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

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] This disclosure relates to a graphene-based sensor, a measuring device using the same, a method for measuring a sample, and a method for analyzing a sample. [Background technology]

[0002] This invention is derived from research conducted as part of the 2022 Startup Growth Technology Development Project, Phase 2 (Second Half), of the Korea Small and Medium Enterprise Technology Information Promotion Agency, Ministry of SMEs and Startups. Detailed information on this project is as follows: -Project Number: S3282664 -Project Specific Number: 1425168230 -Research Project Title: Development of Barristor-Based Rapid Immunological Diagnostics with High Sensitivity -Research Period: July 1, 2022 - June 30, 2024. Furthermore, this invention is a technology developed under the Seoul Institute of Technology's 2022 Campus Town Technology Matching Support Project (Development of surface-cleaning process of barristor-based sensor). Sensors come in various types, including 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. Biosensors, in particular, are broadly classified into optical, electrochemical, and piezoelectric types. Optical sensors utilize optical methods such as color development, fluorescence, chemiluminescence, and surface plasmon resonance. Electrochemical sensors measure the interaction between the analyte and the bioreceptor as an electrochemical signal, such as potential, current, charge, conductivity, or impedance. Piezoelectric sensors measure mass changes. Electrochemical biosensors have advantages over other methods in terms of portability and miniaturization, are possible in both labeled and unlabeled forms, and the signal conversion process is simpler than with optical methods because the detection signal is an electrical signal. Electrochemical sensors may be implemented using a transistor base to accurately measure minute changes in electrical signals.

[0003] Graphene is being actively researched as a new material to replace semiconductors. Graphene has a hexagonal honeycomb-like two-dimensional planar crystal structure, and its lightness, excellent durability, and conductivity have led to its use in various fields. In recent years, a new device called a barristor has emerged using graphene, which functions like a transistor but structurally is closer to a diode. [Overview of the project] [Problems that the invention aims to solve]

[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 exhibited 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] 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 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.

[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 being less than or equal to the second concentration, the graphene layer being connected to the first doping region, and the second electrode being connected to the second doping region.

[0011] In one embodiment, the first electrode may be formed in a form surrounding the periphery of 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 part formed by etching the substrate with a first depth and a first width, and a channel part formed by etching the insulator layer with a second depth and a second width, which connects the storage part 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, a multiplexer that outputs values ​​measured in the graphene layers of the plurality of doping regions may be further included.

[0021] In one embodiment, a reaction layer may be further included, which is located on the surface of the graphene layer and contains a substance that induces an electric 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 other embodiments, a reaction layer may be included that is located on the surface of the graphene layer and contains a substance that induces an electric charge in the graphene layer.

[0024] In other embodiments, a plurality of two-dimensional semiconductor layers are arranged spaced apart on the substrate, the graphene layer is stacked across the plurality of two-dimensional semiconductor layers, and a plurality of first electrodes may be located on each of the plurality of two-dimensional semiconductor layers.

[0025] In other embodiments, some and other parts of the two-dimensional material constituting the plurality of two-dimensional semiconductor layers may be different from each other.

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

[0027] In other embodiments, a third electrode may be further included, which is stacked in contact with the two-dimensional semiconductor layer, separated from the first electrode.

[0028] In other embodiments, a fourth electrode may be further included, which is laminated in contact with the graphene layer, separated from the second electrode.

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

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

[0031] In yet 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 yet another embodiment, the system may further include a display unit for displaying the output voltage of the output unit, and a communication unit for transmitting the output voltage of the output unit to an external source.

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

[0034] In yet another embodiment, the system may further include an internal circuit that embodies a mapping table set corresponding to an artificial neural network that receives measurement values ​​appearing when a sample reacts with the material in the reaction layer of the graphene-based sensor and outputs analytical values.

[0035] In yet another embodiment, the system may further include a voltage application unit that applies a voltage to a graphene-based sensor within a predetermined range, 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 voltage before the application of a sample, and grasps and outputs a second measured value of the resistance or current of the graphene-based sensor due to the application of voltage after the application of a sample.

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

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

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

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

[0040] In yet another embodiment, the material of the reaction layer coated on the graphene layer of the plurality of graphene-based sensors may be partially different from that of the other parts.

[0041] In yet another embodiment, the material of the reaction layer of the plurality of graphene-based sensors may be at least one of quantum points, biomolecules, gas-reactive molecules, and pressure-reactive structures. [Effects of the Invention]

[0042] According to this disclosure, graphene-based sensors have higher reaction sensitivity compared to general sensors, allowing for accurate measurement of subtle changes in electrical signals resulting from interactions between antigens and antibodies, enzymes, DNA, and other elements. Furthermore, graphene-based sensors are applicable not only to the bio-field but also to various fields for measuring light, gas, pressure, and other elements.

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

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

[0045] The effects of the technical concept of this disclosure are not limited to those mentioned above, and other effects not mentioned will be clearly understood by a person ordinary in the art of this disclosure from the description in the specification. [Brief explanation of the drawing]

[0046] [Figure 1A] Figures 1A to 1D show the structure of a first embodiment of the graphene-based sensor according to this disclosure. [Figure 1B] Figures 1A to 1D show the structure of a first embodiment of the graphene-based sensor according to this disclosure. [Figure 1C] Figures 1A to 1D show the structure of a first embodiment of the graphene-based sensor according to this disclosure. [Figure 1D] Figures 1A to 1D show the structure of a first embodiment of the graphene-based sensor according to this disclosure. [Figure 2A] Figures 2A to 2D show the structure of a second embodiment of the graphene-based sensor according to this disclosure. [Figure 2B] Figures 2A to 2D show the structure of a second embodiment of the graphene-based sensor according to this disclosure. [Figure 2C]Figures 2A to 2D show the structure of a second embodiment of the graphene-based sensor according to this disclosure. [Figure 2D] Figures 2A to 2D show the structure of a second embodiment of the graphene-based sensor according to this disclosure. [Figure 3A] Figures 3A and 3B show the structure of a third embodiment of the graphene-based sensor according to this disclosure. [Figure 3B] Figures 3A and 3B show the structure of a third embodiment of the graphene-based sensor according to this disclosure. [Figure 4A] Figures 4A and 4B show the structure of a fourth embodiment of the graphene-based sensor according to this disclosure. [Figure 4B] Figures 4A and 4B show the structure of a fourth embodiment of the graphene-based sensor according to this disclosure. [Figure 5] Figure 5 shows the structure of a fifth embodiment of the graphene-based sensor according to the present disclosure. [Figure 6A] Figures 6A and 6B show the structure of a first embodiment of the sensor array according to this disclosure. [Figure 6B] Figures 6A and 6B show the structure of a first embodiment of the sensor array according to this disclosure. [Figure 7A] Figures 7A to 7D show the structure of a second embodiment of the sensor array according to this disclosure. [Figure 7B] Figures 7A to 7D show the structure of a second embodiment of the sensor array according to this disclosure. [Figure 7C] Figures 7A to 7D show the structure of a second embodiment of the sensor array according to this disclosure. [Figure 7D] Figures 7A to 7D show the structure of a second embodiment of the sensor array according to this disclosure. [Figure 8A] Figures 8A to 8D show the structure of a third embodiment of the sensor array according to this disclosure. [Figure 8B] Figures 8A to 8D show the structure of a third embodiment of the sensor array according to this disclosure. [Figure 8C] Figures 8A to 8D show the structure of a third embodiment of the sensor array according to this disclosure. [Figure 8D] Figures 8A to 8D show the structure of a third embodiment of the sensor array according to this disclosure. [Figure 9A] Figures 9A and 9B show the structure of a fourth embodiment of the sensor array according to this disclosure. [Figure 9B] Figures 9A and 9B show the structure of a fourth embodiment of the sensor array according to this disclosure. [Figure 10A] Figures 10A and 10B show the structure of a fifth embodiment of the sensor array according to this disclosure. [Figure 10B] Figures 10A and 10B show the structure of a fifth embodiment of the sensor array according to this disclosure. [Figure 11A] Figures 11A and 11B show the structure of a sixth embodiment of the sensor array according to this disclosure. [Figure 11B] Figures 11A and 11B show the structure of a sixth embodiment of the sensor array according to this disclosure. [Figure 12] Figure 12 shows the structure of a seventh embodiment of the sensor array according to this disclosure. [Figure 13A] Figures 13A and 13B show the structure of the eighth embodiment of the sensor array according to this disclosure. [Figure 13B] Figures 13A and 13B show the structure of the eighth embodiment of the sensor array according to this disclosure. [Figure 14] Figure 14 shows the structure of the ninth embodiment of the sensor array according to this disclosure. [Figure 15] Figure 15 shows the structure of a tenth embodiment of the sensor array according to this disclosure. [Figure 16A] Figures 16A and 16B show an example of current change in a graphene-based sensor according to this disclosure. [Figure 16B] Figures 16A and 16B show an example of current change in a graphene-based sensor according to this disclosure. [Figure 17] Figures 17 and 18 show various examples of measuring devices using graphene-based sensors according to this disclosure. [Figure 18]Figures 17 and 18 show various examples of measuring devices using graphene-based sensors according to this disclosure. [Figure 19] Figure 19 shows an example of the detailed configuration of the feedback section and control section of the measuring device according to this disclosure. [Figure 20] Figures 20 to 26 show various examples of measuring devices using the sensor array described herein. [Figure 21] Figures 20 to 26 show various examples of measuring devices using the sensor array described herein. [Figure 22] Figures 20 to 26 show various examples of measuring devices using the sensor array described herein. [Figure 23] Figures 20 to 26 show various examples of measuring devices using the sensor array described herein. [Figure 24] Figures 20 to 26 show various examples of measuring devices using the sensor array described herein. [Figure 25] Figures 20 to 26 show various examples of measuring devices using the sensor array described herein. [Figure 26] Figures 20 to 26 show various examples of measuring devices using the sensor array described herein. [Figure 27] Figure 27 shows a block diagram of the measuring device relating to this disclosure. [Figure 28] Figures 28 and 29 show concrete examples of the measuring device according to this disclosure. [Figure 29] Figures 28 and 29 show concrete examples of the measuring device according to this disclosure. [Figure 30] Figure 30 shows a flowchart illustrating the measurement method for the sample related to this disclosure. [Figure 31] Figure 31 shows an example of a method for applying a buffer solution that can be referenced in various embodiments of this disclosure. [Figure 32] Figure 32 shows an example of a method for measuring a sample according to this disclosure. [Figure 33] Figure 33 shows an example of measurement results that can be referenced in various embodiments of this disclosure. [Figure 34]Figure 34 shows an example of the use of the measuring device according to this disclosure. [Figure 35] Figure 35 shows an example of a method for analyzing the measurement results related to this disclosure. [Figure 36] Figure 36 shows an example of an artificial neural network that can be referenced in various embodiments of this disclosure. [Modes for carrying out the invention]

