value of the standard sample for each concentration with the change in the measured value of the sample.

Figure 27 is a diagram illustrating an example of a method for analyzing measured values of a sensor array using an artificial neural network according to an embodiment of the present invention.

Referring to FIG. 27, the sensor array 2700 outputs measured values 2710 (eg, current or voltage) of a plurality of graphene-based sensors. The structure of the sensor array 2700 is shown in FIGS. 5 to 12. In another embodiment, an output unit (see FIGS. 13 to 23) that converts output current into voltage may be present at the output terminal of the sensor array 2700. However, for convenience of explanation, hereinafter, the measured value output from the sensor array 2700 will be explained assuming that the measured value is a value adjusted to a certain range of voltage values through the salpin output unit in FIGS. 13 to 23. In other words, the range of measurement values are all normalized to values within a certain range.

A plurality of measurement values 2710 output from the sensor array 2700 are input to the artificial neural network 2720. The artificial neural network 2720 can be implemented with various conventional deep learning models such as CNN (Convolutional Neural Network). The artificial neural network 2720 is trained in advance using learning data. For example, information on a standard sample (e.g., wavelength/intensity of light, type/amount of biomolecule, type/amount of gas molecule, etc.) of which a plurality of measurement values 2710 of the standard sample measured using a sensor array are known in advance. The artificial neural network (2720) can be trained using a supervised learning method using learning data labeled with (quantity, intensity of pressure, etc.). When the trained artificial neural network (2720) receives a plurality of measurement values (2710) measured by a sensor array, it produces analysis results (2730) (i.e., wavelength/intensity of light, type/amount of biomolecules, type/amount of gas molecules). amount, intensity of pressure, etc.) are output. Below, we look at various analysis methods.

First, as a first example of an analysis method, we will look at a method of detecting the wavelength or intensity of light using the sensor array 2700 and the artificial neural network 2720. The reaction layers of the plurality of graphene-based sensors of the sensor array 2700 include different quantum dot materials that detect light of different wavelengths. For example, the reaction layer of the plurality of graphene-based sensors constituting the sensor array 2700 may be composed of quantum dots that react to R, G, and B, respectively. When the pre-trained artificial neural network 2720 receives a plurality of measurement values 2710 from the sensor array, it outputs a wavelength in the visible light region as an analysis result. As another example, the reactive layer of a plurality of graphene-based sensors can be composed of quantum dots that detect infrared rays in different wavelength ranges, so that wavelengths in the infrared range can be detected. In this case, the amount of glucose in the blood can be measured through infrared detection on the skin. As another example, different quantum dots capable of detecting different wavelengths in the UV-IR region can be configured as a reaction layer of each graphene-based sensor of the sensor array 2700 to analyze the wavelength of the signal.

As a second example of the analysis method, the type and amount of biomolecules can be detected using the sensor array 2700 and the artificial neural network 2720. The reaction layers of the plurality of graphene-based sensors of the sensor array 2700 may include biomolecules (DNA, RNA, antigen, antibody, enzyme, etc.) with different degrees of reaction. For example, the first antibody of the reaction layer of the first graphene-based sensor of the sensor array 2700 reacts with various viruses (e.g., SARS-CoV-1, SARS-CoV-2, MERS-Cov, etc.) The degree to which the second antibody in the reaction layer of the second graphene-based sensor reacts to various viruses (i.e., the signal of the measured value) may be different. In other words, the degree to which the first antibody reacts to the first virus is r1, the degree to which it reacts to the second virus is r2, and the degree to which the second antibody reacts to the first virus and the second virus is r3 and r4, respectively. can be different.

When the sensor array 2700 outputs a plurality of measurement values 2710 for the sample, the artificial neural network 2720 outputs the type/quantity of the virus as an analysis result. For this purpose, the artificial neural network 2720 is trained in advance using learning data that labels the measurement values 2710 of the sensor array 2700 for various types of viruses with the type and/or amount of the virus. In this way, antibodies with different degrees of response to various viruses constitute the reaction layer of each graphene-based sensor, and the type and amount of the sample can be detected depending on the degree of response of each antibody. Therefore, a greater number of viruses can be detected than the number of graphene-based sensors included in the sensor array 2700. In addition, if the degree of reaction of each antibody in the reaction layer of the graphene-based sensor is different for a new virus, the existing sensor array (2700) can be used as is without the need to recreate the reaction layer of the sensor array (2700) and can be used to react to the new virus. It is possible to detect new viruses by training an artificial neural network with learning data.

As a third example of an analysis method, we will look at a method of diagnosing a disease using a sensor array 2700 and an artificial neural network 2720. A reaction layer is formed on the plurality of graphene-based sensors of the sensor array 2700 with various materials such as DNA, RNA, antigens, antibodies, and enzymes. For example, the first graphene-based sensor of the sensor array 2700 includes a reaction layer of a material capable of detecting DNA, and the second graphene-based sensor includes a reaction layer of a material capable of detecting enzymes. can do. When the artificial neural network 2720 receives a plurality of measurement values 2710 from the sensor array 2700, it predicts a disease and outputs it. In other words, a specific disease can be diagnosed through a combination of various measurements. For this purpose, the artificial neural network 2720 can be trained in advance using learning data in which multiple measurement values obtained by inputting a specimen with a specific disease into the sensor array 2700 are labeled with the name of the disease.

