Graphene-based sensor, measuring device using the same, method for measuring a sample, and analysis method

Abstract

A graphene-based sensor, a measuring device using the same, a method for measuring a specimen, and an analysis method are provided. The graphene-based sensor according to an embodiment of the present disclosure includes a substrate, a doping region formed by doping impurities in a partial region of the substrate, an insulator layer laminated on the substrate except for a part of the doping region, a graphene layer laminated on the doping region, a first electrode connected to the graphene layer, and a second electrode connected to the doping region.

Description

Technical Field

[0001] The present disclosure relates to a graphene-based sensor, a measuring device using the same, a method for measuring a specimen, and an analysis method.

Background Art

[0002] Sensors include various types such as biosensors that detect biological substances such as enzymes, antibodies, or DNA, gas sensors that detect the concentration of gases, and pressure sensors that sense pressure. Among them, biosensors are roughly classified into optical, electrochemical, and piezoelectric methods. The optical method is a method that utilizes optical methods such as color development, fluorescence, chemiluminescence, and surface plasmon resonance. The electrochemical method is a method that measures the interaction between an analyte and a bioreceptor as an electrochemical signal such as potential, current, charge amount, conductivity, impedance, etc. The piezoelectric method is a method that measures a change in mass. Electrochemical biosensors have advantages in terms of portability and miniaturization compared to other methods, are possible for both labeled and unlabeled methods, and since the detection signal is an electrical signal, the signal conversion process is easier than optical methods. Electrochemical sensors may be implemented based on transistors in order to accurately measure minute changes in electrical signals.

[0003] Graphene has been actively studied as a new substance to replace semiconductors. Graphene has a hexagonal honeycomb two-dimensional planar crystal structure, is light and thin, and is utilized in various fields due to its excellent durability and conductivity. In recent years, a new device that is functionally a transistor but structurally close to a diode, that is, a barristor, has appeared using graphene.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The technical problem to be solved by the present disclosure is to provide a graphene-based sensor capable of sensing minute changes in electrical signals presented by a specimen.

[0005] Another technical problem to be solved by the present disclosure is to provide a measuring device using a graphene-based sensor.

[0006] <![CDATA[ ]]>Still another technical problem to be solved by the present disclosure is to provide a method of measuring a specimen by applying a voltage to a sensor.

[0007] Still another technical problem to be solved by the present disclosure is to provide a method of analyzing measurement values of a sensor array including a plurality of graphene-based sensors using an artificial neural network.

[0008] The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those of ordinary skill in the art in the technical field of the present disclosure from the description of the specification.

Means for Solving the Problems

[0009] A graphene-based sensor according to an embodiment of the present disclosure may include a substrate, a doping region formed by doping impurities in a part of the region of the substrate, an insulator layer laminated on the substrate except for a part of the doping region, a graphene layer laminated on the doping region, a first electrode connected to the graphene layer, and a second electrode connected to the doping region.

[0010] In one embodiment, the doping region may include a first doping region doped at a first concentration and a second doping 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 doping region, and the second electrode may be connected to the second doping region.

[0011] In one embodiment, the first electrode may be formed in a form surrounding the graphene layer.

[0012] In one embodiment, the second electrode may be located below the substrate facing the graphene layer.

[0013] In one embodiment, it further includes one or more via electrodes formed by penetrating the insulator layer and the substrate at a position separated from the first electrode, and the second electrode may be connected to the one or more via electrodes.

[0014] In one embodiment, the second electrode may penetrate the insulator layer and be connected to the doping region.

[0015] In one embodiment, it may further include a third electrode in contact with the graphene layer at a position separated from the first electrode.

[0016] In one embodiment, it further includes a storage portion formed by etching the substrate with a first depth and a first width, and a channel portion formed by etching the insulator layer with a second depth and a second width to connect the storage portion and the graphene layer, and the second depth and the second width may be smaller than the first depth and the first width, respectively.

[0017] In one embodiment, a plurality of doping regions are arranged separately on the substrate, and the insulator layer may be laminated such that the plurality of doping regions are respectively exposed.

[0018] In one embodiment, the graphene layer is laminated corresponding to the plurality of doping regions, includes a plurality of first electrodes respectively corresponding to the plurality of doping regions, and the second electrode may be commonly connected to the plurality of doping regions.

[0019] In one embodiment, the graphene layer is laminated across the plurality of doping regions, includes a plurality of second electrodes respectively connected to the plurality of doping regions, and the first electrode may be commonly connected to the plurality of doping regions.

[0020] In one embodiment, it may further include a multiplexer that outputs values measured in the graphene layer of the plurality of doping regions.

[0021] In one embodiment, it may further include a reaction layer located on the surface of the graphene layer and containing a substance that induces charge in the graphene layer.

[0022] A graphene-based sensor according to another embodiment of the present disclosure may include a substrate, a two-dimensional semiconductor layer laminated on the substrate, a graphene layer laminated across the two-dimensional semiconductor layer, a first electrode laminated in contact with the two-dimensional semiconductor layer, and a second electrode laminated in contact with the graphene layer.

[0023] In another embodiment, it may include a reaction layer located on the surface of the graphene layer and containing a substance that induces charge in the graphene layer.

[0024] In another embodiment, a plurality of two-dimensional semiconductor layers are arranged separately on the substrate, the graphene layer is laminated across the plurality of two-dimensional semiconductor layers, and a plurality of first electrodes may be respectively located on the plurality of two-dimensional semiconductor layers.

[0025] In another embodiment, some and others of the two-dimensional substances constituting the plurality of two-dimensional semiconductor layers may be different from each other.

[0026] In another embodiment, the plurality of two-dimensional semiconductor layers are arranged in a first direction, and the graphene layer may be arranged in a second direction perpendicular to the first direction.

[0027] In another embodiment, it may further include a third electrode laminated in contact with the two-dimensional semiconductor layer at a distance from the first electrode.

[0028] In another embodiment, it may further include a fourth electrode laminated in contact with the graphene layer at a distance from the second electrode.

[0029] In another embodiment, a plurality of two-dimensional semiconductor layers are arranged radially, the graphene layer is arranged across the plurality of two-dimensional semiconductor layers in a circular band shape, and further includes a third electrode commonly connected to the plurality of two-dimensional semiconductor layers, and a plurality of first electrodes may be respectively connected to the plurality of two-dimensional semiconductor layers.

[0030] A measuring device according to still another embodiment of the present disclosure may include a graphene-based sensor in which a graphene layer is laminated on a doping region formed on a substrate or a two-dimensional semiconductor layer laminated on the substrate, and an output unit that converts the output current of the graphene-based sensor into a voltage and outputs it.

[0031] In still another embodiment, the output unit may include a preamplifier that converts the output current of the graphene-based sensor into a voltage and outputs it, and a feedback unit that adjusts the output voltage of the preamplifier to a certain range.

[0032] In still another embodiment, it may further include a display unit that displays the output voltage of the output unit, and a communication unit that transmits the output voltage of the output unit to the outside.

[0033] In still another embodiment, it includes a sensor array in which the graphene-based sensors are arranged in one dimension or two dimensions, and a multiplexer connected to a plurality of graphene-based sensors constituting the sensor array, and the output unit can selectively receive the output current of each graphene-based sensor through the multiplexer.

[0034] In still another embodiment, it may further include an internal circuit in which a mapping table set corresponding to an artificial neural network that receives a measurement value that appears when a specimen reacts with a substance in the reaction layer of the graphene-based sensor and outputs an analysis value is implemented.

[0035] In yet another embodiment, a voltage application unit that applies a voltage within a predefined range to the graphene-based sensor, and a measurement unit that grasps a first measured value of the resistance or current of the graphene-based sensor due to the application of the voltage before applying the specimen and outputs the grasped second measured value of the resistance or current of the graphene-based sensor due to the application of the voltage after applying the specimen may be further included.

[0036] In yet another embodiment, the measurement unit can output a result value including the maximum difference among the differences in voltage between the first measured value and the second measured value.

[0037] In yet another embodiment, the measurement unit repeatedly performs the process of measuring the second measured value at regular time intervals, and each time it is repeatedly performed, it can output the second measured value of the resistance or current of the sensor due to the application of the voltage.

[0038] In yet another embodiment, the measurement unit can grasp and output the change in the maximum difference between the first measured value and the second measured value each time it is repeatedly performed.

[0039] An analysis method according to yet another embodiment of the present disclosure is an analysis method using a sensor array including a plurality of sensors in which a graphene layer is laminated on a semiconductor region doped on a substrate or a two-dimensional semiconductor, the method including receiving a plurality of measured values from a plurality of graphene-based sensors of the sensor array, and receiving the plurality of measured values with an artificial neural network and obtaining an analysis result using an operation determined in a mapping table set.

[0040] In yet another embodiment, the substances of the reaction layers coated on the graphene layers of the plurality of graphene-based sensors may be partially different from each other.

[0041] In yet another embodiment, the substances of the reaction layers of the plurality of graphene-based sensors may be at least one of quantum dots, biomolecules, molecules that react with gases, and structures that react with pressure.

Advantages of the Invention

[0042] According to the present disclosure, since the graphene-based sensor has a higher reaction sensitivity than a general sensor, it can accurately measure minute changes in electrical signals due to interactions such as antigen-antibody, enzyme, and DNA. In addition, the graphene-based sensor can be applied not only to the bio field but also to various fields for measuring light, gas, pressure, etc.

[0043] According to the present disclosure, a sample can be accurately measured using a sensor. The accuracy of the measurement can be improved based on the difference in the measured values of the sensor before and after the application of the sample.

[0044] According to the present disclosure, the measurement sensitivity of a sample can be improved using a graphene-based sensor.

[0045] The effects according to the technical idea of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those of ordinary skill in the technical field of the present disclosure from the description in the specification.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0047] The various embodiments described in the present disclosure are illustrated for the purpose of clearly explaining the technical idea of the present disclosure and are not intended to limit it to specific embodiments. The technical idea of the present disclosure includes various modifications, equivalents, alternatives, and embodiments selectively combined from all or part of each embodiment described in the present disclosure. Also, the scope of rights of the technical idea of the present disclosure is not limited to the various embodiments presented below or the specific descriptions related thereto.

[0048] Including technical or scientific terms, the terms used in the present disclosure may have meanings generally understood by those having ordinary knowledge in the technical field to which the present disclosure pertains, unless otherwise defined.

[0049] Expressions such as "including", "may include", "comprising", "may comprise", "having", "may have", etc. used in the present disclosure mean that the target features (e.g., functions, operations, or components, etc.) may exist and do not exclude the existence of other further features. That is, such expressions should be understood as open-ended terms that include the possibility of including other embodiments.

[0050] Singular expressions used in the present disclosure may include plural meanings in the context, unless otherwise specified, and this also applies equally to the singular expressions described in the claims.

[0051] As used in this disclosure, expressions such as "first", "second", or "the first", "the second", etc. are used to distinguish one object from other objects when indicating a plurality of homogeneous objects, unless otherwise specified in the context, and do not limit the order or importance between the objects.