[0047] The various embodiments described herein are illustrative for the purpose of clearly illustrating the technical idea of ​​this disclosure and are not intended to limit it to any particular embodiment. The technical idea of ​​this disclosure includes various modifications, equivalents, alternatives, and embodiments selectively combined from all or part of the embodiments described herein. Furthermore, the scope of rights relating to the technical idea of ​​this disclosure is not limited to the various embodiments or specific descriptions thereof presented below.

[0048] Unless otherwise defined, terms used in this disclosure, including technical or scientific terms, may have meanings that are generally understood by a person with ordinary skill in the art to which this disclosure pertains.

[0049] Expressions used in this disclosure such as “includes,” “may include,” “equip,” “may equip,” “may equip,” “have,” and “may have” mean that the feature in question (e.g., function, operation, or component) may exist, but do not exclude the existence of other further features. In other words, such expressions should be understood as open-ended terms that imply the possibility of including other embodiments.

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

[0051] The terms "first," "second," or "first," "second," etc., used in this disclosure, unless otherwise specified in the context, are used to distinguish one object from others when referring to multiple objects of the same kind, and do not limit the order or importance of the objects.

[0052] Expressions used in this disclosure 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” can mean each of the listed items, or any possible combination of the listed items. For example, “at least one of A or B” could mean (1) at least one A, (2) at least one B, or (3) all of at least one A and at least one B.

[0053] The expression “based on” as used in this disclosure is used to describe one or more factors that influence an act or action of decision, judgment, or action described in the phrase or sentence containing the expression, and the expression does not exclude any further factors that influence such act or action of decision, judgment, or action.

[0054] The expression used in this disclosure that one component (e.g., a first component) is “connected” or “linked” to another component (e.g., a second component) may mean that the one component is connected or linked not only directly to the other component, but also via yet another component (e.g., a third component).

[0055] As used in this disclosure, the expression "configured to" may, depending on the context, mean "set to do," "capable of doing," "modified to do," "made to do," or "capable of doing." This expression is not limited to meaning "specifically designed in hardware." For example, a processor configured to perform a particular operation may mean a generic-purpose processor capable of performing that particular operation by running software, or a special-purpose computer structured by programming to perform that particular operation.

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

[0057] As used in this disclosure, "Artificial Intelligence (AI)" can mean technologies that mimic and embody human learning, reasoning, or perceptual abilities in a computer. Specifically, artificial intelligence can make judgments or predictions based on input data by analyzing input data and generating output data based on the results of that analysis. Furthermore, artificial intelligence can perform technologies that mimic the cognitive or judgment functions of the human brain (e.g., linguistic comprehension, visual comprehension, reasoning / prediction, knowledge representation, or behavioral control). In one embodiment, artificial intelligence may include the concepts of machine learning or symbolic logic.

[0058] As used in this disclosure, the term "machine learning (ML)" can refer to a series of processes that train an artificial neural network model using its experience in processing data. Such machine learning can improve the data processing capabilities of the artificial neural network model.

[0059] The term "artificial neural network model" as used in this disclosure refers to a model constructed by modeling the correlation between data, which may be expressed by multiple parameters. Such an artificial neural network model may be implemented on a computer to simulate the structure of the human brain, for example, neurons in the human neural network, and to include multiple network nodes with weighted values. The parameters of this artificial neural network model can be optimized by iterating through a series of operations: extracting features from given training data, analyzing the extracted features, and deriving the correlation between the data. This process of optimizing the parameters of an artificial neural network model can be called machine learning. To give a specific example, an artificial neural network model can learn the mapping (correlation) between inputs and outputs for data given in pairs of inputs and outputs, which can be called supervised learning. To give another example, even when only input data is given, an artificial neural network model can derive regularities between the given data and learn their relationships, which can be called unsupervised learning. In this disclosure, the term "artificial neural network model" may be replaced with "artificial neural network," "model," "neural network model," "artificial intelligence learning model," or "machine learning model."

[0060] Various embodiments described herein will be explained below with reference to the attached drawings. In the attached drawings and descriptions thereof, identical or substantially equivalent components may be given the same reference numerals. In the following descriptions of various embodiments, redundant descriptions of identical or corresponding components may be omitted, but this does not mean that the component is not included in that embodiment.

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

[0062] Referring to Figure 1, the graphene-based sensor includes a substrate 100, a non-conductive layer 110, a graphene layer 120, a first electrode 130, a second electrode 140, and a doping region 160.

[0063] The substrate 100 has doping regions 160 formed by doping with impurities. In one embodiment, the substrate may be a semiconductor layer composed of a general semiconductor material such as silicon, germanium, or a compound semiconductor. The doping regions 160 may be doped in an n-type or p-type manner. In one embodiment, the doping regions 160 may be formed in two regions 150 and 155 with different doping concentrations. For example, the doping regions 160 may have a first concentration (e.g., 1*10 8 / cm 3 ~5*10 19 / cm 3 A first doping region 150 doped with ) and a second concentration (>= first concentration) (for example, 10 19 / cm 3 The doping region 160 may include a second doping region 155 doped with the above. In other embodiments, the entire doping region 160 may be doped with the same concentration. However, for the sake of explanation, the doping region 160 will be described below, including two regions 150 and 155 doped with different concentrations. The area and depth of the doping region 160 can be varied in various ways.

[0064] The insulated layer 110 is formed on the substrate 100, except for a portion of the doping region 160. For example, the insulated layer 110 is not present above a portion or all of the first doping region 150 and is exposed on the substrate 100. In one embodiment, the insulated 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 that is not covered by the insulated layer 110. For example, the graphene layer 120 may be laminated across the insulated 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 beneath the substrate 100. In another embodiment, the upper surface of the substrate 100 may further include via electrodes 170 connected to the second electrode 140 on the lower surface of the substrate. The via electrodes 170 may penetrate the insulator layer 110 and the substrate 100 to connect to the second electrode 140 beneath the substrate 100. The connection structure of the first electrode 130 and the second electrode 140 in this embodiment is merely an example to aid in understanding the present invention, and the scope of this 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 the following Figures 2 to 5.