The present invention can also be implemented as computer-readable program code on a computer-readable recording medium. Computer-readable recording media include all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Additionally, computer-readable recording media can be distributed across networked computer systems so that computer-readable code can be stored and executed in a distributed manner.

So far, the present invention has been examined focusing on its preferred embodiments. A person skilled in the art to which the present invention pertains will understand that the present invention may be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an illustrative rather than a restrictive perspective. The scope of the present invention is indicated in the claims rather than the foregoing description, and all differences within the equivalent scope should be construed as being included in the present invention.

100: substrate 110: insulator layer
120: graphene layer 130: first electrode
140: second electrode

Claims (21)

Board;
a doped region formed by doping a partial region of the substrate with an impurity;
a nonconductor layer laminated on the substrate except for a portion of the doped region;
A graphene layer stacked on the doped region;
A first electrode connected to the graphene layer; and
A graphene-based sensor comprising a second electrode connected to the doped region. The method of claim 1, wherein the doping region is:
a first doped region doped at a first concentration; and
It includes a second doped region doped at a second concentration,
the first concentration is less than or equal to the second concentration,
The graphene layer is connected to the first doped region,
The second electrode is a graphene-based sensor, characterized in that connected to the second doped region. According to clause 1,
A graphene-based sensor further comprising a reaction layer located on the surface of the graphene layer and containing a material that induces charges in the graphene layer. The method of claim 1, wherein the substrate is:
A graphene-based sensor characterized by a semiconductor layer containing a semiconductor material other than a two-dimensional material. The method of claim 1, wherein the first electrode is:
A graphene-based sensor, characterized in that it is formed in a shape surrounding the circumference of the graphene layer. The method of claim 1, wherein the second electrode is:
A graphene-based sensor, characterized in that it is located below the substrate facing the graphene layer. According to clause 6,
It further includes at least one via electrode formed to penetrate the insulator layer and the substrate at a position spaced apart from the first electrode,
The second electrode is a graphene-based sensor, characterized in that connected to the at least one via electrode. The method of claim 1, wherein the second electrode is:
A graphene-based sensor, characterized in that it penetrates the insulator layer and is connected to the doped region. According to clause 1,
A graphene-based sensor further comprising a third electrode in contact with the graphene layer at a position spaced apart from the first electrode. According to clause 1,
a storage portion formed by etching the substrate to a first depth and a first width; and
It further includes a channel portion connecting the storage portion and the graphene layer, which is formed by etching the insulator layer to a second depth and a second width,
The second depth and the second width are respectively smaller than the first depth and the first width. According to clause 1,
There are a plurality of doped regions in the substrate,
A graphene-based sensor, wherein the insulator layer is stacked so that a plurality of doped regions are exposed. According to clause 11,
The graphene layers are stacked spaced apart from each other in the plurality of doped regions,
It includes a plurality of first electrodes respectively corresponding to the plurality of doped regions,
The second electrode is a graphene-based sensor, characterized in that it is commonly connected to the plurality of doped regions. According to clause 11,
The graphene layer is stacked over the plurality of doped regions,
It includes a plurality of second electrodes each connected to the plurality of doped regions,
The first electrode is a graphene-based sensor, characterized in that the first electrode is commonly connected to the plurality of doped regions. According to claim 12 or 13,
A graphene-based sensor further comprising a multiplexer that outputs measured values through the graphene layers of the plurality of doped regions. A graphene-based sensor in which a graphene layer is stacked on a doped area formed on a substrate; and
A measuring device comprising: an output unit that converts the output current of the graphene-based sensor into voltage and outputs it. The method of claim 15, wherein the output unit,
a preamplifier that converts the output current of the graphene-based sensor into voltage and outputs it; and
A measuring device comprising a feedback unit that adjusts the output voltage of the pre-amplifier to a certain range. According to clause 15,
a display unit that displays the output voltage of the feedback unit; and
A measuring device further comprising a communication unit that transmits the output voltage of the feedback unit to the outside. According to clause 15,
A sensor array in which the graphene-based sensors are arranged in one or two dimensions; and
Includes a multiplexer connected to a plurality of graphene-based sensors constituting the sensor array,
A measurement device characterized in that the pre-amplifier selectively receives the output current of each graphene-based sensor through the multiplexer. In an analysis method using a sensor array in which graphene-based sensors are arranged in one or two dimensions,
Receiving a plurality of measurement values from a plurality of graphene-based sensors of the sensor array; and
Comprising: acquiring analysis results by inputting the plurality of measured values into an artificial neural network,
The artificial neural network uses learning data that labels the measured value that appears when the sample reacts with the material of the reaction layer of each graphene-based sensor as an analysis result that includes at least one of the type, intensity, amount, or concentration of the sample. An analysis method characterized by learning. According to clause 19,
An analysis method, characterized in that the materials of the reaction layer applied on the graphene layer of the plurality of graphene-based sensors are different materials. According to clause 19,
An analysis method wherein the material of the reaction layer of the plurality of graphene-based sensors includes at least one of quantum dots, biomolecules, molecules that react to gas, and structures that react to pressure.

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