[0052] Expressions such as "A, B, and C", "A, B, or C", "at least one of A, B, and C", or "at least one of A, B, or C" used in this disclosure can mean each of the listed items or any possible combination of the listed items. For example, "at least one of A or B" can refer to (1) at least one A, (2) at least one B, and (3) all of at least one A and at least one B.

[0053] The expression "based on" used in this disclosure is used to describe one or more factors that affect the act or operation of decision-making or judgment described in the clause or sentence containing this expression, and this expression does not exclude further factors that affect the act or operation of that decision-making or judgment.

[0054] The expression that one component (for example, the first component) is "connected to" or "coupled to" another component (for example, the second component) as used in this disclosure can mean that the one component is directly connected or coupled to the other component, and can also mean that it is connected or coupled through still another component (for example, the third component).

[0055] As used in this disclosure, the expression "configured to" may, depending on the context, have meanings such as "set to", "capable of", "modified to", "made to", "able to". This expression is not limited to the meaning of "specially designed in hardware". For example, a processor configured to perform a specific operation may mean a general purpose processor capable of executing that specific operation by executing software, or a special purpose computer structured by programming to perform that specific operation.

[0056] As used in this disclosure, the term "component" means a hardware component such as software, an FPGA (Field-Programmable Gate Array), or an ASIC (Application Specific Integrated Circuit). However, "component" is not limited to hardware and software, and may be configured to be in an addressable storage medium or configured to cause one or more processors to execute. Thus, examples of what a "component" can mean include components such as software components, object-oriented software components, class components, and task components, and processors, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Also, the functions provided within a component and a "component" may be combined as fewer components and "components" or further separated into additional components and "components".

[0057] As used herein, the term "Artificial Intelligence (AI)" can refer to technologies that mimic human learning, reasoning, or perception capabilities and implement them on a computer. Specifically, artificial intelligence can make judgments or predictions based on input data by analyzing the input data and generating output data based on the results of the analysis. In addition, artificial intelligence can perform technologies that mimic the cognitive or judgment functions of the human brain (e.g., language understanding, visual understanding, reasoning / prediction, knowledge representation, or motion control). In one embodiment, artificial intelligence may include the concepts of machine learning or symbolic logic.

[0058] As used herein, the term "Machine Learning (ML)" can refer to a series of processes for training an artificial neural network model using the experience of processing data. Through such machine learning, the data processing ability of the artificial neural network model can be improved.

[0059] As used herein, the term "artificial neural network model" is constructed by modeling the correlation relationships between data, and such correlation relationships may be represented by a plurality of parameters. Such an artificial neural network model may be embodied on a computer so as to simulate the human brain structure, for example, the neurons of the human neural network, and include a plurality of network nodes having weight values. By repeating a series of operations of extracting features from given learning data and analyzing the extracted features to derive the correlation relationships between the data, the parameters of the artificial neural network model can be optimized. The process of optimizing the parameters of the artificial neural network model in this way can be referred to as machine learning. Taking a specific example, the artificial neural network model can learn the mapping (correlation relationship) between the input and the output for the data given as an input-output pair, and this can be referred to as supervised learning. Taking another example, even when only input data is given, the artificial neural network model can derive the regularity between the given data and learn the relationship, and this can be referred to as unsupervised learning. In the present disclosure, the artificial neural network model may be referred to as an "artificial neural network", a "model", a "neural network model", an "artificial intelligence learning model", or a "machine learning model".

[0060] Hereinafter, various embodiments described in the present disclosure will be described with reference to the accompanying drawings. In the accompanying drawings and the description thereof, the same or substantially equivalent components may be given the same reference numerals. Also, in the description of the following various embodiments, redundant descriptions regarding the same or corresponding components may be omitted, but this does not mean that the components are not included in the embodiments.

[0061] Figures 1A to 1D show the structure of a first embodiment of a graphene-based sensor according to the present disclosure. Specifically, FIG. 1A is a perspective view of a first embodiment of a graphene-based sensor, FIG. 1B is a plan view of a first embodiment of a graphene-based sensor, FIG. 1C is a cross-sectional view taken along line A-A' shown in FIG. 1A of a first embodiment of a graphene-based sensor, and FIG. 1D is a cross-sectional view taken along line B-B' shown in FIG. 1A of a first embodiment of a graphene-based sensor. The term "FIG. 1" may refer to any one of FIGS. 1A to 1D.

[0062] 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 doping region 160.

[0063] The substrate 100 has a doping region 160 formed by doping impurities. As an example, the substrate may be a semiconductor layer composed of a general semiconductor material such as silicon, germanium, or a compound semiconductor. The doping region 160 may be doped n-type or p-type. As an example, the doping region 160 may be formed of two regions 150, 155 having different doping concentrations from each other. For example, the doping region 160 may include a first doping region 150 doped at a first concentration (e.g., 1*10 8 / cm 3 ~5*10 19 / cm 3 ) and a second doping region 155 doped at a second concentration (>= the first concentration) (e.g., 10 19 / cm 3 or more). As another example, the entire doping region 160 may be doped at the same concentration. However, hereinafter, for the sake of convenience of explanation, the doping region 160 including two regions 150, 155 doped at different concentrations from each other will be shown and described. Each of the area and depth of the doping region 160 can be variously deformed.

[0064] The non-conductive layer 110 is formed on the substrate 100 except for a part of the doping region 160. For example, there is no non-conductive layer 110 on the upper side of a part or the whole of the first doping region 150, and it is exposed on the substrate 100. As an example, the non-conductive layer 110 may be composed of various oxides such as silicon oxide, hafnium oxide, or aluminum oxide.

[0065] The graphene layer 120 composed of graphene is laminated on the doping region 160 of the doping region 160 that is not covered by the non-conductive layer 110. For example, the graphene layer 120 may have a structure laminated over the non-conductive layer 110 and the doping region 160.

[0066] The first electrode 130 is connected to the graphene layer 120. The second electrode 140 is connected to the doping region 160 under the substrate 100. As another example, 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 through the non-conductive layer 110 and the substrate 100 and be connected to the second electrode 140 under the substrate 100. The connection structure of the first electrode 130 and the second electrode 140 in this embodiment is merely an example for helping the understanding of the present invention, and the scope of the present disclosure 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 5 that follow.

[0067] The surface of the graphene layer 120 may further include a reaction layer 180 formed of a substance that reacts with the sample or a structure that senses pressure. Depending on the type of the sample (e.g., light, biological substance, gas, etc.), the type of the substance constituting the reaction layer may vary. For example, a biological substance (such as DNA, antigen, antibody, or enzyme), quantum dots (e.g., lead sulfide quantum dots), or a polymer film may be applied to the graphene layer 120 to form the reaction layer 180, or the reaction layer 180 composed of a structure for measuring pressure may be laminated on the graphene layer 120. As another example, the reaction layer 180 may be composed of a plurality of substances or a plurality of layers. Depending on the type of the substance of the reaction layer 180 located on the surface of the graphene layer 120, the graphene-based sensor of this embodiment may be used in various forms such as an optical sensor that senses light, a biosensor that detects a biological substance, and an environmental sensor that detects gas, temperature, or humidity, etc.

[0068] FIGS. 2A to 2D show the structure of a second embodiment of the graphene-based sensor according to the present disclosure. Specifically, FIG. 2A is a perspective view of the second embodiment of the graphene-based sensor, FIG. 2B is a plan view of the second embodiment of the graphene-based sensor, FIG. 2C is a cross-sectional view taken along the line A-A' shown in FIG. 2A of the second embodiment of the graphene-based sensor, and FIG. 2D is a cross-sectional view taken along the line B-B' shown in FIG. 2A of the second embodiment of the graphene-based sensor. The term "FIG. 2" may refer to any one of FIGS. 2A to 2D.

[0069] 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 doping region 260. The substrate 200, the insulator layer 210, the graphene layer 220, and the first electrode 230 are the same as the structure in FIG. 1. The depth and area of the doping region 260 may be the same as those in the first embodiment of FIG. 1, or a part thereof may be deformed.

[0070] The first electrode 230 is the same as that of the first embodiment in FIG. 1, but the second electrode 240 is different from that of the first embodiment in FIG. 1. The second electrode 240 is formed at a position separated from the first electrode 230 and is connected to the doping region 260 (for example, the first doping region 250 and / or the second doping region 255) through the insulator layer 210 and the substrate 200.

[0071] FIGS. 3A to 3B show the structure of a third embodiment of the graphene-based sensor according to the present disclosure. Specifically, FIG. 3A is a plan view of the third embodiment of the graphene-based sensor, and FIG. 3B is a cross-sectional view of the third embodiment of the graphene-based sensor. The term "FIG. 3" may refer to any one of FIGS. 3A to 3B.

[0072] 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 doping region 360. The configuration of this embodiment is the same as that of the first embodiment in FIG. 1 except for the first electrode 330. The graphene-based sensor may further include a reaction layer 380. <s

[0073] In the first embodiment of FIG. 1, the first electrode 130 is located on one side of the graphene layer 120, but the first electrode 330 of this embodiment is in a form (for example, a rectangular form) surrounding the graphene layer 320. As another embodiment, the first electrode 230 of the second embodiment may be deformed into a form surrounding the graphene layer 220 like the first electrode 330 of this embodiment.

[0074] This embodiment shows two via electrodes 370 connected to the second electrode 340, but the number of the via electrodes 370 can be variously deformed according to the embodiment. As another embodiment, the via electrode 370 may be omitted.

[0075] Figures 4A and 4B show the structure of the fourth embodiment of the graphene-based sensor according to the present disclosure. Specifically, FIG. 4A is a plan view of the fourth embodiment of the graphene-based sensor, and FIG. 4B is a cross-sectional view of the fourth embodiment of the graphene-based sensor. The term "FIG. 4" may refer to either one of FIGS. 4A and 4B.

[0076] 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 doping region 460. Compared with the first to third embodiments, this embodiment further includes a plurality of electrodes 432, 434, 470, 472. For the convenience of description, this embodiment shows and describes the additional electrodes 432, 434, 470, 472 based on the structure of the first embodiment (i.e., the structure including via electrodes), but this embodiment may be implemented based on the structure of the second or third embodiment.

[0077] The third electrode 432 and the fourth electrode 434 are formed in contact with the graphene layer 420 at positions separated from the first electrode 430. The third electrode 432 and the fourth electrode 434 are used to confirm the bonding error of the first electrode 430 (e.g., the bonding error between the first electrode 430 and the graphene layer 420), or the third electrode 432 or the fourth electrode 434 may be used when there is a bonding error in the first electrode 430.

[0078] The fifth electrode 472 and the sixth electrode 474 are respectively connected to the second electrode 440 formed below the substrate 400 through the insulator layer 410 and the substrate 400. Therefore, when a bonding error such as that of the via electrode 470 occurs, the fifth electrode 472 or the sixth electrode 474 can be used. Also, by using the via electrode 470 and the fifth electrode 472 (or the sixth electrode 474), it can be confirmed that the via electrode 470 is properly bonded to the second electrode 440.