[0067] The graphene layer 120 may further include a reaction layer 180 formed on its surface, which is composed of a substance that reacts with the sample or a structure that senses pressure. The type of substance constituting the reaction layer may vary depending on the type of sample (e.g., light, biomaterial, gas, etc.). For example, the reaction layer 180 can be formed by coating the graphene layer 120 with a biomaterial (DNA, antigen, antibody, or enzyme, etc.), a quantum dot (e.g., a lead sulfide quantum dot), or a polymer film, or a reaction layer 180 composed of a structure that measures pressure can be laminated onto the graphene layer 120. In other embodiments, the reaction layer 180 may be composed of multiple substances or multiple layers. Depending on the type of 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 biomaterials, or an environmental sensor that detects gas, temperature, or humidity.

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

[0069] Referring to Figure 2, the graphene-based sensor includes a substrate 200, a non-conductive layer 210, a graphene layer 220, a first electrode 230, a second electrode 240, and a doping region 260. The substrate 200, the non-conductive layer 210, the graphene layer 220, and the first electrode 230 are identical in structure to those in Figure 1. The depth and area of ​​the doping region 260 may be the same as in the first embodiment of Figure 1, or a part of it may be modified.

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

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

[0072] Referring to Figure 3, the graphene-based sensor includes a substrate 300, a non-conductive 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 Figure 1, except for the first electrode 330. The graphene-based sensor may further include a reaction layer 380.

[0073] In the first embodiment shown in Figure 1, the first electrode 130 is located on one side of the graphene layer 120, but in this embodiment, the first electrode 330 surrounds the graphene layer 320 (for example, in a rectangular shape). In another embodiment, the first electrode 230 of the second embodiment may be modified to surround the graphene layer 220, as in 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 via electrodes 370 can be varied in any way depending on the embodiment. In other embodiments, the via electrodes 370 may be omitted.

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

[0076] Referring to Figure 4, the graphene-based sensor includes a substrate 400, a non-conducting 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, and 472. For convenience of explanation, this embodiment is described by showing the additional electrodes 432, 434, 470, and 472 based on the structure of the first embodiment (i.e., the structure including via electrodes), but this embodiment may be embodied 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 a position separated from the first electrode 430. The third electrode 432 and the fourth electrode 434 are used together with the first electrode 430 to check for bonding errors in the first electrode 430 (for example, bonding errors 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 connected to the second electrode 440, which is formed on the lower part of the substrate 400, by penetrating the insulator layer 410 and the substrate 400, respectively. Therefore, if a bonding error occurs with the via electrode 470, the fifth electrode 472 or the sixth electrode 474 can be used. In addition, the via electrode 470 and the fifth electrode 472 (or the sixth electrode 474) can be used to confirm that the via electrode 470 is properly bonded to the second electrode 440.

[0079] Depending on the embodiment, only some of the electrodes from the third to sixth electrodes 432, 434, 470, and 472 may be included. 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] The above describes examples in which the substrate is made of a general semiconductor material, with reference to Figures 1 to 4. Below, with reference to Figure 5, examples in which the substrate is made of a two-dimensional material will be described.

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

[0082] Referring to Figure 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. Depending on the embodiment, the graphene-based sensor 500 may be implemented with or without a reaction layer 560. For the sake of explanation, the following description will assume the presence of the reaction layer 560. In addition, a structure can be created to surround the reaction layer 560 to prevent the sample from protruding outside the reaction layer.

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

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

[0085] The first electrode 540 is stacked in contact with the two-dimensional semiconductor layer 520, and the second electrode 550 is stacked in contact with the graphene layer 530. In other words, 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 an electric charge in the graphene layer 530. Depending on the type of sample (e.g., light, biomaterial, gas, etc.), the type of substance constituting the reaction layer 560 may vary. For example, the reaction layer 560 may be formed by coating the graphene layer 530 with a biomaterial (e.g., DNA, antigen, antibody, or enzyme), a quantum dot (e.g., a lead sulfide quantum dot), or a polymer film, or a reaction layer 560 composed of a structure for measuring pressure may be stacked on the graphene layer 530. In other embodiments, the reaction layer 560 may be composed of various substances or various layers. Depending on the type of 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 biomaterials, or an environmental sensor for detecting gas, temperature, or humidity.

[0087] A sensor array can be created by arranging one or more graphene-based sensors from the first to fifth embodiments. If the sensor array contains multiple graphene-based sensors, the sensor array may contain graphene-based sensors with the same structure, or it may contain a mixture of graphene-based sensors from the first to fifth embodiments. However, for the sake of explanation, the following description will focus on a sensor array in which the sensor array contains multiple graphene-based sensors, all of which have the same structure.

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

[0089] Referring to Figure 6, the sensor array is a structure in which multiple 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 Figure 3. That is, the graphene-based sensor includes a substrate 600, a non-conductive 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 the measured value (e.g., 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 Figure 4.

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

[0091] The reaction layers 680 of the multiple graphene-based sensors constituting the sensor array may each contain the same substance or different substances (A1, A2, A3). For example, a sensor array that detects the wavelength or intensity of light can be realized by configuring substances with different quantum points as the reaction layers 680 of each graphene-based sensor. Alternatively, a sensor array that detects the type and / or amount of biomolecules can be realized by configuring biomolecules that react to biomolecules (e.g., at least one of DNA, RNA, antigen, antibody, or enzyme) to varying degrees as the reaction layers 680 of each graphene-based sensor. For example, the sensor array can distinguish between coronavirus and avian influenza virus, or between SARS-CoV-1 and SARS-CoV-2 among coronaviruses. Alternatively, a sensor array that detects the type and / or amount of gas can be realized by configuring molecules that react to specific gas molecules to varying degrees as the reaction layers 680 of each graphene-based sensor. Alternatively, by combining materials that react to quantum points, biomolecules, gas molecules, etc., the reaction layer 680 of the sensor array can be constructed, allowing for the simultaneous detection of light, biomolecules, gas molecules, etc., using a single sensor array.

[0092] If the materials or structures constituting the reaction layer 680 of each graphene-based sensor in the sensor array are different from each other, the thickness of the insulator layer 610 may be set to a height predetermined to prevent the materials 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 of the second embodiment of the sensor array along the line A-A' shown in Figure 7A, Figure 7C is an example of a cross-sectional view of the second embodiment of the sensor array along the line B-B' shown in Figure 7A, and Figure 7D is another example of a cross-sectional view of the second embodiment of the sensor array along the line B-B' shown in Figure 7A. The term "Figure 7" may refer to any one of Figures 7A to 7D.

[0094] Referring to Figure 7, the sensor array includes multiple graphene-based sensors. Each graphene-based sensor constituting the sensor array includes a substrate 700, a non-conductive layer 710, a graphene layer 720, a first electrode 730, a second electrode 740, and a doping region 760. The graphene-based sensor in this embodiment is a modified version of the structure of the second embodiment in Figure 2.

[0095] The graphene layers 720 of multiple graphene-based sensors are interconnected. In other words, the graphene layers 720 of the sensor array are stacked across multiple graphene-based sensors. Therefore, the first electrode 730 connected to the graphene layer 720 is a common electrode for multiple graphene-based sensors. In the sensor array of Figure 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. In addition, each graphene-based sensor may have an output terminal 742 for outputting measurement values.

[0096] In another embodiment, the insulated layer 710 may be formed in a stepped manner (see Figures 7C and 7D). This embodiment shows two examples based on the height of the insulated layer 710. For example, the region of the insulated layer 710 where the first electrode 730 is located may be formed higher than the surrounding area (see Figure 7C). In this case, it is possible to prevent the sample from extending outside the sensor array.

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

[0098] Referring to Figure 8, the sensor array includes multiple graphene-based sensors. Each graphene-based sensor constituting the sensor array includes a substrate 800, a non-conductive 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 to be long in the longitudinal direction of the substrate 800, crossing multiple graphene-based sensors. For example, if the doping region 860 consists of multiple regions having different concentrations, it includes a second doping region 855 that crosses all of the multiple graphene-based sensors and multiple first doping regions 850, 851, 852 corresponding to the graphene layer 820 of each graphene-based sensor.

