Multifunctional Heterojunction Metal Oxide Gas Sensor

Abstract

A method for identifying a gas is provided. The method includes providing a gas sensor device comprising at least two stacked metal oxide layers, wherein a change in conductance of the gas sensor device in the presence of a gas varies with the temperature of the stacked metal oxide layers. The method includes bringing a gas into proximity with the stacked metal oxide layers. The method also includes measuring the conductance of the gas sensor device while the gas is in proximity to the stacked layers at multiple temperatures to generate a temperature-conductance profile. The method also includes identifying a gas of interest based on the temperature-conductance profile.

Description

Technical Field

[0001] The present disclosure relates to a multifunctional heterojunction metal oxide gas sensor and a method for manufacturing the multifunctional heterojunction metal oxide gas sensor.

Background Art

[0002] Metal oxide semiconductor (MOX or MOS) materials are used as gas sensing elements due to their chemoresistive behavior. SnO2, which is an n-type MOX, is an example of a material used in MOX-type commercial devices and platforms because it provides a good trade-off between sensitivity and stability (i.e., resistance to signal degradation over time). In the context of multisensory gas detection solutions (i.e., electronic noses), access to diverse linearly independent sensor outputs is a factor that enables the discrimination of various volatile substances and mixtures.

Summary of the Invention

Means for Solving the Problems

[0003] One embodiment relates to a method for identifying a gas. The method includes providing a gas sensor device comprising at least two stacked metal oxide layers, wherein a change in the conductance of the gas sensor device in the presence of the gas varies with the temperature of the stacked metal oxide layers. The method includes bringing the gas into proximity with the stacked metal oxide layers. The method also includes measuring the conductance of the gas sensor device when the gas is in proximity to the stacked layers at a plurality of temperatures to generate a temperature-conductance profile. The method also includes identifying the target gas based on the temperature-conductance profile.

[0004] One embodiment relates to a multifunctional heterojunction metal oxide gas sensor device. The gas sensor device includes a substrate, at least two electrodes formed on the substrate, a first metal oxide layer formed on the substrate and the electrodes, and a second metal oxide layer formed on the first metal oxide layer. The change in the conductance of the gas sensor device in the presence of a gas varies with the temperature of the first and second metal oxide layers.

[0005] The above summary is not intended to describe every illustrated embodiment or every implementation of the present disclosure.

[0006] The drawings included in this application are incorporated herein and form a part hereof. The drawings illustrate embodiments of the present disclosure and, together with the description, explain the principles of the present disclosure. The drawings are only examples of certain embodiments and do not limit the present disclosure.

Brief Description of the Drawings

[0007]

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Best Mode for Carrying Out the Invention

[0008] It should be recognized that the elements in the drawings are illustrated for simplicity and clarity. Well-known elements that may be useful or necessary in a commercially realizable embodiment may not be shown for simplicity and to aid in the understanding of the illustrated embodiments.

[0009] The present disclosure describes a multifunctional heterojunction metal oxide gas sensor and a method for manufacturing the multifunctional heterojunction metal oxide gas sensor. In particular, the present disclosure describes a multifunctional heterojunction metal oxide gas sensor device including two or more MOX thin films having different detection characteristics. In one example, the first layer is an n-type SnO2 layer and the second layer is a p-type NiO layer. By varying the operating temperature, the response of this multilayer device changes from being dominated by the upper layer (e.g., p-type layer) to being dominated by the lower layer (e.g., n-type layer). Thus, the term multifunctional can refer to the ability of the gas sensor to have either (or both) p-type response characteristics or n-type response characteristics depending on the operating temperature or material composition or both of different thin films. In other embodiments, in the case of multiple different n-type (or p-type) layers, the response characteristics can be those of any one or a combination of both of these layers. The stack effectively functions as a multiplexed sensor in which the detection characteristics of each layer or combinations thereof and diverse responses to gases can be individually accessed on demand by varying the operating temperature.

[0010] Various embodiments of the present disclosure are described herein with reference to the accompanying drawings. Alternative embodiments may be devised without departing from the scope of the present disclosure. Note that various connections and positional relationships (e.g., above, below, adjacent, etc.) between the elements of the following description and the drawings are set forth. These connections or positional relationships or both can be direct or indirect, and the present disclosure is not intended to be limited in this regard. Thus, the connection of entities can refer to either a direct or an indirect connection, and the positional relationship between entities can be a direct or an indirect positional relationship. As an example of an indirect positional relationship, a reference in this description to forming layer "A" above layer "B" includes a situation where there is one or more intermediate layers (e.g., layer "C") between layer "A" and layer "B" as long as the relevant characteristics and functions of layer "A" and layer "B" are not substantially changed by the intermediate layer(s).

[0011] The following definitions and abbreviations shall be used for the interpretation of the claims and the specification. As used herein, the terms "comprising", "comprises", "including", "includes", "having", "has", "containing", or "contains", or any other variations thereof, are intended to cover non-exclusive inclusions. For example, a composition, mixture, process, method, article, or apparatus comprising a list of elements is not necessarily limited to only those elements, but may include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.

[0012] For the purposes of the following description, "above", "below", "right", "left", "vertical", "horizontal", "upper", "lower", and their derivatives shall relate to the structures and methods described in the orientation in the drawings. The terms "lying on top of", "at the top of", "above", "disposed above", or "disposed at the top of" mean that a first element, such as a first structure, is present on a second element, such as a second structure, and an intervening element, such as an interface structure, may be present between the first element and the second element. The term "direct contact" means that a first element, such as a first structure, and a second element, such as a second structure, are connected without an intermediate conductive layer, insulating layer, or semiconductor layer at the interface of the two elements. It should be noted that the term "selective", such as "second element selective first element", means that the first element can be etched and the second element can act as an etch stop.