[0079] According to the embodiment, it may include only some of the third electrode to the sixth electrodes 432, 434, 470, 472. For example, various modifications are possible, such as including only the third electrode 432 or including only the third electrode 432 and the fifth electrode 472.

[0080] As described above, with reference to FIGS. 1 to 4, an embodiment in which the substrate is implemented with a general semiconductor material has been described. Hereinafter, with reference to FIG. 5, an embodiment in which the substrate is implemented with a two-dimensional material will be described later.

[0081] FIG. 5 shows the structure of a fifth embodiment of a graphene-based sensor according to the present disclosure.

[0082] Referring to FIG. 5, the graphene-based sensor 500 includes a substrate 510, a two-dimensional semiconductor layer 520, a graphene layer 530, a first electrode 540, and a second electrode 550. According to the embodiment, the graphene-based sensor 500 may be implemented including a reaction layer 560 or may be implemented without the reaction layer 560. Hereinafter, for convenience of explanation, a case where the reaction layer 560 is present will be assumed for explanation. In addition, a structure surrounding the periphery of the reaction layer 560 can be created to prevent the sample from protruding outside the reaction layer.

[0083] The two-dimensional semiconductor layer 520 is formed by laminating a two-dimensional material on the substrate 500. For example, the two-dimensional semiconductor layer 520 may be implemented with Si, Ge, WS2, MoS, or the like. In addition, the two-dimensional semiconductor layer 520 may be implemented with various types of two-dimensional materials.

[0084] The graphene layer 530 is formed by laminating graphene over the substrate 510 and the two-dimensional semiconductor layer 520.

[0085] The first electrode 540 is laminated in contact with the two-dimensional semiconductor layer 520, and the second electrode 550 is laminated in contact with the graphene layer 530. That is, the first electrode 540 is connected to the two-dimensional semiconductor layer 520, and the second electrode 550 is connected to the graphene layer.

[0086] The reaction layer 560 is located on the surface of the region where the two-dimensional semiconductor layer 520 and the graphene layer 530 are stacked. The reaction layer 560 contains a substance that induces charge in the graphene layer 530. Depending on the type of the sample (e.g., light, biological substance, gas, etc.), the type of the substance constituting the reaction layer 560 may vary. For example, a biological substance (e.g., DNA, antigen, antibody, or enzyme, etc.), a quantum dot (e.g., lead sulfide quantum dot), or a polymer film, etc. may be coated on the graphene layer 530 to form the reaction layer 560, or the reaction layer 560 composed of a structure for measuring pressure may be laminated on the graphene layer 530. As another example, the reaction layer 560 may be composed of various substances or various layers. Depending on the type of the substance of the reaction layer 560 located on the surface of the graphene layer 530, the graphene-based sensor 500 of this embodiment may be used in various forms such as an optical sensor for sensing light, a biosensor for detecting a biological substance, and an environmental sensor for detecting gas, temperature, or humidity, etc.

[0087] One or more of the graphene-based sensors of the first to fifth embodiments can be arranged to form a sensor array. When there are a plurality of graphene-based sensors included in the sensor array, the sensor array may include graphene-based sensors all having the same structure, or may include a mixture of the graphene-based sensors of the first to fifth embodiments. However, hereinafter, for the sake of convenience of explanation, a sensor array in which the number of graphene-based sensors included in the sensor array is plural and all the graphene-based sensors having the same structure are arranged will be described.

[0088] FIG. 6A and FIG. 6B show the structure of the first embodiment of the sensor array according to the present disclosure. Specifically, FIG. 6A is a plan view of the first embodiment of the sensor array, and FIG. 6B is a cross-sectional view taken along the line A-A' shown in FIG. 6A of the first embodiment of the sensor array. The term "FIG. 6" may refer to either one of FIG. 6A and FIG. 6B.

[0089] Referring to FIG. 6, the sensor array has a structure in which a plurality of graphene-based sensors are arranged in one dimension. The graphene-based sensors constituting the sensor array are based on the structure of the third embodiment in FIG. 3. That is, the graphene-based sensor includes a substrate 600, an insulator layer 610, a graphene layer 620, a first electrode 630, a second electrode 640, and a doping region 660. The sensor array may further include an output terminal 632 that outputs a measured value (for example, an electrical signal such as current or voltage) of each graphene-based sensor. The output terminal 632 may correspond to the fourth electrode 434 described in FIG. 4.

[0090] The second electrodes 640 of the plurality of graphene-based sensors constituting the sensor array are common electrodes connected to each other. In other words, a common electrode 690 in the longitudinal direction crossing the plurality of graphene-based sensors may be formed on the back surface of the substrate 600. Although this embodiment shows via electrodes 670, the via electrodes 670 may be omitted depending on the embodiment.

[0091] The reaction layers 680 of the plurality of graphene-based sensors constituting the sensor array may all contain the same substance or may contain different substances (A1, A2, A3) from each other. For example, by configuring the reaction layers 680 of each graphene-based sensor with different substances of quantum dots, a sensor array capable of detecting the wavelength or intensity of light can be realized. Or, by configuring the reaction layers 680 of each graphene-based sensor with biomolecules (for example, at least one of DNA, RNA, antigen, antibody, enzyme) that react differently with each other, a sensor array capable of detecting the type and / or amount of biomolecules can be realized. For example, the sensor array can distinguish between coronavirus and avian influenza virus, or distinguish between SARS-CoV-1 and SARS-CoV-2 among coronaviruses. Or, by configuring the reaction layers 680 of each graphene-based sensor with molecules that react differently with specific gas molecules, a sensor array capable of detecting the type and / or amount of gas can be realized. Or, by combining substances such as quantum dots, biomolecules, and substances that react with gas molecules to form the reaction layer 680 of the sensor array, light, biomolecules, gas molecules, etc. can be simultaneously detected using one sensor array.

[0092] When substances or structures, etc. constituting the reaction layer 680 of each graphene-based sensor of the sensor array are different from each other, the thickness of the insulator layer 610 may be embodied in a preset height to prevent the substances, etc. of the reaction layer 680 of each graphene-based sensor from mixing with each other.

[0093] Figures 7A to 7D show the structure of a second embodiment of the sensor array according to the present disclosure. Specifically, Figure 7A is a plan view of the second embodiment of the sensor array, Figure 7B is a cross-sectional view taken along the line A-A' shown in Figure 7A of the second embodiment of the sensor array, Figure 7C is an example of a cross-sectional view taken along the line B-B' shown in Figure 7A of the second embodiment of the sensor array, and Figure 7D is another example of a cross-sectional view taken along the line B-B' shown in Figure 7A of the second embodiment of the sensor array. The term "Figure 7" may refer to any one of Figures 7A to 7D.

[0094] 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 doping region 760. The graphene-based sensor of this embodiment is a modification of a part of the structure of the second embodiment in FIG. 2.

[0095] The graphene layers 720 of the plurality of graphene-based sensors are connected to each other. In other words, the graphene layer 720 of the sensor array is laminated across the plurality of graphene-based sensors. Therefore, the first electrode 730 connected to the graphene layer 720 is a common electrode for the plurality of graphene-based sensors. In the sensor array of FIG. 6, the second electrode 640 is the common electrode, whereas in this embodiment, the first electrode 730 is the common electrode. The second electrode 740 exists for each graphene-based sensor. Further, each graphene-based sensor may further have an output terminal 742 for outputting a measured value.

[0096] As yet another embodiment, the insulator layer 710 may be formed in a stepped manner (see FIGS. 7C and 7D). This embodiment shows both two examples according to the height of the insulator layer 710. For example, the region of the insulator layer 710 where the first electrode 730 exists may be formed higher than the periphery (see FIG. 7C). In this case, it is possible to prevent the specimen from protruding outside the sensor array.

[0097] FIGS. 8A to 8D show the structure of a third embodiment of the sensor array according to the present disclosure. Specifically, FIG. 8A is a plan view of the third embodiment of the sensor array, FIG. 8B is a cross-sectional view taken along line A-A' shown in FIG. 8A of the third embodiment of the sensor array, FIG. 8C is an example of a cross-sectional view taken along line B-B' shown in FIG. 8A of the third embodiment of the sensor array, and FIG. 8D is another example of a cross-sectional view taken along line B-B' shown in FIG. 8A of the third embodiment of the sensor array. The term "FIG. 8" may refer to any one of FIGS. 8A to 8D.

[0098] Referring to FIG. 8, the sensor array includes a plurality of graphene-based sensors. Each graphene-based sensor constituting the sensor array includes a substrate 800, an insulator layer 810, a graphene layer 820, a first electrode 830, a second electrode 840, and a doping region 860.

[0099] The doping region 860 is formed long in the longitudinal direction of the substrate 800 across the plurality of graphene-based sensors. For example, when the doping region 860 is composed of a plurality of regions having different concentrations from each other, it includes a second doping region 855 that crosses all of the plurality of graphene-based sensors and a plurality of first doping regions 850, 851, 852 corresponding to the graphene layer 820 of each graphene-based sensor.

[0100] The second electrode 830 is connected to the doping region 860 through the insulator layer 810 as in the second embodiment of FIG. 2. Although this embodiment shows a structure including two second electrodes 840, 842, the number of the second electrodes 840, 842 may be variously deformed according to the embodiment. Since the second electrode 840 is connected to the doping region 860 that crosses all of the plurality of graphene-based sensors, the second electrode 840 serves as a common electrode for the plurality of graphene-based sensors. The first electrode 830 exists for each graphene-based sensor. An output terminal 832 for outputting the measurement value of each graphene-based sensor may be further included.

[0101] As another embodiment, the insulator layer 810 may be formed in a stepped manner (see FIGS. 8C and 8D). This embodiment shows both examples according to the height of the insulator layer 810. For example, the region of the insulator layer 810 where the second electrode 840 exists may be formed higher than the periphery as in the embodiment of FIG. 7C (see FIG. 8C). As still another embodiment, the lower surface of the substrate 800 may further include other additional electrodes 894, 898 connected to the doping region 760.

[0102] Figures 9A and 9B and Figures 10A and 10B respectively show the structures of the fourth and fifth embodiments of the sensor array according to the present disclosure. Specifically, FIG. 9A is a plan view of the fourth embodiment of the sensor array, and FIG. 9B is a cross-sectional view taken along the line A-A' shown in FIG. 9A of the fourth embodiment of the sensor array. Further, FIG. 10A is a plan view of the fifth embodiment of the sensor array, and FIG. 10B is a cross-sectional view taken along the line A-A' shown in FIG. 10A of the fifth embodiment of the sensor array. The term "FIG. 9" may refer to either of FIGS. 9A and 9B, and similarly, the term "FIG. 10" may refer to either of FIGS. 10A and 10B.