[0100] The second electrode 830 penetrates the insulated layer 810 and is connected to the doping region 860, as shown in the second embodiment of Figure 2. This embodiment shows a structure including two second electrodes 840 and 842, but the number of second electrodes 840 and 842 can be varied depending on the embodiment. Since the second electrode 840 is connected to the doping region 860 that crosses all of the graphene-based sensors, the second electrode 840 becomes a common electrode for the multiple graphene-based sensors. The first electrode 830 is present for each graphene-based sensor. Output terminals 832 for outputting the measured values ​​of each graphene-based sensor may be further included.

[0101] In another embodiment, the insulated layer 810 may be formed in a stepped manner (see Figures 8C and 8D). This embodiment shows two examples based on the height of the insulated layer 810. For example, the region of the insulated layer 810 where the second electrode 840 is located may be formed higher than the surrounding area, as in the embodiment of Figure 7C (see Figure 8C). In yet another embodiment, the lower surface of the substrate 800 may further include additional electrodes 894, 898 connected to the doping region 760.

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

[0103] Referring to Figures 9 and 10, the sensor array includes output units 950 and 1050 on substrates 900 and 1000. Each graphene-based sensor constituting the sensor array includes substrates 900 and 1000, insulated layers 910 and 1010, graphene layers 920 and 1020, first electrodes 930 and 1030, second electrodes 940 and 1040, and doping regions 960 and 1060. Reaction layers 980 and 1080 may be further included on the surface of the graphene layers 920 and 1020. The structure of the sensor array in Figure 9 is a modified version of the structure in Figure 7, and the structure of the sensor array in Figure 10 is a modified version of the structure in Figure 8.

[0104] The output sections 950 and 1050 may be implemented on substrates 900 and 1000 by semiconductor circuits.

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

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

[0107] Referring to Figure 11, the sensor array has a structure in which graphene-based sensors are arranged in the form of n*m (where n and m are natural numbers greater than or equal to 2). Each graphene-based sensor includes a substrate 1100, a non-conductive 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 in the sensor array may be embodied by the structures of the various embodiments described in Figures 1 to 5.

[0108] The graphene layer 1120 is stacked across multiple graphene-based sensors in rows. For example, the graphene layers of three graphene-based sensors located in the first row are linked together. Therefore, a common first electrode 1130 exists across the row.

[0109] The doping region 1160 exists across multiple graphene-based sensors in a row. 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, a common second electrode 1140 exists in each row. For example, as shown in Figure 8, a second electrode 1140 exists in each row that penetrates the insulated layer 1110 and is connected to the doping region 1160.

[0110] Multiple first electrodes 1130, 1131, 1132 and multiple second electrodes 1140, 1141, 1142 allow for the output of measurements from a desired graphene-based sensor among multiple graphene-based sensors. For example, the B2 sensor can be operated by applying signals to the first electrode in the first row and the second electrode in the second column.

[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. In one embodiment, 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 the 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 that address signal from 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] Figure 12 shows the structure of a seventh embodiment of the sensor array according to this disclosure.

[0113] Referring to Figure 12, the structure of the sensor array, consisting of multiple graphene-based sensors 1210 and two multiplexers 1220 and 1240, is identical to that of Figure 11. This embodiment further includes an amplifier 1240. The amplifier 1240 is coupled to a 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 implemented in chip form.

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

[0115] Referring to Figure 13, the sensor array includes multiple graphene-based sensors 1360. The structure of the sensor array may be any one of the structures shown in Figures 6 to 12. Furthermore, the structure of the sensor array may be any one of the structures shown in Figures 14 and 15, which will be described later. The sensor array 1360 also includes a storage section 1340 and a channel section 1350.

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

[0117] The depth and width of the channel portion 1350 may be made smaller than the depth and width of the storage portion 1340, so that only substances of a certain size or smaller from the sample stored in the storage portion 1340 are transmitted to the multiple graphene-based sensors 1360 via the channel portion 1350. For example, if the sample is blood, the channel portion 1350 may be made to be less than a certain width (e.g., less than 6 μm), so that only the remaining substances excluding red blood cells and white blood cells are transmitted to the graphene-based sensors 1360.

[0118] Although this embodiment shows a sensor array structure further including a storage section 1340 and a channel section 1350, the configuration of the storage section 1340 and the channel section 1350 may also be applied to the single graphene-based sensor described in Figures 1 to 5.

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

[0120] Referring to Figure 14, the sensor array 1400 includes multiple sensing regions 1430, 1432, 1434, and 1436, each consisting of stacked 2D semiconductor layers 1410, 1412, 1414, and 1416 and a graphene layer 1420. Each of these sensing regions 1430, 1432, 1434, and 1436 plays the role of a graphene-based sensor as described in Figure 5.

[0121] Multiple two-dimensional semiconductor layers 1410, 1412, 1414, and 1416 are arranged on a substrate spaced apart from each other, and the graphene layer 1420 may be stacked across the multiple two-dimensional semiconductor layers 1410, 1412, 1414, and 1416. For example, the multiple two-dimensional semiconductor layers 1410, 1412, 1414, and 1416 may be arranged at equal intervals in a first direction (e.g., the vertical direction), and the graphene layer 1420 may be stacked across the multiple two-dimensional semiconductor layers 1410, 1412, 1414, and 1416 in a second direction perpendicular to the first direction (e.g., the horizontal direction). The graphene layer 1420 is commonly connected to multiple sensing regions 1430, 1432, 1434, and 1436.

[0122] As one embodiment, multiple two-dimensional semiconductor layers 1410, 1412, 1414, and 1416 may be materialized from the same two-dimensional material, or from different two-dimensional materials. Alternatively, only some of the two-dimensional semiconductor layers (two or more of 1410, 1412, 1414, and 1416) may be materialized from different two-dimensional materials.

[0123] In another embodiment, the sizes of sensing regions 1430, 1432, 1434, and 1436, which are stacked with multiple two-dimensional semiconductor layers 1410, 1412, 1414, and 1416 and a graphene layer 1420, may differ from each other. For example, the widths of at least two or more two-dimensional semiconductor layers 1410, 1412, 1414, and 1416 may differ from each other. Alternatively, the widths of the graphene layers 1420 corresponding to the sensing regions 1430, 1432, 1434, and 1436 may differ from each other.

[0124] For the sake of explanation, this embodiment only shows the two-dimensional semiconductor layers 1410, 1412, 1414, and 1416 and the graphene layer 1420, but the structure of each sensing region 1430, 1432, 1434, and 1426 may include the structure shown in Figure 5. For example, multiple first electrodes (not shown) may be connected to multiple two-dimensional semiconductor layers 1410, 1412, 1414, and 1416, respectively, and one second electrode (not shown) may be connected to the graphene layer 1420.

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

[0126] Figure 15 shows the structure of a tenth embodiment of the sensor array according to this disclosure.

[0127] Referring to Figure 15, the sensor array 1500 includes multiple sensing regions 1580, 1582, 1584, 1586, 1588, and 1590, each consisting of stacked 2D semiconductor layers 1510, 1512, 1514, 1516, 1518, and 1520 and a graphene layer 1530. The multiple 2D semiconductor layers 1510, 1512, 1514, 1516, 1518, and 1520 are arranged radially on the substrate, spaced apart from each other, and the graphene layer 1530 may be stacked in a circular strip shape over the multiple 2D semiconductor layers 1510, 1512, 1514, 1516, 1518, and 1520.

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

[0129] The sensor array 1500 may further include a third electrode 1560 that is commonly connected to multiple two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, and 1520. Since the multiple two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, and 1520 are arranged radially, the third electrode 1560 may be formed so as to be commonly connected to the multiple two-dimensional semiconductor layers 1510, 1512, 1514, 1516, 1518, and 1520 inside the radial arrangement. Using multiple first electrodes 1540, 1542, 1544, 1546, 1548, 1550 and a third electrode 1560, the state of each two-dimensional semiconductor layer 1510, 1512, 1514, 1516, 1518, 1520 (for example, whether or not there is a malfunction) can be checked.

[0130] The sensor array 1500 may further include a fourth electrode 1572 connected to the graphene layer 1530. For example, the graphene layer 1530 may be a circular strip with a portion open, and a second electrode 1570 and a fourth electrode 1572 may be connected to both ends of the graphene layer 1530, respectively. The state of the graphene layer 1530 (e.g., a fault condition) can be checked using the second electrode 1570 and the fourth electrode 1572.