[0013] For the sake of brevity, the prior art related to semiconductor device and integrated circuit (IC) fabrication may or may not be described in detail herein. Further, the various tasks and process steps described herein may be incorporated into more comprehensive procedures or processes having additional steps or functionality not described in detail herein. In particular, the various steps in the manufacture of semiconductor devices and semiconductor-based ICs are well known, and thus, for the sake of brevity, many of the conventional steps are either briefly mentioned herein or completely omitted without providing details of well-known processes.

[0014] Generally, the various processes used to form microchips (or gas sensor devices) packaged in ICs are divided into four general categories: namely, film deposition, removal / etching, semiconductor doping, and patterning / lithography.

[0015] Deposition is any process of growing, coating, or otherwise transferring material onto a wafer. Available techniques specifically include physical vapor deposition (PVD), chemical vapor deposition (CVD), electrochemical deposition (ECD), molecular beam epitaxy (MBE), and more recently atomic layer deposition (ALD). Another deposition technique is plasma enhanced chemical vapor deposition (PECVD), which is a process that utilizes the energy within a plasma to cause reactions on the wafer surface that would otherwise require higher temperatures associated with conventional CVD. The electrical and mechanical properties of the film can also be improved by the energy ion bombardment during PECVD deposition.

[0016] Removal / etching is any process that removes material from a wafer. Examples include etching processes (wet or dry), chemical-mechanical planarization (CMP), etc. An example of a removal process is ion beam etching (IBE). Generally, IBE (or milling) refers to a dry plasma-etching method that utilizes a remote, broad beam of ions / plasma to remove substrate material by means of a physically inert gas, a chemically reactive gas, or both. Similar to other dry plasma-etching techniques, IBE has advantages such as etching rate, anisotropy, selectivity, uniformity, aspect ratio, and minimization of substrate damage. Another example of a dry removal process is reactive ion etching (RIE). Generally, RIE uses a chemically reactive plasma to remove material deposited on a wafer. In RIE, a plasma is generated under low pressure (vacuum) by an electromagnetic field. High-energy ions from the RIE plasma attack the wafer surface and react with the wafer surface to remove the material.

[0017] Semiconductor doping is generally a modification of electrical properties, for example, by doping the source and drain of a transistor by diffusion, ion implantation, or both. After these doping processes, furnace annealing or rapid thermal annealing ("RTA") is performed. Annealing serves to activate the implanted dopants. Films of both conductors (e.g., polysilicon, aluminum, copper, etc.) and insulators (e.g., various forms of silicon dioxide, silicon nitride, etc.) are used to connect and insulate transistors and their components. By selectively doping various regions of a semiconductor substrate, the conductivity of the substrate can be changed by applying a voltage. By fabricating structures of these various components, millions of transistors can be constructed and wired together to form the complex circuits of state-of-the-art microelectronic devices.

[0018] Semiconductor lithography is the formation of a three-dimensional relief image or pattern on a semiconductor substrate for later transfer of the pattern to the substrate. In semiconductor lithography, a pattern is formed by a photosensitive polymer called a photoresist. To construct the complex structures that make up transistors and the numerous wirings that connect the millions of transistors in a circuit, the steps of lithography and etching / pattern transfer are repeated multiple times. Each pattern printed on the wafer is aligned with previously formed patterns, and conductors, insulators, and selectively doped regions are gradually built up to form the final device.

[0019] Here, turning more specifically to an overview of the technology related to aspects of the present disclosure, generally, metal oxide semiconductor (MOX or MOS) materials are used as gas sensing elements due to their chemoresistive behavior. SnO2, which is an n-type MOX, is one of the common materials found in MOX-type commercial devices and platforms because it exhibits an effective trade-off between sensitivity and stability (i.e., resistance to signal degradation over time).

[0020] In the context of multisensory gas detection solutions (i.e., electronic noses), access to diverse linearly independent sensor outputs is a factor that enables the discrimination of various volatile substances and mixtures. Unfortunately, due to the predominance of SnO2 in the overall MOX sensor market, many of the available sensors consist of variations of this material, so the range of available responses is limited. Variations of these types of SnO2 sensors are achieved, for example, by varying the doping concentration of the SnO2 material or by changing the physical shape (i.e., decoration) of the SnO2 particles. However, these do not result in response ranges that are sufficiently different from each other.

[0021] To overcome these possible limitations, in one embodiment, the multifunctional heterojunction metal oxide gas sensor may include a p-type MOX layer or a combination of an n-type MOX layer and a p-type MOX layer. The p-type MOX layer alone may provide diverse responses. However, the sensitivity may be too low. In one embodiment, the multifunctional heterojunction metal oxide gas sensor provides a combination of an n-type layer and a p-type layer at the nanoscale, and these devices may exhibit some improvement in detection performance. In such an embodiment, the addition of the p-type MOX layer serves to enhance the detection characteristics of the primary detection element (e.g., the n-type MOX layer), and the output of the n-type MOX layer is always dominant.

[0022] In one embodiment of the present invention, the multisensory solution is integrated into a single structure that can exhibit a diverse set of sensor responses with the ability to access these different responses in a simple manner.

[0023] Generally, the gas sensor device of an embodiment of the present invention includes two stacked MOX layers that contact each other at the junction. Each MOX layer is a chemoresistive material whose resistance depends on the type of gas to which it is exposed and its concentration. These MOX detection layers are thin, granular, and porous, so that under normal conditions, gas from the environment permeates throughout the structure and reacts with both the upper and lower layers. In one embodiment, the thicknesses (as well as their chemical compositions) of the different MOX layers may determine the performance characteristics of the device. The thicknesses of these layers may also result in unexpectedly valuable detection behavior as described herein.

[0024] As described in further detail below, sensor resistivity data is measured over a period of time in the presence of one or more analyte gases. An example of a graph of this measurement data is shown, for example, in FIGS. 3 and 4 of the present application. These resistivity curves have certain patterns that can be identified to determine the presence of the analyte gas. In certain embodiments, an operator (or user) can manually identify the presence of the gas by examining the graph. In other embodiments, an artificial intelligence system and a deep learning neural network can be utilized to determine the presence of the gas. In certain embodiments, a system is provided that is configured to perform real-time gas detection based on the resistivity data of the gas sensor device and automatically discover the identity of these gases (i.e., without requiring user input).