[0103] Referring to FIGS. 9 and 10, the sensor array includes output portions 950, 1050 on substrates 900, 1000. Each graphene-based sensor constituting the sensor array includes substrates 900, 1000, insulator layers 910, 1010, graphene layers 920, 1020, first electrodes 930, 1030, second electrodes 940, 1040, and doping regions 960, 1060. The surface of the graphene layers 920, 1020 may further include reaction layers 980, 1080. The structure of the sensor array in FIG. 9 is a structure obtained by deforming a part of the structure in FIG. 7, and the structure of the sensor array in FIG. 10 is a structure obtained by deforming a part of the structure in FIG. 8.

[0104] The output portions 950, 1050 may be implemented by semiconductor circuits on the substrates 900, 1000.

[0105] The output portions 950, 1050 include terminals 972, 1072 for receiving address signals and output terminals 974, 1074, etc. The output portions 950, 1050 are connected to the output terminals 970, 1032 of a plurality of graphene-based elements of the sensor array. When the output portions 950, 1050 receive an address signal, they selectively output a signal (e.g., a measured value) received from the output terminal of the corresponding graphene-based element among the plurality of graphene-based elements. For example, the output portions 950, 1050 may be implemented as multiplexer circuits. Various embodiments of the output portions 950, 1050 are shown in FIGS. 17 to 26.

[0106] Figures 11A and 11B show the structure of the sixth embodiment of the sensor array according to the present disclosure. Specifically, FIG. 11A is a plan view of the sixth embodiment of the sensor array, and FIG. 11B is a cross-sectional view taken along line A-A' shown in FIG. 11A of the sixth embodiment of the sensor array. The term "FIG. 11" may refer to either one of FIGS. 11A and 11B.

[0107] Referring to FIG. 11, the sensor array has a structure in which graphene-based sensors are arranged in the form of n*m (n and m are natural numbers of 2 or more). Each graphene-based sensor includes a substrate 1100, an insulator layer 1110, a graphene layer 1120, a first electrode 1130, a second electrode 1140, and a doping region 1160. In addition, each graphene-based sensor of the sensor array may be implemented by the structures of various embodiments described in FIGS. 1 to 5.

[0108] The graphene layer 1120 is stacked across a plurality of graphene-based sensors in row units. For example, the graphene layers of three graphene-based sensors located in the first row are connected to each other. Therefore, there is a common first electrode 1130 in row units.

[0109] The doping region 1160 exists across a plurality of graphene-based sensors in column units. For example, the doping regions of three graphene-based sensors located in the first row are connected to each other, the doping regions of three graphene-based sensors located in the second row are connected to each other, and the doping regions of three graphene-based sensors located in the third row are connected to each other. Therefore, there is a common second electrode 1140 in column units. For example, as shown in FIG. 8, there is a second electrode 1140 connected to the doping region 1160 through the insulator layer 1110 for each row.

[0110] The measured values of a desired graphene-based sensor among a plurality of graphene-based sensors can be output via a plurality of first electrodes 1130, 1131, 1132 and a plurality of second electrodes 1140, 1141, 1142. For example, a signal can be applied to the first electrode in the first row and the second electrode in the second column to operate the B2 sensor.

[0111] A first multiplexer 1150 and a second multiplexer 1160 may be included to selectively control the operation of each graphene-based sensor and selectively output the measured values of each graphene-based sensor. As an example, the first multiplexer 1150 and the second multiplexer 1160 may be embodied as semiconductor circuits on a substrate 1100. The first multiplexer 1150 may be connected to a plurality of second electrodes 1140, 1141, 1142, and the second multiplexer 1150 may be connected to output terminals (not shown) of a plurality of graphene-based sensors. For example, when the first multiplexer 1150 receives an address signal, it receives and outputs the signal of the column corresponding to the address signal among the plurality of second electrodes 1140, 1141, 1142, and when the second multiplexer 1160 receives an address signal, it receives and outputs the signal of the corresponding row.

[0112] FIG. 12 shows the structure of a seventh embodiment of a sensor array according to the present disclosure.

[0113] Referring to FIG. 12, the structures of a plurality of graphene-based sensors 1210 and two multiplexers 1220, 1240 of the sensor array are the same as those in FIG. 11. This embodiment further includes an amplifier 1240. The amplifier 1240 is connected to the second multiplexer 1230 and amplifies the output signal of the second multiplexer 1230. The sensor array including the amplifier 1240 in this embodiment may be embodied in a chip form.

[0114] Figures 13A and 13B show the structure of the eighth embodiment of the sensor array according to the present disclosure. Specifically, FIG. 13A is a plan view of the eighth embodiment of the sensor array, and FIG. 13B is a cross-sectional view of the eighth embodiment of the sensor array. The term "FIG. 13" may refer to either one of FIGS. 13A and 13B.

[0115] Referring to FIG. 13, the sensor array includes a plurality of graphene-based sensors 1360. The structure of the sensor array may be any one of the structures of FIGS. 6 to 12. Further, the structure of the sensor array may be any one of the structures of FIGS. 14 and 15 described below. Further, the sensor array 1360 includes a storage unit 1340 and a channel unit 1350.

[0116] The storage unit 1340 is formed by etching the substrate 1300 to a certain depth and a certain width. The plurality of graphene-based sensors are connected to the storage unit 1340 via the channel unit 1350. The channel unit 1350 may be formed by etching a part of the insulator layer 1310 between the plurality of graphene-based sensors 1360 and the storage unit 1340 to a certain depth and a certain width.

[0117] The depth and width of the channel unit 1350 are formed to be smaller than the depth and width of the storage unit 1340, so that only substances having a size equal to or less than a certain size among the specimens stored in the storage unit 1340 are transmitted to the plurality of graphene-based sensors 1360 via the channel unit 1350. For example, when the specimen is blood, the channel unit 1350 may be formed to have a width less than a certain width (for example, less than 6 μm), so that only the remaining substances excluding red blood cells and white blood cells are transmitted to the graphene-based sensor 1360.

[0118] This embodiment shows a structure further including a storage unit 1340 and a channel unit 1350 in the sensor array structure, but the configurations of the storage unit 1340 and the channel unit 1350 may also be applied to the single graphene-based sensor described in FIGS. 1 to 5.

[0119] FIG. 14 shows the structure of the ninth embodiment of the sensor array according to the present disclosure.

[0120] Referring to FIG. 14, the sensor array 1400 includes a plurality of sensing regions 1430, 1432, 1434, 1436 in which two-dimensional semiconductor layers 1410, 1412, 1414, 1416 and a graphene layer 1420 are stacked. Each of the plurality of sensing regions 1430, 1432, 1434, 1436 serves as the graphene-based sensor described in FIG. 5.

[0121] The plurality of two-dimensional semiconductor layers 1410, 1412, 1414, 1416 are arranged on the substrate with a space therebetween, and the graphene layer 1420 may be stacked across the plurality of two-dimensional semiconductor layers 1410, 1412, 1414, 1416. For example, the plurality of two-dimensional semiconductor layers 1410, 1412, 1414, 1416 are arranged at equal intervals in a first direction (for example, the vertical direction), and the graphene layer 1420 may be stacked across the plurality of two-dimensional semiconductor layers 1410, 1412, 1414, 1416 in a second direction (for example, the horizontal direction) perpendicular to the first direction. The graphene layer 1420 is commonly connected to the plurality of sensing regions 1430, 1432, 1434, 1436.

[0122] As an example, the plurality of two-dimensional semiconductor layers 1410, 1412, 1414, 1416 may be embodied by the same two-dimensional material or different two-dimensional materials from each other. Alternatively, only some of the two-dimensional semiconductor layers (two or more of 1410, 1412, 1414, 1416) may be embodied by different two-dimensional materials from each other.

[0123] As another example, the sizes of the sensing regions 1430, 1432, 1434, 1436 in which the plurality of two-dimensional semiconductor layers 1410, 1412, 1414, 1416 and the graphene layer 1420 are stacked may be different from each other. For example, the widths of at least two or more of the two-dimensional semiconductor layers 1410, 1412, 1414, 1416 may be embodied to be different from each other. Alternatively, the widths of the graphene layer 1420 corresponding to the sensing regions 1430, 1432, 1434, 1436 may be embodied to be different from each other.

[0124] This embodiment shows only the two-dimensional semiconductor layers 1410, 1412, 1414, 1416 and the graphene layer 1420 for convenience of explanation. However, the structure of each sensing region 1430, 1432, 1434, 1426 may include a structure as shown in FIG. 5. For example, a plurality of first electrodes (not shown) may be respectively connected to the plurality of two-dimensional semiconductor layers 1410, 1412, 1414, 1416, and one second electrode (not shown) may be connected to the graphene layer 1420.

[0125] The reaction layer (not shown) may exist for each of the sensing regions 1430, 1432, 1434, 1436 where the two-dimensional semiconductor layers 1410, 1412, 1414, 1416 and the graphene layer 1420 are stacked. The substances constituting the reaction layer of each sensing region may be the same or different from each other.

[0126] FIG. 15 shows the structure of the tenth embodiment of the sensor array according to the present disclosure.

[0127] Referring to FIG. 15, the sensor array 1500 includes a plurality of sensing regions 1580, 1582, 1584, 1586, 1588, 1590 in which two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, 1520 and a graphene layer 1530 are stacked. The plurality of two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, 1520 are radially arranged on the substrate spaced apart from each other, and the graphene layer 1530 may be stacked over the plurality of two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, 1520 in a circular band shape.

[0128] A plurality of first electrodes 1540, 1542, 1544, 1546, 1548, 1550 are respectively connected to the plurality of two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, 1520, and the second electrode 1570 is connected to the graphene layer 1530.

[0129] The sensor array 1500 may further include a third electrode 1560 commonly connected to a plurality of two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, 1520. Since the plurality of two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, 1520 are arranged radially, the third electrode 1560 may be formed to be commonly connected to the plurality of two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, 1520 inside the radial arrangement. Using the plurality of first electrodes 1540, 1542, 1544, 1546, 1548, 1550 and the third electrode 1560, the state of each two-dimensional semiconductor layer 1510, 1512, 1514, 1516, 1518, 1520 (for example, the presence or absence of a fault, etc.) can be confirmed.

[0130] The sensor array 1500 may further include a fourth electrode 1572 connected to the graphene layer 1530. For example, the graphene layer 1530 is a circular strip with a part open, and the second electrode 1570 and the fourth electrode 1572 may be connected to both ends of the graphene layer 1530 respectively. Using the second electrode 1570 and the fourth electrode 1572, the state of the graphene layer 1530 (for example, a fault state, etc.) can be confirmed.

[0131] Using the plurality of first electrodes 1540, 1542, 1544, 1546, 1548, 1550 and the second electrode 1570 respectively connected to the plurality of two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, 1520, the sensing values of each sensing region 1580, 1582, 1584, 1586, 1588, 1590 can be measured.

[0132] Using the sensor arrays shown in FIGS. 6 to 15 described above, it is possible to detect the frequency and intensity of light, biomolecules, gas molecules, or pressure. For example, by configuring the reaction layer located on the surface of the graphene layer or the sensing region of each graphene-based sensor with antibodies against various viruses and bacteria, it is possible to detect the types of antigens carried by the specimen. Alternatively, by configuring the reaction layer with antigens composed of specific proteins of various viruses and bacteria, it is possible to detect the types of antibodies carried by the specimen. Alternatively, by configuring antibodies that react to various pathogens to different extents as the reaction layer, it is possible to detect the types of pathogens. Alternatively, by configuring various enzymes as the reaction layer, it is possible to detect the types of various substances that react to the enzymes.