[0131] By using multiple first electrodes 1540, 1542, 1544, 1546, 1548, 1550 and a second electrode 1570, each connected to multiple 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 Figures 6 to 15 described above, it is possible to detect light frequency and intensity, biomolecules, gas molecules, or pressure. For example, the reaction layer located on the surface of the graphene layer or sensing region of each graphene-based sensor can be composed of antibodies from various viruses and bacteria to detect the type of antigen present in the sample. Alternatively, the reaction layer can be composed of antigens made from specific proteins of various viruses and bacteria to detect the type of antibody present in the sample. Alternatively, antibodies that react to various pathogens to different degrees can be used as the reaction layer to detect the type of pathogen. Alternatively, various enzymes can be used as the reaction layer to detect the type of various substances that react with the enzymes.

[0133] In yet another embodiment, the reaction layer of the graphene-based sensor constituting the sensor array is composed of molecules that react to specific gas molecules to different degrees, thereby enabling the detection of the type and quantity of gas molecules. In yet another embodiment, the reaction layer is made into a structure that reacts to pressure, thereby enabling the detection of pressure intensity.

[0134] As another embodiment, the reaction layers of multiple graphene-based sensors constituting a sensor array can be composed of nucleic acids to detect nucleic acid sequences. For example, the reaction layer material can be composed of single-stranded nucleic acids of approximately 20 base pairs (base-pairs), thereby realizing a sensor array capable of detecting down to one base pair. For instance, 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 some base pairs of the first nucleic acid have been mutated can be configured as the reaction layer of the second graphene-based sensor. When a sample with concentrations of C1 and C2 of the first and second nucleic acids, respectively, is input to the sensor array, the measured values ​​(S1, S2) of the sensor array are displayed as follows.

[0135]

number

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

[0137]

number

[0138] In other words, knowing the sensor array measurements (S1, S2) allows us to determine the concentrations of the primary and secondary nucleic acids in the sample. Although the above example uses two types of nucleic acids, it is possible to detect three or more types of nucleic acids using the same method.

[0139] Furthermore, another example allows for the measurement of concentrations of various substances in the blood (e.g., calcium, sodium, blood glucose, etc.). By configuring the reaction layers of the graphene-based sensors constituting the sensor array with various enzymes or channels, the concentrations of ions, blood glucose, etc., in the blood can be detected. For example, by configuring the reaction layer of the first graphene-based sensor with glucose oxidase and the reaction layer of the second graphene-based sensor with a potassium channel, the glucose and potassium concentrations in the blood can be measured. In addition, by configuring the sensor array to include various enzymatic reactions and various channels, the concentrations of various substances in a sample can be measured simultaneously.

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

[0141] Referring to Figure 16, the output currents 1600 and 1610 of the graphene-based sensor vary depending on the degree of reaction in the reaction layer. Furthermore, the output currents 1610 and 1610 of the graphene-based sensor may also vary depending on the type of material constituting the reaction layer. However, since the sensitivity of the output currents 1600 and 1610 of the graphene-based sensor in this embodiment is greater than that of the output currents 1650 and 1660 of conventional transistor-based sensors, it is possible to accurately measure minute changes in electrical signals. By using the changes in output currents 1600 and 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.

[0142] The output currents 1600 and 1610 of the graphene-based sensor vary on a logarithmic scale, resulting in a wide measurement range that makes accurate measurement difficult. Therefore, Figures 17 to 26 present a method and apparatus for measuring the output currents 1600 and 1610 of the graphene-based sensor by converting them to a voltage within a fixed range (e.g., 0 to 10V).

[0143] Figures 17 and 18 show various examples of measuring devices using graphene-based sensors according to this disclosure.

[0144] Referring to Figures 17 and 18, the measuring device includes graphene-based sensors 1710, 1810 and output units 1720, 1820. The graphene-based sensors 1710, 1810 may be implemented 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 device may further include control units 1750, 1850.

[0145] The preamplifiers 1730 and 1830 convert the output current of the graphene-based sensors 1710 and 1810 into a voltage. In other words, when the preamplifiers 1730 and 1830 receive a current, they output a voltage within a certain range. In one embodiment, the preamplifiers 1730 and 1830 include an amplifier and a variable resistor, and the output voltage of the amplifier can be adjusted to a certain range (e.g., 0 to 10V) by adjusting the variable resistor. The structure of the preamplifiers 1730 and 1830 in this embodiment is merely an example to aid understanding, and various conventional circuit structures that convert current input to voltage output may be used in the preamplifiers 1730 and 1830 in this embodiment.

[0146] The feedback units 1740 and 1840 provide feedback control to the output voltage of the preamplifier units 1730 and 1830, adjusting it to the set point of the control units 1750 and 1850. As shown in Figure 16, the graphene-based sensors 1710 and 1810 produce different output current graphs for 1600 and 1610 depending on the reactant in the reaction layer. Therefore, the feedback units 1740 and 1840 adjust the output voltage of the preamplifier units 1730 and 1830 according to the set point. For example, if the output voltage range of the preamplifier units 1730 and 1830 is 0 to 10V and the set point is 1V, the feedback units 1740 and 1840 adjust the output voltage of the preamplifier units 1730 and 1830 to a value in the range of 0 to 1V and output it. The feedback units 1740 and 1840, which adjust the output voltage range according to the set point, may be composed of analog circuits or implemented as a DSP (Digital Signal Processor). An example of a feedback section implemented using a DSP is shown in Figure 19.

[0147] The connection between the preamplifiers 1730, 1830 and the graphene-based sensors 1710, 1810 can be implemented in various forms. For example, referring to Figure 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 Figure 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 ground. In this case, the remaining electrodes of the graphene-based sensor 1810 may be connected to the output of the feedback unit 1840.

[0148] Figure 19 shows an example of the detailed configuration of the feedback section and control section of the measuring device according to this disclosure.

[0149] Referring to Figure 19, the feedback unit 1900 is implemented as a DSP. When the feedback unit 1900 receives a voltage within a certain range (e.g., a first range), it feedback-controls the output voltage values ​​of the aforementioned preamplifiers 1730 and 1830 to adjust the voltage to a certain range (e.g., second range < first range) and outputs it. Various conventional circuit structures that feedback-control the output voltages of the preamplifiers 1730 and 1830 to output 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] Figures 20 to 26 show various examples of measuring devices using the sensor array described herein.

[0151] Referring to Figure 20, the measuring device includes multiple output units 2020, 2022, 2024, and 2026 that output measurement values ​​from multiple graphene-based sensors 2010, 2012, 2014, and 2016 included in the sensor array 2000. Each output unit 2020, 2022, 2024, and 2026 may be embodied by the structure shown in Figure 17 or Figure 18. For example, if the sensor array 2000 includes four graphene-based sensors 2010, 2012, 2014, and 2016, then four output units 2020, 2022, 2024, and 2026 are connected to each graphene-based sensor 2010, 2012, 2014, and 2016. Since the number of output units is equal to the number of graphene-based sensors included in the sensor array 2000, the size of the measuring device increases with that number.

[0152] Referring to Figures 21 and 22, the measuring device includes an output section with added multiplexers 2100 and 2200. While the embodiment in Figure 20 requires an output section equal to the number of graphene-based sensors included in the sensor array 2000, this embodiment only requires one output section. The multiplexers 2100 and 2200 selectively receive output currents from multiple graphene-based sensors in the sensor array 2000 and output voltages. Figure 21 shows a structure in which the output section 1720 in Figure 17 is connected to the multiplexer 2100, and Figure 22 shows a structure in which the output section 1820 in Figure 18 is connected to the multiplexer 2200.

[0153] Referring to Figures 23 and 24, this is the case where the sensor array and multiplexers 2300 and 2400 are implemented as a single chip. In this case, the sensor array selectively outputs measurement values ​​from multiple graphene-based sensors via the multiplexers 2300 and 2400, so the output units 2310 and 2410 can be configured as a single unit. The output unit 2310 in Figure 23 is the same as the output unit 1720 in Figure 17, and the output unit 2110 in Figure 24 has the same configuration as the output unit 1830 in Figure 18.

[0154] Referring to Figures 25 and 26, the sensor array, multiplexers 2510, 2610, and preamplifiers 2520, 2620 are implemented as a single chip 2500, 2600. The output units 2530, 2630 include a feedback unit and a control unit. Figure 25 is implemented based on the structure of the output unit 1820 in Figure 18, and Figure 26 is implemented based on the configuration of the output unit 1720 in Figure 17.