[0025] Data analysis and data science are becoming increasingly popular. Due to the rapid increase in the Internet of Things (IoT) and sensor devices (such as the gas sensor device of embodiments of the present invention), as well as the growing popularity of cloud computing, the quality of data collected in real time from various different components has become important. Certain embodiments utilize an algorithm based on a combination of deep learning (such as a recurrent neural network) and signal processing techniques to train a model and then compare real-time data to predictions from the existing model to discover the presence of the analyte gas.

[0026] In embodiments of the present invention, neural networks and other deep learning systems may be utilized to assist in gas detection of a gas sensor device. An artificial neural network (ANN) (more generally also referred to as a neural network) is a computing system composed of several simple, highly interconnected processing elements (nodes) that process information by dynamic state responses to external inputs. An ANN is a processing device (algorithm and / or hardware or both) that roughly models, but on a much smaller scale, the structure of neurons in the mammalian cerebral cortex. Such systems can learn tasks progressively and autonomously using examples, and are well - applied to, for example, speech recognition, text processing, and computer vision. Large - scale ANNs can have hundreds or thousands of processor units, while the mammalian brain has billions of neurons, and correspondingly, the scale of overall interactions and emergent behavior also increases.

[0027] Many types of neural networks are known, including feed - forward neural networks such as multi - layer perceptrons, deep learning neural networks (DNN), and convolutional neural networks. A feed - forward neural network is an artificial neural network (ANN) in which the connections between units do not form cycles. A deep learning neural network is an artificial neural network that has multiple hidden layers of units between an input layer and an output layer. Similar to shallow ANNs, DNNs can model complex non - linear relationships. For example, DNN architectures for object detection and analysis generate compositional models in which an object is represented as a hierarchical composition of image primitives. Additional layers enable the composition of features from lower layers, giving the possibility of modeling complex data with fewer units than shallower networks of similar performance. DNNs are typically designed as feed - forward networks.

[0028] In certain embodiments described herein, systems, methods, and computer program products are provided that use data from a gas sensor device and artificial intelligence (AI) to facilitate gas detection. Machine learning, a subset of AI, utilizes algorithms to learn from data (such as gas sensor data) and generate insights based on this data. AI refers to the intelligence when a machine can make decisions that maximize the likelihood of success in a given topic based on information. In particular, AI can learn from a data set to solve problems and provide relevant recommendations and judgments. AI is a subset of cognitive computing that refers to systems that can learn on a large scale, reason purposefully, and interact with humans and nature. Cognitive computing is a blend of computer science and cognitive science. Cognitive computing utilizes self-learning algorithms that use data, visual recognition, and natural language processing to solve problems and optimize processes.

[0029] Now, referring first to FIG. 1 where like numerals represent the same or similar elements, a cross-sectional view of an example of a multifunctional heterojunction metal oxide gas sensor device is shown. In this example, the gas sensor device 100 includes a substrate 102. In certain embodiments, the substrate 102 is an insulating substrate and can serve as a base layer on which additional layers are formed. The substrate 102 can also be used for supporting and handling the gas sensor device 100. A pair of electrodes 104 are formed on the substrate 102. The electrodes 104 are composed of an electrically conductive material (or a combination of electrically conductive materials) and provide a path for current to flow through the sensing structure.

[0030] As shown in FIG. 1, two detection layers are deposited on the insulating substrate 102 and the patterned electrode 104. The first detection layer is the n-type metal oxide layer 106, which is conformally deposited on the insulating substrate 102 and the electrode 104. In certain embodiments, the n-type metal oxide layer 106 may be subjected to CMP (or other suitable planarization process) to planarize the layer. In certain embodiments, the n-type metal oxide material may be SnO2, ZnO, TiO2, WO3, In2O3, Fe2O3, MgO, CaO, ZrO2, V2O5, Nb2O5, Ta2O5, MoO3, Al2O3, Ga2O3, HfO2, Cr2O3, or CuO. However, it should be recognized that other suitable materials or combinations of materials may be used for the n-type metal oxide layer 106. Further, it should be recognized that one or more of the n-type metal oxide materials listed above may also exhibit p-type conductivity depending on how they are fabricated. In certain embodiments, the n-type metal oxide layer 106 may have high sensitivity (e.g., analyte gas at sub-ppm levels under controlled conditions). The characteristic of the n-type metal oxide material is that the resistance of the layer decreases when exposed to a reducing analyte, as described in more detail below.

[0031] Referring back to FIG. 1, a second detection layer, which is a p-type metal oxide layer 108, is then formed over the n-type metal oxide layer 106. In certain embodiments, the p-type metal oxide material can be NiO, Co3O4, Cr2O3, Mn3O4, Mn2O3, Y2O3, La2O3, CeO2, PdO, Ag2O, Bi2O3, Sb2O3, TeO2, Fe2O3, or HfO2. However, it must be recognized that other suitable materials or combinations of materials may be used for the p-type metal oxide layer 108. Further, it must be recognized that one or more of the p-type metal oxide materials listed above may also exhibit n-type conductivity depending on how they are fabricated. In certain embodiments, the p-type metal oxide layer 108 can have various selectivities (i.e., different surface characteristics). In certain embodiments, the difference in humidity of the p-type metal oxide layer 108 can have a limited effect on the sensitivity of the gas sensor device 100. In certain embodiments, the p-type metal oxide layer 108 can have low sensitivity (e.g., 10 to 100 times lower sensitivity than the n-type metal oxide layer 106 which can be a result of the conduction mechanism).