[0133] As yet another example, by configuring the reaction layer of the graphene-based sensors that make up the sensor array with molecules that react to specific gas molecules to different extents, it is possible to detect the types and amounts of gas molecules. As yet another example, by configuring the reaction layer as a structure that reacts to pressure, it is possible to detect the intensity of the pressure.

[0134] As yet another example, by configuring the reaction layers of the plurality of graphene-based sensors that make up the sensor array with nucleic acids, it is possible to detect the sequences of the nucleic acids. As an example, by configuring the substance of the reaction layer with single-stranded nucleic acids of around 20 base pairs, a sensor array capable of detecting up to 1 base pair can be realized. For example, a nucleic acid having a complementary sequence to a first nucleic acid having a specific sequence can be configured as the reaction layer of the first graphene-based sensor, and a nucleic acid having a complementary sequence to a second nucleic acid in which a part of the base pairs of the first nucleic acid is mutated can be configured as the reaction layer of the second graphene-based sensor. When a specimen with the concentrations of the first nucleic acid and the second nucleic acid being C1 and C2 respectively is input into the sensor array, the measured values (S1, S2) of the sensor array are displayed as follows.

[0135]

Equation

[0136] Here, M nm means the magnitude of the signal shown by the graphene-based sensor when a nucleic acid having a sequence of n is bound to a sequence of m as a complementary sequence. M nm may be experimentally determined and defined in advance. From the above mathematical formula, the concentrations of the first nucleic acid and the second nucleic acid in the sample can be determined as follows.

[0137]

Equation

[0138] That is, if the measurement values (S1, S2) of the sensor array are known, the concentrations of the first nucleic acid and the second nucleic acid in the sample can be known. Although the above example is described using two types of nucleic acids, three or more types of nucleic acids can also be detected by the same method.

[0139] Furthermore, as another example, the concentrations of various substances (e.g., calcium, sodium, blood glucose, etc.) in blood can be measured. By configuring the reaction layer of the graphene-based sensors constituting the sensor array with various enzymes or channels, the concentrations of ions, blood glucose, etc. in blood can be detected. 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 and potassium concentration in blood can be measured. In addition, the sensor array can be configured to include various enzyme reactions and various channels, and the concentrations of various substances in the sample can be measured at once.

[0140] Figures 16A and 16B show an example of the current change of the graphene-based sensor according to the present disclosure. Specifically, Figure 16A shows the current change of the graphene-based sensor, and Figure 16B shows the current change of the transistor-based sensor. The term "Figure 16" may refer to any one of Figures 16A and 16B.

[0141] Referring to FIG. 16, the output currents 1600, 1610 of the graphene-based sensor vary depending on the degree of reaction of the reaction layer. Also, the output currents 1610, 1610 of the graphene-based sensor may vary depending on the type of substance constituting the reaction layer. However, compared to the output currents 1650, 1660 of the conventional transistor-based sensor, the sensitivity of the output currents 1600, 1610 of the graphene-based sensor in this embodiment is greater, so that minute changes in electrical signals can be accurately measured. Using the changes in the output currents 1600, 1610, it is possible to detect the wavelength and intensity of light, the type and amount of biomolecules, the type and amount of gas molecules, or the intensity of pressure, etc.

[0142] Since the output currents 1600, 1610 of the graphene-based sensor change on a logarithmic scale, the measurement range is too wide and it is difficult to accurately measure it. Therefore, in FIGS. 17 to 26, a method and an apparatus for measuring by converting the output currents 1600, 1610 of the graphene-based sensor into a voltage within a certain range (for example, 0 to 10 V, etc.) are presented.

[0143] FIGS. 17 and 18 show various examples of a measuring apparatus using the graphene-based sensor according to the present disclosure.

[0144] Referring to FIGS. 17 and 18, the measuring apparatus includes graphene-based sensors 1710, 1810 and output units 1720, 1820. The graphene-based sensors 1710, 1810 may be embodied in the form of chips 1700, 1800. The output units 1720, 1820 include pre-amplifiers 1730, 1830 and feedback units 1740, 1840. Depending on the embodiment, the measuring apparatus may further include control units 1750, 1850.

[0145] The pre-amplification units 1730 and 1830 convert the output currents of the graphene-based sensors 1710 and 1810 into voltages. In other words, when receiving a current, the pre-amplification units 1730 and 1830 output voltages within a certain range. As an example, the pre-amplification units 1730 and 1830 include an amplifier and a variable resistor, and the output voltage of the amplifier can be adjusted to a certain range (for example, 0 to 10 V) by adjusting the variable resistor. The structure of the pre-amplification units 1730 and 1830 in this embodiment is merely an example for aiding understanding, and various conventional circuit structures that convert a current input into a voltage and output it may be used for the pre-amplification units 1730 and 1830 in this embodiment.

[0146] The feedback units 1740 and 1840 perform feedback control on the output voltages of the pre-amplification units 1730 and 1830 according to the set points of the control units 1750 and 1850 and output the results. As described in FIG. 16, since the graphene-based sensors 1710 and 1810 have different graphs of output currents 1600 and 1610 due to the reactants in the reaction layer, the feedback units 1740 and 1840 adjust the output voltages of the pre-amplification units 1730 and 1830 according to the set points. For example, if the range of the output voltages of the pre-amplification units 1730 and 1830 is 0 to 10 V and the set point is 1 V, the feedback units 1740 and 1840 adjust the output voltages of the pre-amplification units 1730 and 1830 to values within the range of 0 to 1 V and output them. The feedback units 1740 and 1840 that adjust the range of the output voltage according to the set point may be composed of an analog circuit or implemented by a DSP (Digital Signal Processor). An example of a feedback unit implemented by a DSP is shown in FIG. 19.

[0147] The connection between the pre-amplification units 1730 and 1830 and the graphene-based sensors 1710 and 1810 may be implemented in various forms. For example, referring to FIG. 17, the (-) terminal of the amplifier may be connected to the output terminal of the graphene-based sensor 1710, and the (+) terminal of the amplifier may be connected to the output of the feedback unit 1740. Referring to FIG. 18, the (-) terminal of the amplifier may be connected to the output terminal of the graphene-based sensor 1810, and the (+) terminal of the amplifier may be connected to the ground. At this time, the remaining electrodes of the graphene-based sensor 1810 may be connected to the output of the feedback unit 1840.

[0148] FIG. 19 shows an example of the detailed configuration of the feedback unit and the control unit of the measuring device according to the present disclosure.

[0149] Referring to FIG. 19, the feedback unit 1900 is implemented as a DSP. When the feedback unit 1900 receives a voltage within a certain range (for example, the first range), it feedback-controls the value of the output voltage of the aforementioned pre-amplification units 1730 and 1830 and adjusts it to a voltage within a certain range (for example, the second range < the first range) and outputs it. Various conventional circuit structures for feedback-controlling the output voltage of the pre-amplification units 1730 and 1830 and outputting it as a voltage within a certain range may be applied to the feedback unit 1900 of this embodiment, and the feedback unit 1900 is not limited to the structure of this embodiment. The measuring device may further include a display unit 1920, a communication unit 1930, an input unit 1940, etc. together with the control unit 1910.

[0150] FIGS. 20 to 26 show various examples of the measuring device using the sensor array according to the present disclosure.

[0151] Referring to FIG. 20, the measuring device includes a plurality of output units 2020, 2022, 2024, 2026 that output measurement values of a plurality of graphene-based sensors 2010, 2012, 2014, 2016 included in the sensor array 2000. Each output unit 2020, 2022, 2024, 2026 may be implemented by the structure of FIG. 17 or FIG. 18. For example, when the sensor array 2000 includes four graphene-based sensors 2010, 2012, 2014, 2016, four output units 2020, 2022, 2024, 2026 are connected to each graphene-based sensor 2010, 2012, 2014, 2016. Since the number of output units is required to be the same as the number of graphene-based sensors included in the sensor array 2000, the size of the measuring device increases according to that number.

[0152] Referring to FIGS. 21 and 22, the measuring device includes output units to which multiplexers 2100, 2200 are added. The embodiment of FIG. 20 needs to include the same number of output units as the number of graphene-based sensors included in the sensor array 2000, but this embodiment only needs to include one output unit. The multiplexers 2100, 2200 selectively receive output currents from a plurality of graphene-based sensors of the sensor array 2000 and output voltages. FIG. 21 shows a structure in which the configuration of the output unit 1720 of FIG. 17 is connected to the multiplexer 2100, and FIG. 22 shows a structure in which the configuration of the output unit 1820 of FIG. 18 is connected to the multiplexer 2200.

[0153] Referring to FIGS. 23 and 24, this is the case where the sensor array and the multiplexers 2300, 2400 are implemented as one chip. In this case, since the sensor array selectively outputs the measurement values of a plurality of graphene-based sensors via the multiplexers 2300, 2400, the output units 2310, 2410 may be configured as one. The output unit 2310 in FIG. 23 is the same as the output unit 1720 in FIG. 17, and the output unit 2110 in FIG. 24 has the same configuration as the output unit 1830 in FIG. 18.

[0154] Referring to FIGS. 25 and 26, this is the case where the sensor array, multiplexers 2510, 2610, and pre-amplification units 2520, 2620 are embodied as one chip 2500, 2600. Output units 2530, 2630 include a feedback unit and a control unit. FIG. 25 is embodied based on the structure of the output unit 1820 in FIG. 18, and FIG. 26 is embodied based on the configuration of the output unit 1720 in FIG. 17.

[0155] FIG. 27 shows a block diagram of a measuring device according to the present disclosure.

[0156] Hereinafter, a sensor measuring device 2700 will be described as an example of the measuring device. The sensor measuring device 2700 may be embodied as a computing device that performs a computing function.

[0157] Referring to FIG. 27, the sensor measuring device 2700 includes a voltage application unit 2702 and a measurement unit 2704. The sensor measuring device 2700 may be embodied in various forms, and FIGS. 17 to 26 may be referred to as examples. The sensor 2710 can output currents or resistances that are different from each other depending on the type of specimen. Various sensors 2710 may be used in this embodiment. As another example, a graphene-based sensor can be used to increase the sensitivity of the sensor. Regarding the graphene-based sensor, FIGS. 1 to 15 and FIGS. 17 to 26 may be referred to as examples.

[0158] The voltage application unit 2702 applies at least one voltage within a predefined voltage range to the sensor 2710. For example, the voltage application unit 2702 inputs voltages in the range of 0 to 2V to the sensor 2710 at regular intervals (for example, at intervals of 0.1V). The voltage range and the voltage application interval may be variously modified depending on the embodiment.