[0155] Figure 27 shows a block diagram of the measuring device relating to this disclosure.

[0156] The following describes a sensor measuring device 2700 as an example of a measuring device. The sensor measuring device 2700 may be implemented as a computing device that performs computing functions.

[0157] Referring to Figure 27, the sensor measuring device 2700 includes a voltage application unit 2702 and a measuring unit 2704. The sensor measuring device 2700 may be embodied in various forms, and Figures 17 to 26 may be referenced as examples. The sensor 2710 can output different currents or resistances depending on the type of sample. Various sensors 2710 may be used in this embodiment. In another embodiment, a graphene-based sensor can be used to increase the sensitivity of the sensor. Figures 1 to 15 and Figures 17 to 26 may be referenced as examples of graphene-based sensors.

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

[0159] The measuring unit 2704 measures the resistance or current of the sensor 2710 when a voltage is applied. The measuring unit 2704 determines the resistance or current of the sensor for each voltage. For example, the measuring unit 2704 determines the measured resistance or current of the sensor 2710 when a voltage is applied before the sample is applied, and determines the measured resistance or current of the sensor 2710 when a voltage is applied after the sample is applied. A measurement method using such a sensor measuring device 2700 will be described later with reference to Figures 30 to 33.

[0160] The measurement unit 2704 can display measurement results via a screen interface, output whether or not a sample has been recognized via voice or other means, or transmit measurement results to external devices via a wired or wireless network. The types of information displayed or transmitted by the measurement unit 2704 can be varied in various ways depending on the embodiment.

[0161] Figures 28 and 29 show concrete examples of the measuring device according to this disclosure.

[0162] Referring to Figure 28, the measuring device 2800 may be embodied in a small, portable size, and one side of the measuring device 2800 has an insertion opening 2810 into which a diagnostic strip can be inserted. Pu is It is a thin, long rod-shaped device, and the diagnostic strip with the sample attached... Pu It may be inserted into the insertion port 2810 of the measuring device 2800.

[0163] In one embodiment, the measuring device 2800 can transmit the measured values ​​of the graphene-based sensor to an external device such as a smartphone. An example of the use of the measuring device will be described later with reference to Figure 34. In another embodiment, the measuring device 2800 may further include an internal circuit capable of analyzing the measured values ​​of the graphene-based sensor. This will be described later with reference to Figures 35 and 36. Furthermore, the measuring device 2800 may further include a display capable of showing the analysis results, or a communication unit capable of transmitting the analysis results and other information to an external device via a wired or wireless network.

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

[0165] The measuring device 2900 may include internal circuitry capable of analyzing the output values ​​of multiple graphene-based sensors. This will be described later with reference to Figures 35 and 36. The measuring device 2900 may further include a display unit for displaying the analysis results and a communication unit for transmitting the analysis results to an external device.

[0166] As another embodiment, quantitative analysis of a sample can be performed using the measuring devices 2800 and 2900 shown in Figure 28 or Figure 29. As shown in Figure 28, the measuring device 2800 has one insertion port. 2 If device 810 is present, multiple diagnostic strips containing standard samples of different concentrations and a diagnostic strip of the sample can be sequentially inserted into the insertion port 2810 of the measuring device 2800, and the graphene-based sensor measurement value for each diagnostic strip can be obtained. Alternatively, if the measuring device 2900 has multiple insertion ports 2910 as shown in Figure 29, multiple diagnostic strips containing standard samples of different concentrations and a diagnostic strip of the sample can be simultaneously inserted into the insertion port 2910 of the measuring device 2900, and the graphene-based sensor measurement values ​​for multiple diagnostic strips can be obtained at once. The measuring devices 2800 and 2900 grasp the change in the graphene-based sensor measurement value (e.g., change in current or voltage) over time for standard samples of different concentrations. Then, the measuring devices 2800 and 2900 can compare the change in the measurement value of the standard samples at different concentrations with the change in the measurement value of the sample to determine the concentration or intensity of the sample.

[0167] Figure 30 shows a flowchart illustrating the measurement method for the sample related to this 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 measuring device 2700 of FIG. 27 for the sake of convenience of explanation.

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

[0170] As another example, the sensor measuring 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 salt (C7H 16 NO6SNa), EDTA, glycine-HCI, HEPBS (C 10 H 22 N2O4S), free HEPES acid (C8H 18 N2O4S), sodium HEPES salt (C8H 17 N2O4SNa), HEPPS (C9H 20 N2O4S), free HEPPSO 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), disodium POPSO 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 hydrochloric acid (C4H 11 NO3·HCl), Tris-(hydroxymethyl)propane (C6H 14Examples include O3, TT-SDS (Tris-Tricine-SDS buffer), and TTE (Tris-TAPS-EDTA buffer). Depending on the example, the buffer solution may be omitted. An example of how to apply the buffer solution 3110 to the sensor 3100 is shown in Figure 31.

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

[0172] The sample is applied to the sensor 2710 (S3020). In one embodiment, if a buffer solution is applied to the sensor 2710, the sample is applied to the sensor 2710 while the buffer solution is still applied. The sensor measuring device 2700 applies a voltage within a certain range to the sensor after the sample is applied (S3030). The voltage applied to the sensor 2710 primarily before the sample is applied and the voltage applied to the sensor 2710 secondary after the sample is applied may be the same voltage. For example, if the voltage applied primarily is a voltage in the range of 0 to 2V in units of 0.1V, the voltage applied secondary may also be a voltage in the range of 0 to 2V in units of 0.1V.

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

[0174] In one embodiment, the sensor measuring device 2700 can output the first measurement value and the second measurement value to a screen or transmit it to an external device. In another embodiment, the sensor measuring device 2700 can output the difference between the first measurement value and the second measurement value to a screen or transmit it to an external device.

[0175] In another embodiment, the sensor measuring device 2700 can output the maximum difference between the first measured value and the second measured value on a screen or transmit it to an external device. For example, if the difference between the first measured value and the second measured value for a voltage of 0.2V is A, and the difference between the first measured value and the second measured value for a voltage of 0.5V is B (>A), the sensor measuring device 2700 can output B, which represents the relatively larger difference.

[0176] In another embodiment, the sensor measuring device 2700 can repeatedly perform the process of measuring the second measurement value. For example, the sensor measuring device 2700 can input a voltage in the range of 0 to 2V to the sensor 2710 at a constant interval at time t1 to acquire multiple second measurement values ​​at time t1. The sensor measuring device 2700 can further input a voltage in the range of 0 to 2V to the sensor 2710 at a constant interval at time t2 to acquire multiple second measurement values ​​at time t2. In this way, multiple second measurement values ​​at time tn can be acquired. The number of times the second measurement value is repeatedly measured and the interval between repeated measurements (i.e., t2-t1) can be varied in various ways depending on the embodiment.

[0177] The sensor measuring device 2700 can determine the maximum difference by comparing multiple second measurement values ​​measured at time t1 with a previously measured first measurement value. Furthermore, the sensor measuring device 2700 can determine the maximum difference by comparing multiple second measurement values ​​measured at time t2 with a first measurement value. In this manner, the maximum difference can be determined by comparing multiple second measurement values ​​measured at time tn with a first measurement value. For example, at time t1, the maximum difference between the first and second measurement values ​​measured at 0.5V may be the maximum, and at time t2, the maximum difference between the first and second measurement values ​​measured at 0.2V may be the maximum. That is, the applied voltages at which the maximum difference between the first and second measurement values ​​appears at each measurement time (t1, t2, ..., tn) may be different from each other.

[0178] In one embodiment, the sensor measuring device 2700 can determine the difference between the first measurement value and the second measurement value as follows.

[0179]

number

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

[0181] In this embodiment, a first measurement value taken before applying the sample to the sensor 2710 and a second measurement value taken after applying the sample to the sensor are obtained, allowing for accurate measurement of the effect of the sample on the sensor. In another embodiment, the sensor measuring device 2700 can more accurately measure the sample by repeatedly measuring the second measurement value at regular time intervals and then using the change in the maximum difference between the second measurement value obtained at each time point (t1, t2, ... tn) and the first measurement value before applying the sample.