[0032] The metal oxide layers are described with respect to the embodiment shown in FIG. 1, but it must be recognized that in other embodiments, more than three layers may be utilized. In the example shown in FIG. 1, the combined film thickness of the n-type metal oxide layer 106 and the p-type metal oxide layer 108 can range from 10 to 200 nm. In the example shown in FIG. 1, the width of the electrode gap between the electrodes 104 can range from 10 to 100 um. However, it must be recognized that other film thicknesses or electrode gap dimensions may be used. The current flow through the gas sensor device 100 (or equivalently the sensor resistance) depends on the environment to which the gas sensor device 100 is exposed and thus provides a means for detecting various types of gases.

[0033] As also shown in FIG. 1, a heat source or heating element 110 is disposed below the insulating substrate 102. This heating element 110 is used to bring the detection layer (i.e., the n-type metal oxide layer 106 and the p-type metal oxide layer 108) to a desired operating temperature (usually 100° C. to 500° C.). By operating the MOX material at a high temperature (e.g., 100° C. to 500° C.), surface reactions can be induced and desorption can be promoted. In certain embodiments, the higher operating temperature of the MOX material can be utilized to achieve improved sensitivity, minimize response and recovery times, and in some cases provide some gas discrimination.

[0034] Viewed individually, each detection layer is characterized by its own response pattern when exposed to a series of gases. As an example, the upper layer (e.g., the p-type metal oxide layer 108) strongly responds to gas A but not to gas B, while the lower layer (e.g., the n-type metal oxide layer 106) may exhibit the opposite behavior. Not only the amplitude, but also the direction of the resistance change can be different between the upper and lower detection layers. This is typically the case when comparing an n-type MOX layer with a p-type MOX layer. That is, when exposed to a reducing gas (or reducing analyte) such as H2 or a different complex hydrocarbon, the resistance of the n-type MOX layer decreases, while the resistance of the p-type MOX layer increases.

[0035] Next, referring to FIG. 2A, a graph is shown depicting the resistance (i.e., the y-axis of the graph) of an example of an n-type metal oxide (MOX) layer over time (i.e., the x-axis of the graph) in the presence of a reducing analyte (or gas). As shown in FIG. 2A, the resistance (R 空気 ) of just air before the analyte gas is introduced is initially high. At the point when the analyte gas is introduced, the resistance gradually decreases from a higher R 空気 level to a lower R ガス level. When the analyte gas is removed or otherwise exhausted until only air is present again, the resistance increases again from a lower R ガス level to a higher R 空気It gradually increases with the level. Although not shown in Figure 2A, the opposite behavior can be observed upon exposure to oxidizing gases such as O3 (ozone) (in contrast to reducing analytes or gases), in which case the resistance of the n-type MOX first increases when exposed to the oxidizing analyte gas.

[0036] Next, referring to Figure 2B, a graph showing the resistance (i.e., the y-axis of the graph) of an example of a p-type metal oxide (MOX) layer over time (i.e., the x-axis of the graph) in the presence of a reducing analyte (or gas) is shown. As shown in Figure 2B, the resistance (R 空気 ) is initially low for just air before the analyte gas is introduced. At the point when the analyte gas is introduced, the resistance increases gradually from a lower R 空気 level to a higher R ガス level. When the analyte gas is removed or otherwise exhausted until only air is present again, the resistance gradually decreases from a higher R ガス level back to a lower R 空気 level. Although not shown in Figure 2B, the opposite behavior can be observed upon exposure to oxidizing gases such as O3 (ozone) (in contrast to reducing analytes or gases), in which case the resistance of the p-type MOX first decreases when exposed to the oxidizing analyte gas.

[0037] The data shown in Figures 2A and 2B are for a single-layer gas sensor device (i.e., either an n-type MOX layer or a p-type MOX layer), and it must be recognized that the response and recovery time scales and profiles can vary significantly between the n-type layer and the p-type layer.

[0038] In certain embodiments, the differences in these detection responses are utilized by a machine learning (ML) algorithm to generate a model that can discriminate between different substances or mixtures, identify volatile substances, and characterize different gas environments. Responses that are simply proportional to each other (i.e., linearly dependent) cannot add information that can be used for gas discrimination (apart from the possibility of reducing measurement noise).

[0039] The multilayer MOX gas sensor device according to an embodiment of the present invention is integrated into a single structure that exhibits different detection behaviors. First, by changing the operating temperature of the gas sensor device 100 (e.g., using the heating element 110 shown in FIG. 1), the entire structure can exhibit a response characteristic of any of the detection layers. In the case of two stacked layers, one being n-type and the other being p-type, the observed detection behavior can shift from n-type dominance to p-type dominance depending on the temperature, as will be described in more detail below with respect to FIGS. 3 and 4. In addition, due to the shape described herein (where two layers of a given thickness contact at the junction), a third operating state occurs where the sensor response is a complex combination of the responses of the individual layers. Such a combination results in a unique profile of resistance (or equivalently current versus time) that cannot be generated by any set of operating conditions in a single-layer gas sensor device structure. Valuable features can be extracted from the profiles generated by each layer individually or by their combination and provided to a machine learning algorithm for gas detection purposes.

[0040] Next, referring to FIG. 3, a graph is shown depicting the current (i.e., the y-axis of the graph) of a two-layer gas sensor device (e.g., the gas sensor device shown in FIG. 1) having an n-type metal oxide layer and a p-type metal oxide layer over time (i.e., the x-axis of the graph) in the presence of toluene. Several lines corresponding to several different operating temperatures of the gas sensor device (i.e., temperatures in the range of 220°C to 400°C in 20-degree increments) are shown in the graph. In particular, FIG. 3 shows the output of a heterojunction MOX gas sensor device comprising two stacked layers, a 70-nm-thick SnO2 lower layer (n-type) and a 15-nm-thick NiO upper layer (p-type). As the temperature is varied from low (220°C) to high (400°C), the baseline current (i.e., the current measured when the gas to be detected is absent) gradually increases. This baseline trend is often observed in various single-layer MOX-based sensors, as described above with respect to FIGS. 2A and 2B.