[0159] The measurement unit 2704 measures the resistance or current of the sensor 2710 by applying a voltage. The measurement unit 2704 grasps the resistance or current of the voltage-specific sensor. For example, before applying the specimen, the measurement unit 2704 grasps the measured value of the resistance or current of the sensor 2710 by applying a voltage, and after applying the specimen, the measurement unit 2704 grasps the measured value of the resistance or current of the sensor 2710 by applying a voltage. The measurement method using such a sensor measurement device 2700 will be described later with reference to FIGS. 30 to 33.

[0160] The measurement unit 2704 can display the measurement result via a screen interface, output whether the specimen is recognized by voice or the like, or transmit the measurement result to an external device via a wired or wireless communication network. The types of information displayed or transmitted by the measurement unit 2704 can be variously modified according to the embodiments.

[0161] FIGS. 28 and 29 show exemplary embodiments of the measurement device according to the present disclosure.

[0162] Referring to FIG. 28, the measurement device 2800 may be embodied in a small portable size, and an insertion port 2810 into which a diagnostic strip can be inserted exists on one side of the measurement device 2800. The diagnostic strip 2810 is thin and long in a rod shape, and the diagnostic strip 2810 with the specimen may be inserted into the insertion port 2810 of the measurement device 2800.

[0163] As an example, the measurement device 2800 can transmit the measured value of the graphene-based sensor to an external device such as a smartphone. This will be described later with reference to FIG. 34 as an exemplary use of the measurement device. As another example, the measurement device 2800 may further include an internal circuit capable of analyzing the measured value of the graphene-based sensor. This will be described later with reference to FIGS. 35 and 36. Further, the measurement device 2800 may further include a display capable of displaying the analysis result or a communication unit capable of transmitting the analysis result or the like externally via a wired or wireless network.

[0164] Referring to FIG. 29, the measuring device 2900 includes a plurality of insertion ports 2910 into which a plurality of diagnostic strips can be inserted, and includes a plurality of grapheme-based sensors that can simultaneously analyze the plurality of diagnostic strips inserted from each insertion port 2910. The plurality of grapheme-based sensors included in the measuring device 2900 may exist separately for each insertion port 2910 or may exist in the sensor array structure described above.

[0165] The measuring device 2900 may include an internal circuit that can analyze the output values of the plurality of grapheme-based sensors. Similarly, this will be described later with reference to FIGS. 35 and 36. The measuring device 2900 may further include a display unit that displays the analysis result and a communication unit for transmitting the analysis result to an external device.

[0166] As another example, quantitative analysis of a sample can be performed using the measuring devices 2800 and 2900 of FIG. 28 or FIG. 29. If there is one insertion port 1810 in the measuring device 2800 as shown in FIG. 28, a plurality of diagnostic strips of standard samples with different concentrations and the diagnostic strip of the sample are sequentially inserted into the insertion port 2810 of the measuring device 2800, and the measured values of the grapheme-based sensors for each diagnostic strip can be obtained. Or, if there are a plurality of insertion ports 2910 in the measuring device 2900 as shown in FIG. 29, the diagnostic strips of standard samples with different concentrations and the diagnostic strip of the sample are simultaneously inserted into the insertion ports 2910 of the measuring device 2900, and the measured values of the grapheme-based sensors for the plurality of diagnostic strips can be obtained at once. The measuring devices 2800 and 2900 grasp the change in the measured values of the grapheme-based sensors over time (for example, the change in current or voltage) for standard samples with different concentrations. Then, the measuring devices 2800 and 2900 compare the change in the measured values of the standard samples by concentration with the change in the measured values of the sample, and can grasp the concentration or intensity of the sample.

[0167] FIG. 30 shows a flowchart showing a method for measuring a sample according to the present disclosure.

[0168] The method for measuring a sample described with reference to FIGS. 30 to 33 will be described as a method performed by the sensor measurement device 2700 of FIG. 27 for the sake of convenience of explanation.

[0169] Referring to FIG. 30, the sensor measurement device 2700 applies a voltage in a certain range to the sensor 2710 before applying the sample (S3000). For example, the sensor measurement device 2700 can apply a voltage in the range of 0 to 2V to the sensor at regular intervals and at regular times.

[0170] As another example, the sensor measurement device 2700 can apply a voltage after applying a buffer solution to the sensor 2710. Examples of buffer solutions include PBS (Phosphate Buffered Saline), UTM (Universal Transport Medium), PBST (PBS + Tween-20), ADA (C6H 10 N2O5), AMP (C4H 11 NO), BES (C6H 15 NO5S), Bicine (C6H 13 NO4), BIS-TRIS (C8H 19 NO5), Bis-Tris Propane (C 11 H 26 N2O6), boric acid (H3BO3), CAPS (C6H 11 NH(CH2)3SO3H), sodium CAPSO salt (C9H 18 NO4SNa), CHES (C8H 17 NSO3), DIPSO (C7H 17 NO6S), sodium DIPSO monosodium salt (C7H 16 NO6SNa), EDTA, glycine-HCI, HEPBS (C 10 H 22 N2O4S), HEPES free acid (C8H 18 N2O4S), sodium HEPES salt (C8H 17 N2O4SNa), HEPPS (C9H 20 N2O4S), HEPPSO free acid (C9H 20 N2O5S), sodium HEPPSO salt (C9H 19N2O5SNa), MES free acid monohydrate (C6H 13 NO4S·H2O), MES sodium salt (C6H 12 NNaO4S), MOPS free acid (C7H 15 NO4S), MOPS sodium salt (C7H 14 NO4SNa), MOPSO free acid (C7H 15 NO5S), MOPSO sodium salt (C7H 14 NO5SNa), PIPES free acid (C8H 18 N2O6S2), PIPES sodium salt (C8H 16.5 N2Na 1.5 O6S2), POPSO free acid (C 10 H 22 N2O8S2), POPSO disodium salt (C 10 H 20 N2O8S2Na2), Satellite free Ampicillin, SDS (Sodium dodecyl sulfate)-urea-Tris solution, sodium acetate, SSC, SSPE, TAE, TAPS free acid (C7H 17 NO6S), TAPS sodium salt (C7H 17 NO6SNa), TAPSO free acid (C7H 17 NO7S), TAPSO sodium salt (C7H 16 NO7SNa), TBS (TRIS-BUFFER SALINE), TBST (TBS-Tween 20), TE buffer (Tris-EDTA), TES free acid (C6H 15 NO6S), TG buffer (TRIS-GLYCINE), TG-SDS buffer (TRIS-GLYCINE-SDS), Tris (C4H 11 NO3), Tris-HCl, Tris hydrochloride (C4H 11 NO3·HCl), Tris-(hydroxymethyl)propane (C6H 14There are O3), TT-SDS (Tris-Tricine-SDS buffer), TTE (Tris-TAPS-EDTA Buffer), etc. The buffer solution may be omitted depending on the embodiment. An example of a method for applying the buffer solution 3110 to the sensor 3100 is shown in FIG. 31.

[0171] The sensor measurement device 2700 measures the resistance or current of the sensor 2710 by applying a voltage (S3010). Hereinafter, the resistance or current of the sensor 2710 measured before applying the sample is referred to as the first measurement value. The sensor measurement device 2700 grasps the first measurement values for each voltage within a certain voltage range. For example, when a voltage is applied at intervals of 0.1 V in the range of 0 to 2 V, the sensor measurement device 2700 grasps a plurality of first measurement values indicating the resistance or current of the sensor for each voltage such as 0.1 V, 0.2 V,... 2 V.

[0172] Apply the sample to the sensor 2710 (S3020). As an example, when a buffer solution is applied to the sensor 2710, the sample is applied to the sensor 2710 in a state where the buffer solution is applied. The sensor measurement device 2700 applies a voltage within a certain range to the sensor after applying the sample (S3030). The voltage applied to the sensor 2710 for the first time before applying the sample and the voltage applied to the sensor 2710 for the second time after applying the sample may be voltages under the same conditions. For example, when the voltage applied for the first time is a voltage in units of 0.1 V in the range of 0 to 2 V, the voltage applied for the second time may also be a voltage in units of 0.1 V in the range of 0 to 2 V.

[0173] The sensor measurement device 2700 measures the resistance or current of the sensor 2710 by applying a voltage (S3040). Hereinafter, the resistance or current of the sensor 2710 measured after applying the specimen is referred to as the second measurement value. The sensor measurement device 2700 grasps the difference between the first measurement value before applying the specimen and the second measurement value after applying the specimen (S3050). That is, the sensor measurement device 2700 grasps the difference between the first measurement value and the second measurement value for each voltage. For example, if there are a plurality of first measurement values measured by applying 0.2V and 0.5V before applying the specimen and a plurality of second measurement values measured by applying 0.2V and 0.5V after applying the specimen, the sensor measurement device 2700 compares the first measurement value for 0.2V before applying the specimen with the second measurement value for 0.2V after applying the specimen, and compares the first measurement value for 0.5V before applying the specimen with the second measurement value for 0.5V after applying the specimen. The method of comparing the measurement values before and after applying the specimen will be described later with reference to FIG. 32.

[0174] As an example, the sensor measurement device 2700 can output the first measurement value and the second measurement value to the screen or transmit them to an external device. As another example, the sensor measurement device 2700 can output the difference between the first measurement value and the second measurement value to the screen or transmit it to an external device.

[0175] As yet another example, the sensor measurement device 2700 can output the maximum difference between the first measurement value and the second measurement value to the screen or transmit it to an external device. For example, if the difference between the first measurement value and the second measurement value for a 0.2V voltage is A and the difference between the first measurement value and the second measurement value for a 0.5V voltage is B (>A), the sensor measurement device 2700 can output B, which has a relatively large difference.

[0176] As yet another example, the sensor measurement device 2700 can repeatedly perform the process of measuring the second measurement value. For example, the sensor measurement device 2700 can input a voltage in the range of 0 to 2V at regular intervals to the sensor 2710 at time t1 to obtain a plurality of second measurement values at time t1. The sensor measurement device 2700 can input a voltage in the range of 0 to 2V at regular intervals to the sensor 2710 at time t2 to obtain a plurality of second measurement values at time t2. A plurality of second measurement values at time tn can be obtained in such a manner. The number of times of repeatedly measuring the second measurement value and the repetition measurement interval (i.e., t2 - t1) can be variously modified according to the embodiment.

[0177] The sensor measurement device 2700 can grasp the maximum difference by comparing a plurality of second measurement values measured at time t1 with the previously measured first measurement value. Further, the sensor measurement device 2700 can grasp the maximum difference by comparing a plurality of second measurement values measured at time t2 with the first measurement value. The maximum difference can be grasped by comparing a plurality of second measurement values measured at time tn with the first measurement value in such a manner. For example, at time t1, the difference between the first measurement value measured at 0.5V and the second measurement value may be the largest, and at time t2, the difference between the first measurement value measured at 0.2V and the second measurement value may be the largest. That is, the applied voltages at which the maximum difference between the first measurement value and the second measurement value appears at each measurement time (t1, t2,... tn) may be different from each other.

[0178] As one example, the sensor measurement device 2700 can obtain the difference between the first measurement value and the second measurement value as follows.