[0182] The sensor measuring device 2700 can output the difference between the second measurement value obtained at each point in time and the first measurement value measured before the sample was applied to a screen or transmit it to an external device. In another embodiment, the sensor measuring device 2700 can output the maximum difference between the second measurement value obtained at each point in time and the second measurement value measured before the sample was applied to a screen or transmit it to an external device. For example, the sensor measuring device 2700 can display the maximum difference as a number or graph (e.g., a bar graph or line graph). In yet another embodiment, the sensor measuring device 2700 can output the result of identifying the sample based on the change in the maximum difference to a screen or transmit it to an external device. In yet another embodiment, the sensor measuring device 2700 can output various values ​​such as the applied voltage at each measurement point in time when the maximum difference appears.

[0183] Figure 32 shows an example of a method for measuring a sample according to this disclosure.

[0184] Referring to Figure 32, the sensor measuring device 2700 applies a voltage to the sensor 3200 before the sample is applied and obtains a first measurement value 3210, which includes the sensor's current or resistance. In one embodiment, the sensor measuring device 2700 can obtain a first measurement value 3210 by applying at least one voltage to the sensor. For example, the sensor measuring device 2700 can obtain multiple first measurement values ​​3210 by applying voltages at regular intervals in the range of 0 to 2V.

[0185] The sensor measuring device 2700 applies a voltage to the sensor at time 3220 after the sample is applied and obtains a second measurement value, including the sensor's current or resistance. The voltage applied to the sensor after the sample is applied is the same as the voltage applied to the sensor before the sample is applied. The sensor measuring device 2700 can repeat the process of measuring the second measurement value. For example, the sensor measuring device 2700 applies a voltage at time t1 to obtain the second-first measurement value 3230. The sensor measuring device 2700 also obtains the second-second measurement value 3232 to the second-n measurement value 3234 at time points t2 to tn, respectively.

[0186] The sensor measuring device 2700 determines 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 were measured for voltages V1, V2, and V3. The sensor measuring device 2700 compares the first measurement value 3210 with the second measurement value at time t1 (i.e., the 2-1 measurement value 3230) for each voltage. If the difference between the first measurement value 3210 and the 2-1 measurement value 3230 measured with V2 applied at time t1 is the maximum, the sensor measuring device 2700 determines the difference between the first measurement value 3210 and the 2-1 measurement value 3230 at V2 as the maximum difference at time t1, which is 3240. In this way, at time t2, the voltage differences between the first measurement value 3210 and the second-nth measurement value 3232 are compared to determine the maximum difference 3242, and at time tn, the voltage differences between the first measurement value 3210 and the second-nth measurement value 3234 are compared to determine the maximum difference 3244.

[0187] Figure 33 shows an example of measurement results that can be referenced in various embodiments of this disclosure.

[0188] Referring to Figure 33, we see the first graph 3300, which shows the results of measuring the current when a voltage within a certain range is applied to the sensor before sample application, and the second graph 3310, which shows the results of measuring the current when a voltage within a certain range is applied to the sensor after sample application. Graphs 3300 and 3310 show the sensor measurements for different voltages at regular intervals connected in a curved form. Furthermore, the graphs in this embodiment are merely examples to aid understanding, and the sensor measurement results may be displayed in various graphs or numerical values.

[0189] In one embodiment, if the process of obtaining the second measurement value is repeated as shown in Figure 32, there may be multiple second graphs 3310. That is, there may be a second-first graph showing the second-first measurement value 3230 of the sensor measured at time t1, a second-second graph showing the second-second 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 the sake of explanation.

[0190] The sensor measuring device 2700 can determine the difference in voltage-specific sensor measurements between the first graph and the second graph using mathematical formula 3. The difference in measurements is not limited to mathematical formula 3 and can be determined 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 Figure 32, there may be a graph 3320 showing the difference between the second-first measurement value 3230 and the first measurement value 3210, and a graph 3330 showing the difference between the second-second measurement value 3232 and the first measurement value 3210. In both graphs 3330 and 3340, the sample can be identified based on the positional changes of the maximum value points 3332 and 3342.

[0191] Figure 34 shows an example of the use of the measuring device according to this disclosure.

[0192] Referring to Figure 34, the measuring device 3410 may include a communication unit that can be connected to and used with the smartphone 3400. For example, the measuring device 3410 may be connected to the smartphone via an earphone jack, a charging terminal, or Bluetooth®. Various examples of the measuring device 3410 are shown in Figures 17 to 29.

[0193] The smartphone 3400 can display the voltage values ​​received from the measuring device 3410 on its screen or transmit them to an external device. In another embodiment, the smartphone 3400 can display the results obtained by analyzing the voltage values ​​received from the measuring device 3410 on its screen or transmit them to an external device. For example, the smartphone 3400 may have software that embodies the analysis method shown in Figures 35 to 36, which will be described later.

[0194] In another embodiment, one side of the measuring device 3410 may have information that can identify the measuring device, such as a QR code (registered trademark) or a barcode. If a QR code (registered trademark) is present on the measuring device 3410, the smartphone 3400 can use its camera to recognize the QR code (registered trademark) 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] Figure 35 shows an example of a method for analyzing the measurement results related to this disclosure.

[0196] Referring to Figure 35, the sensor array 3500 outputs measured values ​​3510 (e.g., current or voltage) from multiple graphene-based sensors. The structure of the sensor array 3500 is shown in Figures 1 to 15. In another embodiment, the output terminal of the sensor array 3500 may have an output unit that converts the output current to a voltage. However, for the sake of explanation, it is assumed below that the measured values ​​output from the sensor array 3500 are adjusted to a certain range of voltage values ​​by the output unit. That is, the range of the measured values ​​is normalized to a certain range of values.

[0197] Multiple measurement values ​​3510 output from the sensor array 3500 are input to the artificial neural network 3520. The artificial neural network 3520 may be implemented using various conventional deep learning models such as CNN (Convolutional Neural Network). The artificial neural network 3520 is pre-trained using training data. For example, the artificial neural network 3520 can be trained using a supervised learning method with training data in which multiple measurement values ​​3510 taken from a standard sample using the sensor array are labeled with pre-known information about the standard sample (e.g., wavelength / intensity of light, type / amount of biomolecule, type / amount of gas molecule, pressure intensity, etc.). As an example of a supervised learning method, the learning method of a regression analysis model may be mainly used. Once the artificial neural network 3520 has completed training in this way, upon receiving multiple measurement values ​​3510 taken from the sensor array, it outputs analysis results 3530 (e.g., wavelength / intensity of light, type / amount of biomolecule, type / amount of gas molecule, 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 a sensor array 3500 and an artificial neural network 3520 will be described. The reaction layers of the multiple graphene-based sensors in the sensor array 3500 contain materials with different quantum points that sense light of different wavelengths. For example, the reaction layers of the multiple graphene-based sensors constituting the sensor array 3500 can be composed of quantum points that respond to R, G, and B, respectively. When the pre-trained artificial neural network 3520 receives multiple measurement values ​​3510 from the sensor array, it outputs wavelengths in the visible light region as analysis results. As another example, the reaction layers of multiple graphene-based sensors can be composed of quantum points that sense infrared light in different wavelength bands to detect wavelengths in the infrared region. In this case, the amount of blood glucose can be measured by infrared sensing on the skin. As yet another example, different quantum points that can detect different wavelength bands in the UV-IR region can be configured as the reaction layers of each graphene-based sensor in the sensor array 3500, and the wavelength of the signal can be analyzed.

[0199] As a second embodiment of the analytical method, the type and quantity of biomolecules can be detected using the sensor array 3500 and the artificial neural network 3520. The reaction layers of the multiple graphene-based sensors in the sensor array 3500 may contain biomolecules (e.g., DNA, RNA, antigens, antibodies, enzymes, etc.) that react to each other to different degrees. 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 to 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 to various viruses (i.e., the signal of the measurement) may be different from each other. In other words, the degree to which the first antibody reacts to the first virus may be r1, the degree to which it reacts to the second virus may be r2, and the degrees to which the second antibody reacts to the first and second viruses may be r3 and r4, respectively, and these degrees may be different from each other.

[0200] If the sensor array 3500 outputs multiple measurement values ​​3510 for a sample, the artificial neural network 3520 outputs the type / quantity of virus as an analysis result. To this end, the artificial neural network 3520 can be pre-trained with training data that labels the measurement values ​​3510 of the sensor array 3500 for various viruses by virus type and / or quantity. In this way, antibodies with different reaction degrees to various viruses are configured as reaction layers for each graphene-based sensor, and the type and quantity of the sample can be detected according to the reaction degree of each antibody. Therefore, it is possible to detect a greater number of viruses than the number of graphene-based sensors contained in the sensor array 3500. Furthermore, if the reaction degrees of each antibody in the reaction layer of the graphene-based sensor differ for a new virus, it is possible to detect the new virus by using the existing sensor array 3500 as is and training the artificial neural network with training data for the new virus, without creating additional reaction layers for the sensor array 3500.