[0041] When a target reducing gas (toluene at 200 ppb in this example) is brought close to the surface of the sensor (during the time from ~700 seconds in FIG. 3), it is detected as a change in current. At low operating temperatures (220 °C ≤ T ≤ 280 °C), the current increases as is characteristic of an n-type MOX sensor. This indicates that the lower layer (n-type) dominates the response of the device. On the other hand, when the same gas exposure is provided while operating the device at a high temperature (340 °C ≤ T ≤ 400 °C), a decrease in current characteristic of a p-type MOX sensor is brought about. This indicates that at high temperatures, the response is dominated by the upper p-type MOX layer (i.e., not by the lower n-type MOX layer).

[0042] Finally, at intermediate operating temperatures, the device response is not dominated by either the n-type or p-type layer, but instead is a combination of the n-type and p-type layers. As shown in FIG. 3, the responses of different materials occur over different time scales and these do not exactly cancel each other out. Instead, a third distinct set of profiles is observed where the current rapidly increases in the initial stage after gas exposure (~700 seconds) (e.g., the 300 °C curve), but ultimately drops to a level below the original baseline immediately thereafter. The profiles in this temperature range do not match those of a single-layer device and are unique to the stacked layer configuration of the gas sensor device 100 of the embodiments of the present invention and can thus provide additional information when extracted and processed by a machine learning classification algorithm.

[0043] Next, referring to FIG. 4, there is shown a graph depicting the current (i.e., the y-axis of the graph) of a two-layer gas sensor device having an n-type metal oxide layer and a p-type metal oxide layer (e.g., the gas sensor device shown in FIG. 1) in the presence of acetone over time (i.e., the x-axis of the graph). Similar to the graph of FIG. 3, several lines corresponding to several different operating temperatures of the gas sensor device (i.e., temperatures in the range of 220° C. to 400° C. in 20-degree increments) are shown on the graph. In particular, FIG. 4 shows the output of a heterojunction MOX gas sensor device comprising two stacked layers, a 70-nm-thick SnO2 lower layer (n-type) and a 15-nm-thick NiO upper layer (p-type). As the temperature is varied from a low temperature (220° C.) to a high temperature (400° C.), the baseline current (i.e., the current measured when the gas to be detected is absent) gradually increases.

[0044] When the target reducing gas (acetone in this example) is brought close to the surface of the sensor (at the time of about 700 seconds in FIG. 3), it is detected as a change in current. At low operating temperatures (220° C. ≦ T ≦ 280° C.), the current increases as is characteristic of an n-type MOX sensor. This indicates that the lower layer (n-type) dominates the response of the device. On the other hand, when the same gas exposure is provided while operating the device at a high temperature (340° C. ≦ T ≦ 400° C.), a decrease in current characteristic of a p-type MOX sensor is brought about. This indicates that at high temperatures, the response is dominated by the upper p-type MOX layer (i.e., not by the lower n-type MOX layer).

[0045] Finally, similar to the example shown in Figure 3 above for toluene, at intermediate operating temperatures, the device response shown in Figure 4 is not dominated by either the n-type or p-type layer, but instead is a combination of the n-type and p-type layers. As shown in Figure 4, a third distinct set of profiles is observed where the current rapidly increases in the initial stage after gas exposure (for example, the 300 °C curve) but ultimately drops to a level below the original baseline immediately thereafter. Similar to the example described above with respect to Figure 3, the profiles in this temperature range do not match the profiles of single-layer devices and are unique to the stacked layer configuration of the gas sensor device 100 of the embodiments of the present invention and can thus provide additional information when extracted and processed by a machine learning classification algorithm.

[0046] It must be recognized that Figures 3 and 4 show examples of one gas sensor device 100 exposed to two different gases at different temperatures. In these examples, the overall trends of the current over time at different temperatures can be somewhat similar. However, there are certain differences in the shape of the curves that can be processed and distinguished with the help of a machine learning classification algorithm.

[0047] Next, referring to Figure 5, this figure shows the response of an example of a heterojunction MOX gas sensor device 100 (i.e., a two-layer structure comprising a 70 nm thick SnO2 n-type layer and a 15 nm thick NiO p-type layer) when exposed to three different gases (i.e., acetone, ethanol, and toluene). In this example, a temperature profile similar to that shown in Figure 3 is generated. In Figure 5, the graph shows the amplitude of the response (defined as ΔR / R after converting the current values in Figure 3 to resistance R). As shown in Figure 5, the response of the same device to different gases depends not only on the operating temperature (indicated by the change in amplitude as a function of temperature) but also on the type of gas being detected. Thus, in one example of the embodiments of the present invention, the gas sensor device is partially selective with respect to certain gases compared to other gases (i.e., the gas sensor device responds preferentially to some gases). This can enable improved detection and discrimination of gases.

[0048] Referring now to FIG. 6, this figure shows the response of a heterojunction MOX gas sensor device 100 containing the same materials (SnO2 and NiO) but different thicknesses (i.e., a two-layer structure including an SnO2 n-type layer with a thickness of 70 nm and a NiO p-type layer with a thickness of 10 nm) compared to the device of FIG. 5 to the same three same-concentration gases. The behavior of the two devices (i.e., devices with different layer thicknesses) is significantly different. This finding emphasizes the importance of controlling the device layer thickness within an appropriate range to cause the emergence of the detection behavior described herein (the transition of the response from n-type to p-type). Thereby, the range of patterns that can be generated by a multisensory gas solution can also be expanded by fabricating devices with different thicknesses or materials or both.

[0049] Embodiments of the present invention can be applied to various combinations of an n-type MOX material (e.g., SnO2, In2O3, WO3, ZnO, MgO, TiO2,...) and a p-type MOX material (e.g., NiO, Co3O4, PdO, Ag2O,...). Such combinations can each result in a different set of response profiles (n-type dominant, p-type dominant, or a combination of n-type and p-type). Therefore, embodiments of the present invention can enable a wide variety of gas sensors with ideal characteristics for integration into a multisensory platform for gas identification.