[0179]

Equation

[0180] Here, if the value measured by the sensor measurement device 2700 from the sensor 2710 is current, the first measurement value and the second measurement value are the current values of the sensor. If the value measured by the sensor measurement device 2700 from the sensor 2710 is resistance, the first measurement value and the second measurement value are the resistance values of the sensor.

[0181] In this embodiment, by grasping the first measurement value measured before applying the sample to the sensor 2710 and the second measurement value measured after applying the sample to the sensor, the influence of the sample on the sensor can be accurately measured. As another embodiment, the sensor measurement device 2700 can more accurately measure the sample by using the change in the maximum difference between the second measurement values obtained at each time point (t1, t2,... tn) after repeatedly measuring the second measurement value at regular time intervals and the first measurement value before applying the sample.

[0182] The sensor measurement device 2700 can output the difference between the second measurement value grasped at each time point and the first measurement value measured before applying the sample to the screen or transmit it to an external device. As another embodiment, the sensor measurement device 2700 can output the maximum difference among the differences between the second measurement values grasped at each time point and the first measurement value measured before applying the sample to the screen or transmit it to an external device. For example, the sensor measurement device 2700 can display the maximum difference in numbers or graphs (such as bar graphs or line graphs, etc.). As another embodiment, the sensor measurement device 2700 can output the result of identifying the sample based on the change in the maximum difference to the screen or transmit it to an external device. As yet another embodiment, the sensor measurement device 2700 can output various values such as the applied voltage at each measurement time point when the maximum difference appears together.

[0183] FIG. 32 shows an example of a method for measuring a sample according to the present disclosure.

[0184] Referring to FIG. 32, the sensor measurement device 2700 applies a voltage to the sensor before applying the sample 3200 and grasps the first measurement value 3210 including the current or resistance of the sensor. As one embodiment, the sensor measurement device 2700 can grasp the first measurement value 3210 by applying at least one voltage to the sensor. For example, the sensor measurement device 2700 can grasp a plurality of first measurement values 3210 by applying voltages at regular intervals within the range of 0 to 2V.

[0185] The sensor measurement device 2700 applies a voltage to the sensor at 3220 after the specimen is applied, and grasps a second measurement value including the current or resistance of the sensor. The voltage input to the sensor after the specimen is applied is the same as the voltage input to the sensor before the specimen is applied. The sensor measurement device 2700 can repeatedly perform the process of measuring the second measurement value. For example, the sensor measurement device 2700 applies a voltage at time t1 to grasp the second-1 measurement value 3230. Also, the sensor measurement device 2700 grasps the second-2 measurement value 3232 to the second-N measurement value 3234 at times t2 to tn, respectively.

[0186] The sensor measurement device 2700 grasps the difference between the first measurement value 3210 and the second measurement value for each voltage. For example, assume that both the first measurement value 3210 and the second measurement value are measured for voltages V1, V2, and V3. The sensor measurement device 2700 compares the first measurement value 3210 with the second measurement value at time t1 (i.e., the second-1 measurement value 3230) for each voltage. If the difference between the first measurement value 3210 measured by applying V2 at time t1 and the second-1 measurement value 3230 is the largest, the sensor measurement device 2700 grasps the difference between the first measurement value 3210 at V2 and the second-1 measurement value 3230 as the maximum difference 3240 at time t1. In this way, the difference between the first measurement value 3210 and the second-2 measurement value 3232 for each voltage at time t2 is compared to grasp the maximum difference 3242, and the difference between the first measurement value 3210 and the second-N measurement value 3234 for each voltage at time tn is compared to grasp the maximum difference 3244.

[0187] FIG. 33 shows an example of measurement results that can be referred to in various embodiments of the present disclosure.

[0188] Referring to FIG. 33, a first graph 3300 showing the result of measuring the current by applying a voltage within a certain range to the sensor before the specimen is applied, and a second graph 3310 showing the result of measuring the current by applying a voltage within a certain range to the sensor after the specimen is applied are shown. The graphs 3300 and 3310 show the sensor measurement values for each voltage at regular intervals connected in a curve form. Also, the graph of this embodiment is merely an example for assisting understanding, and the sensor measurement results may be displayed in various graphs or numbers, etc.

[0189] As an example, when the process of grasping the second measurement value as shown in FIG. 32 is repeatedly performed, there may be a plurality of second graphs 3310. That is, there may be a second-1 graph showing the second-1 measurement value 3230 of the sensor measured at time t1, a second-2 graph showing the second-2 measurement value 3232 of the sensor measured at time t2, and a second-N graph showing the second-N measurement value 3234 of the sensor measured at time tn. However, in this embodiment, only one graph is shown for convenience of explanation.

[0190] The sensor measurement device 2700 can grasp the difference in the sensor measurement values by voltage between the first graph and the second graph using Mathematical Formula 3. The difference in the measurement values is not limited to Mathematical Formula 3 and can be obtained by various methods. The difference between the first graph 3300 and the second graph 3310 is shown in the third graph 3320. For example, referring to FIG. 32, there may be a graph 3320 showing the difference between the second-1 measurement value 3230 and the first measurement value 3210, and a graph 3330 showing the difference between the second-2 measurement value 3232 and the first measurement value 3210, respectively. The specimen can be identified based on the change in the positions of the maximum value points 3332 and 3342 in both graphs 3330 and 3340.

[0191] FIG. 34 shows an exemplary use of the measurement device according to the present disclosure.

[0192] Referring to FIG. 34, the measurement device 3410 may include a communication unit that can be connected to the smartphone 3400 for use. For example, the measurement device 3410 may be connected to the smartphone via a headphone jack, a charging terminal, or Bluetooth (registered trademark), etc. Various examples of the measurement device 3410 are shown in FIGS. 17 to 29.

[0193] The smartphone 3400 can display the voltage value received from the measuring device 3410 on the screen or transmit it to an external device. As another example, the smartphone 3400 can display on the screen or transmit to an external device the result obtained by analyzing the voltage value received from the measuring device 3410. For example, software implementing the analysis methods shown in FIGS. 35 to 36 described below may be present in the smartphone 3400.

[0194] As another example, on one side of the measuring device 3410, information capable of identifying the measuring device, such as a QR code (registered trademark) or a barcode, may be present. When a QR code (registered trademark) is present on the measuring device 3410, the smartphone 3400 can recognize the QR code (registered trademark) using the camera and save or transmit the value of the QR code (registered trademark) and the voltage value (or analysis result) of the measuring device 3410 to an external device.

[0195] FIG. 35 shows an example of a method for analyzing the measurement results according to the present disclosure.

[0196] Referring to FIG. 35, the sensor array 3500 outputs measurement values 3510 (e.g., current or voltage) of a plurality of graphene-based sensors. The structure of the sensor array 35 is shown in FIGS. 1 to 15. As another example, an output unit that converts the output current into a voltage may be present at the output end of the sensor array 3500. However, hereinafter, for the convenience of explanation, it will be described that the measurement values output from the sensor array 3500 are adjusted to voltage values within a certain range by the output unit. That is, the ranges of the measurement values are all normalized to values within a certain range.

[0197] A plurality of measurement values 3510 output from the sensor array 3500 are input into the artificial neural network 3520. The artificial neural network 3520 may be implemented by various conventional deep learning models such as a CNN (Convolutional Neural Network). The artificial neural network 3520 is pre-trained using learning data. For example, the artificial neural network 3520 can be trained by a supervised learning method using learning data in which a plurality of measurement values 3510 obtained by measuring a standard sample with the sensor array are labeled with information on the standard sample that is known in advance (e.g., light wavelength / intensity, biomolecule type / quantity, gas molecule type / quantity, pressure intensity, etc.). As an example of the supervised learning method, a learning method of a regression analysis model may be mainly used. When the artificial neural network 3520 that has completed learning in this way receives a plurality of measurement values 3510 measured by the sensor array, it outputs an analysis result 3530 (e.g., light wavelength / intensity, biomolecule type / quantity, gas molecule type / quantity, pressure intensity, etc.). Various analysis methods will be described below.

[0198] First, as a first embodiment of the analysis method, a method for detecting the wavelength or intensity of light using the sensor array 3500 and the artificial neural network 3520 will be described. The reaction layers of the plurality of graphene-based sensors of the sensor array 3500 contain substances of different quantum dots that sense light of different wavelengths. For example, the reaction layers of the plurality of graphene-based sensors constituting the sensor array 3500 can be composed of quantum dots that react to R, G, and B, respectively. When the artificial neural network 3520 that has completed learning in advance receives a plurality of measurement values 3510 of the sensor array, it outputs the wavelength in the visible light region as an analysis result. As another example, the reaction layers of the plurality of graphene-based sensors can be composed of quantum dots that sense infrared rays in different wavelength bands, and the wavelength in the infrared region can be detected. In this case, the amount of blood glucose can be measured by sensing the infrared rays of the skin. As still another example, different quantum dots that can detect different wavelength bands in the UV-IR region can be configured as the reaction layers of each graphene-based sensor of the sensor array 3500, and the wavelength of the signal can be analyzed.

[0199] As a second embodiment of the analysis method, the types and amounts of biomolecules can be detected using the sensor array 3500 and the artificial neural network 3520. The reaction layers of the plurality of graphene-based sensors of the sensor array 3500 may include biomolecules (e.g., DNA, RNA, antigen, antibody, enzyme, etc.) with different degrees of reaction. For example, the degree to which the first antibody in the reaction layer of the first graphene-based sensor of the sensor array 3500 reacts with various viruses (e.g., SARS-CoV-1, SARS-CoV-2, MERS-Cov, etc.) and the degree to which the second antibody in the reaction layer of the second graphene-based sensor reacts with various viruses (i.e., the signal of the measured value) may be different from each other. In other words, the degree to which the first antibody reacts with the first virus is r1, the degree to which it reacts with the second virus is r2, and the degrees to which the second antibody reacts with the first virus and the second virus are r3 and r4, respectively, and they may be different from each other.

[0200] If the sensor array 3500 outputs a plurality of measured values 3510 for the specimen, the artificial neural network 3520 outputs the type / amount of the virus as an analysis result. For this purpose, the artificial neural network 3520 can be pre-trained with learning data in which the measured values 3510 of the sensor array 3500 for various viruses are labeled with the type and / or amount of the virus. By configuring antibodies with different degrees of reaction to various viruses as the reaction layers of the respective graphene-based sensors in this way, the type and amount of the specimen can be detected according to the degree of reaction of each antibody. Therefore, a number of viruses greater than the number of graphene-based sensors included in the sensor array 3500 can be detected. Also, if the degrees of reaction of the respective antibodies in the reaction layer of the graphene-based sensor are different from each other for a new virus, it is possible to detect the new virus by using the existing sensor array 3500 as it is and training the artificial neural network with learning data for the new virus without further creating the reaction layer of the sensor array 3500.