[0201] As a third embodiment of the analysis method, a method for diagnosing diseases using a sensor array 3500 and an artificial neural network 3520 will be described. Multiple graphene-based sensors of the sensor array 3500 have reaction layers formed 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 that can detect DNA, and the second graphene-based sensor may include a reaction layer of a substance that can detect enzymes. When the artificial neural network 3520 receives multiple measurement values ​​3510 from the sensor array 3500, it predicts and outputs a disease. That is, a specific disease can be diagnosed by various combinations of measurement values. To this end, the artificial neural network 3520 can be pre-trained with training data in which multiple measurement values ​​obtained by inputting a sample capable of diagnosing a specific disease into the sensor array 3500 are labeled with the disease name.

[0202] Figure 36 shows an example of an artificial neural network that can be referenced in various embodiments of this disclosure.

[0203] Referring to Figure 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 contain one or more network nodes 3621. The one or more network nodes 3621 have interconnections with each other by simulating the synaptic activity of neurons, where human neurons exchange signals through synapses. For example, in the artificial neural network 3600, one or more network nodes 3621 exchange data through convolutional connectivity while being located in layers of different depths (e.g., hidden layers). The artificial neural network 3600 also includes network nodes with various other connectivitys (e.g., recurrent). Such connectivitys between network nodes may be defined as mapping tables 3641, 3642 for each layer of the hidden layer. A set of such mapping tables 3641, 3642 may be abbreviated as mapping table set 3640.

[0204] The mapping table set 3640 is determined by the learning of the artificial neural network 3600 described above. In one embodiment, the mapping table set 3640 may be embodied as various forms of internal circuits. Internal circuits embodied as hardware in this way may be included in the measurement devices 2500 and 2600 in Figures 28 and 29. In other embodiments, the mapping table set 3640 may be embodied as various forms of computer programs. Computer programs embodied as software in this way may be included in the measurement devices 2500 and 2600 in Figures 28 and 29. As a result, the artificial neural network 3600 receives multiple measurement values ​​from the sensor array and outputs analysis results (e.g., wavelength / intensity of light, type / amount of biomolecules, type / amount of gas molecules, pressure intensity, etc.) based on calculations determined in the mapping table set 3640.

[0205] In the flowcharts provided in this disclosure, each operation of the method or algorithm is described in a sequential order; however, the operations may be performed in any order that can be combined. The descriptions of the flowcharts provided in this disclosure do not preclude changes or modifications to the method or algorithm, nor do they imply that any operation is essential or preferred. In one embodiment, at least some operations may be performed in parallel, iteratively, or heuristically. In another embodiment, at least some operations may be omitted, and other operations may be added.

[0206] Various embodiments of this disclosure may be embodied as software on a machine-readable storage medium (MRSM). The software may be software for implementing various embodiments of this disclosure. The software can be inferred from various embodiments of this disclosure by a programmer in the art to which the content of this disclosure belongs. For example, the software may be a computer program containing instructions that can be read by a computing device. A computing device is a device that can operate by instructions called from a storage medium, and may be rephrased as, for example, an electronic device. In one embodiment, the processor of the computing device executes a called instruction, causing the components of the computing device to perform the function corresponding to this instruction. The storage medium can mean any kind of recording medium that is readable by a device and stores information. The storage medium may include, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical information storage device, etc. In one embodiment, the storage medium may be embodied in a distributed form, such as a networked computer system. In this case, the software may be stored and executed in a distributed manner on the computer system. In another embodiment, the storage medium may be a non-transitory storage medium. A non-transitory storage medium means a medium that exists regardless of whether the information data is stored semi-permanently or temporarily, and does not contain signals that are transmitted transiently.

[0207] While the technical concept of this disclosure has been explained through various embodiments, the technical concept of this disclosure includes various substitutions, modifications, and alterations that can be understood by a person with ordinary skill in the art to which this disclosure pertains. Furthermore, such substitutions, modifications, and alterations should be understood to be included within the scope of the attached claims.

Claims

1. circuit board and A doping region formed by doping an impurity in a part of the substrate, A non-conductive layer laminated on the substrate, excluding a portion of the doping region, A graphene layer stacked on the doping region, A first electrode connected to the graphene layer, A second electrode connected to the doping region, The aforementioned doping area is, A first doping region doped at the first concentration, It includes 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, The second electrode is connected to the second doping region. Graphene-based sensor.

2. The graphene-based sensor according to claim 1, wherein the first electrode is formed in a manner that surrounds the graphene layer.

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

4. 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, wherein the second electrode is connected to the one or more via electrodes.

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

6. 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.

7. The graphene-based sensor according to claim 1, further comprising a storage portion formed by etching the substrate to a first depth and a first width, and a channel portion formed by etching the insulator layer to a second depth and a second width, which connects the storage portion and the graphene layer, wherein the storage portion is for storing a liquid sample, and the second depth and the second width are each smaller than the first depth and the first width.

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

9. The graphene-based sensor according to claim 8, wherein the graphene layers are stacked to correspond to the plurality of doping regions, each of the plurality of doping regions includes a plurality of first electrodes, and the second electrodes are commonly connected to the plurality of doping regions.

10. The graphene-based sensor according to claim 8, wherein the graphene layer is stacked across the plurality of doping regions and includes a plurality of second electrodes connected to each of the plurality of doping regions, and the first electrode is connected in common to the plurality of doping regions.

11. The graphene-based sensor according to claim 9 or 10, further comprising a multiplexer that outputs values ​​measured in the graphene layers of the plurality of doping regions.

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

13. The system includes a graphene-based sensor in which a graphene layer is laminated on a doping region formed on a substrate, an electrode connected to the doping region, and an output unit that converts the output current of the graphene-based sensor into a voltage and outputs it. The aforementioned doping area is, A first doping region doped at the first concentration, It includes 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, The electrode is connected to the second doping region. Measuring device.

14. The measuring apparatus according to claim 13, 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.

15. The measuring device according to claim 13, further comprising a display unit for displaying the output voltage of the output unit, and a communication unit for transmitting the output voltage of the output unit to an external source.

16. The measuring device according to claim 13, comprising a sensor array in which the graphene-based sensors are arranged in one 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.

17. The measuring apparatus according to claim 13, further comprising an internal circuit equipped with a mapping table set corresponding to an artificial neural network that receives measurement values ​​appearing when a sample reacts with a substance in the reaction layer of the graphene-based sensor and outputs analytical values.

18. The measuring device according to claim 13, further comprising: a voltage application unit that applies a voltage to a graphene-based sensor within a predetermined range; and a measuring unit that grasps a first measured value of the resistance or current of the graphene-based sensor due to the application of voltage before the application of a sample, and grasps and outputs a second measured value of the resistance or current of the graphene-based sensor due to the application of voltage after the application of a sample.

19. The measuring device according to claim 18, wherein the measuring unit outputs a result value including the maximum difference among the voltage differences between the first measured value and the second measured value.

20. The measuring device according to claim 18, wherein the measuring unit repeatedly performs the process of measuring the second measurement value at regular time intervals, and outputs the second measurement value of the resistance or current of the sensor due to the application of voltage each time the process is repeated.

21. The measuring device according to claim 20, wherein the measuring unit grasps and outputs the change in the maximum difference between the first measured value and the second measured value each time the measurement is repeated.

22. An analysis method that utilizes a sensor array including multiple sensors in which graphene layers are stacked on semiconductor regions doped on a substrate, The steps include receiving multiple measurement values ​​from multiple graphene-based sensors of the sensor array, The step includes receiving the multiple measurement values ​​using an artificial neural network and obtaining analysis results using calculations determined in a mapping table set, The aforementioned semiconductor region includes a doping region. The aforementioned doping area is, A first doping region doped at the first concentration, It includes 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, The second doping region is connected to the electrode. Analysis method.

23. The analytical method according to claim 22, wherein the material of the reaction layer coated on the graphene layer of the plurality of graphene-based sensors is partially different from that of other parts.

24. The analytical method according to claim 22, wherein the material of the reaction layer of the plurality of graphene-based sensors is at least one of quantum points, biomolecules, gas-reactive molecules, and pressure-reactive structures.

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