[0050] It must be recognized that the concept of gas detection is not limited to the p-n two-layer combinations described above. In certain embodiments, n-n or p-p heterojunctions (i.e., different MOX materials whose electrical transport properties depend on the same majority carriers) may also be capable of providing a profile characteristic of individual layers or combinations thereof.

[0051] It should also be recognized that the embodiments are not limited to a two-layer gas sensor device structure. In other embodiments, three or more MOX layers may be used. That is, these embodiments with a larger number of layers can generalize the multiplexing effect (i.e., the ability to select a characteristic response for a selected layer within the stack) to a multi-layer body (N>2), and different temperatures can be used to "activate" each layer and access its detection characteristics.

[0052] The embodiments of the multi-layer gas sensor device described herein may enable the implementation of advantageous gas detection methods. By incorporating diverse response patterns (such patterns can be accessed simply by varying the operating temperature) into a single device, it may be possible to improve selectivity in gas detection while enabling improvements in the manufacturing process, lower power consumption, and a smaller footprint. The flexibility of the concept of this device may enable a wide variety of sensor responses resulting from various combinations of MOX materials, various thicknesses of each MOX layer, and various numbers of layers.

[0053] Next, referring to FIG. 7, an exemplary processing system 500 to which an embodiment of the present invention may be applied is shown according to one embodiment. The processing system 500 includes at least one processor (CPU) 504 operably coupled to other components via a system bus 502. A cache 506, a read-only memory (ROM) 508, a random-access memory (RAM) 510, an input / output (I / O) adapter 520, a sound adapter 530, a network adapter 540, a user interface adapter 550, and a display adapter 560 are operably coupled to the system bus 502.

[0054] The first memory device 522 and the second memory device 524 are operably coupled to the system bus 502 by the I / O adapter 520. The memory devices 522 and 524 can be any of a disk memory device (e.g., a magnetic or optical disk memory device), a solid state magnetic device, etc. The memory devices 522 and 524 may be of the same type of memory device or different types of memory devices.

[0055] The speaker 532 is operably coupled to the system bus 502 by the sound adapter 530. The transceiver 542 is operably coupled to the system bus 502 by the network adapter 540. The display device 562 is operably coupled to the system bus 502 by the display adapter 560.

[0056] The first user input device 552, the second user input device 554, and the third user input device 556 are operably coupled to the system bus 502 by the user interface adapter 550. The user input devices 552, 554, and 556 can be any of a keyboard, a mouse, a keypad, an image capture device, a motion detection device, a microphone, a device incorporating at least two of the functions of the aforementioned devices, or any other suitable type of input device. The user input devices 552, 554, and 556 may be of the same type of user input device or different types of user input devices. The user input devices 552, 554, and 556 are used to input and output information to and from the system 500. In certain embodiments, a neural network component 590 having the ability to identify the shape and pattern of a gas sensor response output is operably coupled to the system bus 502. That is, an AI or other deep learning system can be used as the neural network component 590 to learn the shape and pattern of the gas sensor output to identify one or more gases.

[0057] The processing system 500 can include other elements (not shown) and can omit certain elements, as would be readily anticipated by one of ordinary skill in the art. For example, various other input devices or output devices or both can be included in the processing system 500 depending on its particular implementation, as would be readily understood by one of ordinary skill in the art having ordinary skill. For example, various types of wireless or wired or both input devices or output devices or both can be used. Further, additional processors, controllers, memories, etc. of various configurations can be utilized, as would be readily recognized by one of ordinary skill in the art. These and other variations of the processing system 500 are readily anticipated by one of ordinary skill in the art based on the teachings of the present disclosure provided herein.

[0058] The present disclosure includes a detailed description of cloud computing, but it should be understood that the embodiments of the teachings described herein are not limited to a cloud computing environment. Rather, embodiments of the invention can be implemented in conjunction with any other type of computing environment now known or later developed.

[0059] Cloud computing is a service delivery model that enables convenient on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a service provider. This cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0060] The characteristics are as follows.

[0061] On-demand self-service: Cloud consumers can unilaterally provision computing capabilities such as server time and network storage as needed, without the need for human interaction with service providers.

[0062] Broad network access: The capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0063] Resource pooling: The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with various physical and virtual resources dynamically assigned and reassigned according to demand. Consumers generally have no control over or knowledge of the exact location of the provided resources, but there is a sense of location independence in that they can specify location at a more abstract level (e.g., country, state, or data center).

[0064] Rapid elasticity: Capabilities can be elastically provisioned rapidly, in some cases automatically, to scale out quickly and released rapidly to scale in. To the consumer, the capabilities available for provisioning often appear to be infinite and can be purchased in any quantity at any time.

[0065] Measured service: The cloud system automatically controls and optimizes resource use by leveraging metering capabilities at some appropriate level of abstraction for the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both the provider and consumer of the utilized service.

[0066] The service model is as follows.

[0067] Software as a Service (SaaS): The ability provided to the consumer is to use the provider's applications that run on cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or individual application capabilities, except in some cases for limited user-specific application configurations.

[0068] Platform as a Service (PaaS): The ability provided to the consumer is to deploy consumer-created or obtained applications, created using programming languages and tools supported by the provider, onto cloud infrastructure. The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, or storage, but can control the deployed applications and, in some cases, the application hosting environment configuration.

[0069] Infrastructure as a Service (IaaS): The ability provided to the consumer is to provide processing, storage, networking, and other basic computing resources, and the consumer can deploy and run any software that may include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure, but can control the operating systems, storage, deployed applications, and, in some cases, selectively control certain networking components (e.g., host firewalls).

[0070] The configuration model is as follows.

[0071] Private cloud: The cloud infrastructure is operated exclusively for a certain organization. The cloud infrastructure can be managed by that organization or a third party and can exist on-premises or off-premises.

[0072] Community cloud: The cloud infrastructure is shared by several organizations and supports a specific community with shared concerns (e.g., mission, security requirements, policies, and compliance considerations). The cloud infrastructure can be managed by these organizations or a third party and can exist on-premises or off-premises.