[0201] As a third embodiment of the analysis method, a method for diagnosing a disease using a sensor array 3500 and an artificial neural network 3520 will be described. Reaction layers are formed on a plurality of graphene-based sensors of the sensor array 3500 with various substances such as DNA, RNA, antigens, antibodies, and enzymes. For example, the first graphene-based sensor of the sensor array 3500 may include a reaction layer of a substance capable of detecting DNA, and the second graphene-based sensor may include a reaction layer of a substance capable of detecting an enzyme. When the artificial neural network 3520 receives a plurality of measurement values 3510 of the sensor array 3500, it predicts and outputs a disease. That is, a specific disease can be diagnosed by various combinations of measurement values. For this purpose, the artificial neural network 3520 can be pre-trained with learning data in which a plurality of measurement values obtained by inputting a specimen capable of diagnosing a specific disease into the sensor array 3500 are labeled with disease names.

[0202] FIG. 36 shows an example of an artificial neural network that can be referred to in various embodiments of the present disclosure.

[0203] Referring to FIG. 36, the artificial neural network 3600 includes an input layer 3610, one or more hidden layers 3620, and an output layer 3630. The one or more hidden layers 3620 include one or more network nodes 3621. The one or more network nodes 3621 have an interconnection relationship by simulating the synaptic activity of neurons in which human neurons exchange signals through synapses. For example, in the artificial neural network 3600, one or more network nodes 3621 are located in layers (for example, hidden layers) having different depths from each other and exchange data through a convolution connection relationship. In addition, the artificial neural network 3600 includes network nodes having various connection relationships (for example, recurrent, etc.). Such a connection relationship between network nodes may be defined as mapping tables 3641, 3642 for each layer of each hidden layer. A set of such mapping tables 3641, 3642 may be abbreviated as a mapping table set 3640.

[0204] Through the learning of the artificial neural network 3600 described above, the mapping table set 3640 is determined. In one embodiment, the mapping table set 3640 may be embodied as internal circuits in various forms. The internal circuit thus embodied as hardware may be included in the measuring devices 2500 and 2600 of FIGS. 28 and 29. In other embodiments, the mapping table set 3640 may be embodied as computer programs in various forms. The computer program thus embodied as software may be included in the measuring devices 2500 and 2600 of FIGS. 28 and 29. Thereby, the artificial neural network 3600 receives a plurality of measurement values of the sensor array and outputs analysis results (for example, wavelength / intensity of light, type / amount of biomolecules, type / amount of gas molecules, intensity of pressure, etc.) by the operations determined in the mapping table set 3640.

[0205] In the flowchart according to the present disclosure, although each operation of the method or algorithm has been described in a sequential order, in addition to being performed sequentially, it may also be performed in an arbitrarily combinable order. The description of the flowchart of the present disclosure does not exclude making changes or modifications to the method or algorithm, nor does it mean that any operation is essential or preferable. In one embodiment, at least some of the operations may be performed in parallel, iteratively or heuristically. In another embodiment, at least some of the operations may be omitted, and other operations may be added.

[0206] Various embodiments of the present disclosure may be embodied as software in a machine-readable storage medium (MRSM) that can be read by a computing device. The software may be software for embodying various embodiments of the present disclosure. The software can be inferred from various embodiments of the present disclosure by a programmer in the technical field to which the content of the present disclosure belongs. For example, the software may be a computer program including instructions that can be read by a computing device. The computing device is a device that can operate according to instructions called from the storage medium and may be, for example, rephrased as an electronic device. In one embodiment, a processor of the computing device can execute the called instructions and cause components of the computing device to perform functions corresponding to the instructions. The storage medium can mean any type of recording medium in which information is stored and that is readable by a device. The storage medium may include, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical information storage device, and the like. In one embodiment, the storage medium may be embodied in a distributed form, such as in a computer system connected by a network. At this time, the software may be distributed and stored in a computer system or the like and executed. In another embodiment, the storage medium may be a non-transitory storage medium. The non-transitory storage medium means a medium that exists regardless of whether information is stored semi-permanently or temporarily and does not include a signal propagated transiently.

[0207] As described above, the technical idea according to the present disclosure has been explained by various embodiments. However, the technical idea according to the present disclosure includes various substitutions, modifications, and changes that can be made within the scope understood by those having ordinary knowledge in the technical field to which the present disclosure belongs. Also, such substitutions, modifications, and changes should be understood to be included within the scope of the appended claims.

Claims

1. A substrate, a doping region formed by doping impurities in a partial region of the substrate, an insulator layer laminated on the substrate except for a part of the doping region, a graphene layer laminated on the doping region, a first electrode connected to the graphene layer, a second electrode connected to the doping region, a graphene-based sensor.

2. The doping region includes a first doping region doped at a first concentration and a second doping region doped at a second concentration, wherein the first concentration is less than or equal to the second concentration, the graphene layer is connected to the first doping region, and the second electrode is connected to the second doping region, The graphene-based sensor according to claim 1.

3. The graphene-based sensor according to claim 1, wherein the first electrode is formed in a form surrounding the periphery of the graphene layer.

4. The graphene-based sensor according to claim 1, wherein the second electrode is located below the substrate facing the graphene layer.

5. The graphene-based sensor according to claim 1, further comprising one or more via electrodes formed through the insulator layer and the substrate at a position separated from the first electrode, and the second electrode is connected to the one or more via electrodes.

6. The graphene-based sensor according to claim 1, wherein the second electrode is connected to the doping region through the insulator layer.

7. The graphene-based sensor according to claim 1, further comprising a third electrode in contact with the graphene layer at a position separated from the first electrode.

8. The graphene-based sensor according to claim 1, further comprising a storage portion formed by etching the substrate at a first depth and a first width, and a channel portion formed by etching the insulator layer at a second depth and a second width to connect the storage portion and the graphene layer, wherein the second depth and the second width are each smaller than the first depth and the first width.

9. The graphene-based sensor according to claim 1, wherein a plurality of doping regions are arranged separately on the substrate, and the insulator layer is laminated such that the plurality of doping regions are each exposed.

10. The graphene layer is laminated so as to correspond to the plurality of doping regions, and includes a plurality of first electrodes respectively corresponding to the plurality of doping regions, and the second electrode is commonly connected to the plurality of doping regions. The graphene-based sensor according to claim 9.

11. The graphene layer is laminated across the plurality of doping regions, and includes a plurality of second electrodes respectively connected to the plurality of doping regions, and the first electrode is commonly connected to the plurality of doping regions. The graphene-based sensor according to claim 9.

12. The graphene-based sensor according to claim 10 or 11, further including a multiplexer that outputs values measured in the graphene layer of the plurality of doping regions.

13. The graphene-based sensor according to claim 1, further including a reaction layer located on the surface of the graphene layer and containing a substance that induces charge in the graphene layer.

14. A substrate, A two-dimensional semiconductor layer laminated on the substrate, A graphene layer laminated across the two-dimensional semiconductor layer, A first electrode laminated in contact with the two-dimensional semiconductor layer, A second electrode laminated in contact with the graphene layer, including a graphene-based sensor.

15. The graphene-based sensor according to claim 14, including a reaction layer located on the surface of the graphene layer and containing a substance that induces charge in the graphene layer.

16. A plurality of two-dimensional semiconductor layers are arranged separately on the substrate, the graphene layer is laminated across the plurality of two-dimensional semiconductor layers, and a plurality of first electrodes are respectively located on the plurality of two-dimensional semiconductor layers. The graphene-based sensor according to claim 14.

17. Among the two-dimensional substances constituting the plurality of two-dimensional semiconductor layers, a part and the other part are different from each other. The graphene-based sensor according to claim 16.

18. The plurality of two-dimensional semiconductor layers are arranged in a first direction, and the graphene layer is arranged in a second direction perpendicular to the first direction. The graphene-based sensor according to claim 16.

19. The graphene-based sensor according to claim 14, further including a third electrode laminated in contact with the two-dimensional semiconductor layer at a distance from the first electrode.

20. The graphene-based sensor according to claim 14, further comprising a fourth electrode laminated in contact with the graphene layer and separated from the second electrode.

21. The graphene-based sensor according to claim 14, wherein a plurality of two-dimensional semiconductor layers are arranged radially, the graphene layer is arranged across the plurality of two-dimensional semiconductor layers in a circular band shape, and further comprises a third electrode commonly connected to the plurality of two-dimensional semiconductor layers, and a plurality of first electrodes are respectively connected to the plurality of two-dimensional semiconductor layers.

22. A measuring device comprising: a graphene-based sensor in which a graphene layer is laminated on a doping region formed on a substrate or on a two-dimensional semiconductor layer laminated on the substrate; and an output unit that converts the output current of the graphene-based sensor into a voltage and outputs it.

23. The measuring device according to claim 22, wherein the output unit includes a preamplifier that converts the output current of the graphene-based sensor into a voltage and outputs it, and a feedback unit that adjusts the output voltage of the preamplifier to a certain range.

24. The measuring device according to claim 22, further comprising a display unit that displays the output voltage of the output unit, and a communication unit that transmits the output voltage of the output unit to the outside.

25. The measuring device according to claim 22, comprising a sensor array in which the graphene-based sensors are arranged in one dimension or two dimensions, and a multiplexer connected to a plurality of graphene-based sensors constituting the sensor array, wherein the output unit selectively receives the output current of each graphene-based sensor via the multiplexer.

26. The measuring device according to claim 22, further comprising an internal circuit provided with a mapping table set corresponding to an artificial neural network that receives a measurement value that appears when a specimen reacts with a substance in a reaction layer of the graphene-based sensor and outputs an analysis value.

27. The measuring device according to claim 22, further comprising a voltage application unit that applies a voltage within a predefined range to the graphene-based sensor, and a measurement unit that grasps a first measurement value of the resistance or current of the graphene-based sensor due to the application of the voltage before the application of the specimen and grasps and outputs a second measurement value of the resistance or current of the graphene-based sensor due to the application of the voltage after the application of the specimen.

28. The measuring device according to claim 27, wherein the measurement unit outputs a result value including the maximum difference among the differences in voltage between the first measurement value and the second measurement value.

29. The measurement unit repeatedly performs a process of measuring the second measurement value at regular time intervals, and outputs a second measurement value of the resistance or current of the sensor due to voltage application each time the process is repeatedly performed. The measuring device according to claim 27.

30. The measurement unit grasps and outputs a change in the maximum difference between the first measurement value and the second measurement value each time the process is repeatedly performed. The measuring device according to claim 29.

31. An analysis method using a sensor array including a plurality of sensors in which a graphene layer is laminated on a semiconductor region doped on a substrate or a two-dimensional semiconductor, receiving a plurality of measurement values from a plurality of graphene-based sensors of the sensor array; receiving the plurality of measurement values by an artificial neural network and obtaining an analysis result using an operation determined in a mapping table set. Analysis method.

32. The substances of the reaction layer coated on the graphene layer of the plurality of graphene-based sensors are partially different from each other. The analysis method according to claim 31.

33. The substances of the reaction layer of the plurality of graphene-based sensors are at least one of quantum dots, biomolecules, molecules that react with gases, and structures that react with pressure. The analysis method according to claim 31.

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