[0073] Public cloud: The cloud infrastructure is made available to the general public or a large industry group and is owned by an organization that sells cloud services.

[0074] Hybrid cloud: The cloud infrastructure continues to be a distinct entity but is a composition of two or more clouds (private, community, or public) tied together by standardized or proprietary technologies (e.g., cloud bursting for load balancing between clouds) that enable data and application portability.

[0075] The cloud computing environment is service-oriented and emphasizes statelessness, low coupling, modularity, and semantic interoperability. At the center of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0076] Next, referring to FIG. 8, an exemplary cloud computing environment 650 is shown. As illustrated, cloud computing environment 650 includes one or more cloud computing nodes 610 that may be communicated with by a local computing device used by a cloud consumer such as, for example, a personal digital assistant (PDA) or cellular phone 654A, a desktop computer 654B, a laptop computer 654C, or an automotive computer system 654N or combinations thereof. Nodes 610 may communicate with each other. These may be physically or virtually grouped in one or more networks such as, for example, the private, community, public, or hybrid clouds described above in this specification, or combinations thereof (not shown). Thereby, cloud computing environment 650 can provide infrastructure, platform, software, or combinations thereof as services such that a cloud consumer need not maintain resources on a local computing device. The types of computing devices 654A-N shown in FIG. 6 are for illustrative purposes only, and it is understood that computing nodes 610 and cloud computing environment 650 can communicate with any type of computerized device by any type of network or network addressable connection or both (e.g., using a web browser).

[0077] Referring now to FIG. 9, a set of functional abstraction layers provided by cloud computing environment 650 (FIG. 8) is shown. It should be pre - understood that the components, layers, and functions shown in FIG. 9 are for illustrative purposes only and that embodiments of the present invention are not limited thereto. As shown, the following layers and corresponding functions are provided.

[0078] The hardware and software layer 760 includes hardware and software components. Examples of hardware components include mainframe 761, RISC (Reduced Instruction Set Computer) architecture-based server 762, server 763, blade server 764, storage device 765, and network and networking components 766. In some embodiments, the software components include network application server software 767 and database software 768.

[0079] The virtualization layer 770 provides an abstraction layer that can provide examples of virtual entities such as virtual server 771, virtual storage 772, virtual network 773 including a virtual private network, virtual applications and operating systems 774, and virtual client 775.

[0080] In one example, the management layer 780 may provide the functions described below. Resource provisioning 781 provides for the dynamic procurement of computing resources and other resources used to perform tasks within a cloud computing environment. Metering and pricing 782 provides for cost tracking when resources are utilized within a cloud computing environment and for charging or billing for the consumption of these resources. In one example, these resources may include application software licenses. Security provides for the identification and authentication of cloud consumers and tasks, as well as the protection of data and other resources. The user portal 783 provides access to the cloud computing environment for consumers and system administrators. Service level management 784 provides for the allocation and management of cloud computing resources to reach the required service levels. Planning and fulfillment of service level agreements (SLAs) 785 provides for the advance arrangement and procurement of cloud computing resources for which future needs are anticipated in accordance with the SLA.

[0081] The workload layer 790 provides examples of functionality for which a cloud computing environment may be utilized. Examples of workloads and functions that may be provided from this layer include mapping and navigation 791, software development and lifecycle management 792, virtual classroom education delivery 793, data analysis processing 794, transaction processing 795, and neural network gas detection processing 796.

[0082] Referring now to FIG. 10, this figure is a flowchart showing the process 1000 of training an artificial intelligence (AI) model and applying the AI model to gas sensor data. At operation 1002, resistivity training data for a gas sensor device (e.g., gas sensor device 100 of FIG. 1) is obtained. This can be data for any gas (or combination of gases) at various different temperatures. Other factors that can be included in the training model are the thicknesses of the different layers of the MOX thin film of the gas sensor, and the respective material compositions of the MOX thin film. At operation 1004, a deep learning artificial intelligence (AI) system (e.g., neural network component 570 shown in FIG. 7) can be used to train one or more AI models using the resistivity training data of the gas sensor(s). When the AI model is trained at operation 1004, new resistivity data for the gas sensor is obtained at operation 1006. At operation 1008, the trained AI model is applied to the new resistivity data to determine the presence of one or more gases. As described above, these resistivity curves based on gas sensor data can have a certain pattern that can be identified to determine the presence of one or more analyte gases. In some embodiments, an AI system and a deep learning neural network can be utilized to determine the presence of a gas. In some embodiments, a system is provided that is configured to perform real-time gas detection based on the resistivity data of the gas sensor device at operation 1008 and automatically discover the identity of these gases (i.e., without requiring user input).

[0083] Embodiments of the present invention can be a system, method, or computer program product, or a combination thereof, in an integration at any possible technical detail level. The computer program product can include a computer-readable storage medium (s) having computer-readable program instructions for causing a processor to implement aspects of the present invention.

[0084] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. The computer-readable storage medium can be, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof, but is not limited thereto. A non-exhaustive list of further specific examples of computer-readable storage media includes portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM, i.e., flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, flexible disk, punch card, or a mechanically encoded device such as a raised structure in a groove in which instructions are recorded, and any suitable combination thereof. As used herein, a computer-readable storage medium shall not be construed to be a signal per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses passing through an optical fiber cable), or electrical signals transmitted through a wire, which are transient signals.

[0085] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, or a wireless network, or a combination thereof. The network can include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, or edge servers, or a combination thereof. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and transfers the computer-readable program instructions for storage on a computer-readable storage medium within each respective computing / processing device.

[0086] The computer-readable program instructions for carrying out the operation of the present invention can be source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuits, or object-oriented programming languages such as Smalltalk(R), C++, and procedural programming languages such as the "C" programming language or similar programming languages. The computer-readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, an electronic circuit, including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), can execute the computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit for performing aspects of the present invention.

[0087] Aspects of the invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0088] These computer-readable program instructions are provided to a processor of a computer or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions may be stored in a computer-readable storage medium that includes instructions for causing a computer, programmable data processing apparatus, or other device or combination thereof to function in a particular manner so that the storage medium includes an article of manufacture that includes instructions for implementing the aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0089] The computer-readable program instructions may also be loaded onto a computer, other programmable apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device so as to produce a computer-implemented process such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0090] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible embodiments of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of instructions that include one or more executable instructions for implementing the specified logical function(s). In some alternative embodiments, the functions noted in the blocks may occur out of the order noted in the drawings. For example, two blocks shown in succession may, in fact, be executed as one step according to the relevant functionality, may be executed simultaneously, substantially simultaneously, in a partially or fully temporally overlapping manner, or the blocks may be executed in the reverse order. It should also be noted that each block of the block diagram or flowchart diagram, or combinations of blocks of the block diagram or flowchart diagram or both, may be implemented by a dedicated hardware-based system that performs the specified functions.

[0091] The description of the various embodiments has been presented for purposes of illustration, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terms used herein have been chosen to best explain the principles of the embodiments, the practical application, or technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

Claim 1 A method for identifying a gas, comprising: providing a gas sensor device comprising at least two stacked metal oxide layers having different material compositions, wherein a change in the conductance of the gas sensor device in the presence of the gas varies with the temperature of the stacked metal oxide layers; bringing the gas into proximity with the stacked metal oxide layers; measuring the conductance of the gas sensor device when the gas is in proximity to the stacked layers at a plurality of temperatures to generate a temperature-conductance profile; identifying a target gas based on the temperature-conductance profile; wherein (i) over a first portion of the temperature range in which the stacked metal oxide layers operate, the conductance of the gas sensor device is characteristic of a first one of the stacked layers; and (ii) over a second portion of the temperature range, the conductance of the gas sensor device is characteristic of a second one of the stacked layers. Claim 2 The method according to claim 1, wherein one of the metal oxide layers is a p-type layer and the other of the metal oxide layers is an n-type layer. Claim 3 A method for identifying a gas, comprising: providing a gas sensor device comprising at least two stacked metal oxide layers, wherein a change in the conductance of the gas sensor device in the presence of the gas varies with the temperature of the stacked metal oxide layers; bringing the gas into proximity with the stacked metal oxide layers; measuring the conductance of the gas sensor device when the gas is in proximity to the stacked layers at a plurality of temperatures to generate a temperature-conductance profile; identifying a target gas based on the temperature-conductance profile; wherein one of the metal oxide layers is a p-type layer and the other of the metal oxide layers is an n-type layer. Claim 4 The method according to claim 2 or 3, wherein the thickness of the p-type layer is less than the thickness of the n-type layer. Claim 5 The temperature-conductance profile depends on the thickness of the layer, and the temperature-conductance profile is determined before identifying the target gas at a certain thickness of the layer. The method according to any one of claims 1 to 4.

6. The temperature-conductance profile is generated in a temperature range of 100°C to 500°C. The method according to any one of claims 1 to 5.

7. (i) Over a first part of the temperature range in which the laminated metal oxide layer is operated, the conductance of the gas sensor device is characteristic of the first layer of the laminated layers. (ii) Over a second part of the temperature range, the conductance of the gas sensor device is characteristic of the second layer of the laminated layers. The method according to claim 3.

8. Over a third part of the temperature range between the first part and the second part of the temperature range, the conductance of the gas sensor device is characteristic of both the first layer and the second layer of the laminated layers. The method according to claim 1 or 7.

9. The first layer of the laminated layers is an n-type metal oxide layer. The second layer of the laminated layers is a p-type metal oxide layer. The method according to any one of claims 1 to 7.

10. The gas sensor device includes at least three laminated metal oxide layers. The method according to claim 1 or 3.

11. One of the metal oxide layers is a p-type layer and the other of the metal oxide layers is a different p-type layer, or One of the metal oxide layers is an n-type layer and the other of the metal oxide layers is a different n-type layer. The method according to claim 1 or 3.

12. A substrate, At least two electrodes formed on the substrate, A first metal oxide layer formed on the substrate and the electrodes, A second metal oxide layer formed on the first metal oxide layer and having a material composition different from that of the first metal oxide layer A multifunctional heterojunction metal oxide gas sensor device comprising: A change in the conductance of the gas sensor device in the presence of a gas varies with the temperature of the first and second metal oxide layers. (i) Over a first portion of the temperature range in which the first and second metal oxide layers operate, the conductance of the gas sensor device is characteristic of the first metal oxide layer, (ii) Over a second portion of the temperature range, the conductance of the gas sensor device is characteristic of the second metal oxide layer, Multifunctional heterojunction metal oxide gas sensor device.

13. The multifunctional heterojunction metal oxide gas sensor device according to claim 12, wherein the first metal oxide layer is a p-type layer and the second metal oxide layer is an n-type layer.

14. A substrate, At least two electrodes formed on the substrate, A first metal oxide layer formed on the substrate and the electrodes, A second metal oxide layer formed on the first metal oxide layer A multifunctional heterojunction metal oxide gas sensor device comprising: The change in the conductance of the gas sensor device in the presence of a gas varies with the temperature of the first and second metal oxide layers, The first metal oxide layer is a p-type layer and the second metal oxide layer is an n-type layer, Multifunctional heterojunction metal oxide gas sensor device.

15. The multifunctional heterojunction metal oxide gas sensor device according to claim 13 or 14, wherein the thickness of the p-type layer is smaller than the thickness of the n-type layer.

16. The first metal oxide layer is at least one selected from the group consisting of NiO, Co ３ O ４ , PdO, and Ag ２ O, and the multifunctional heterojunction metal oxide gas sensor device according to any one of claims 12 to 15.

17. The second metal oxide layer is SnO ２ , In ２ O ３ , WO ３ , ZnO, MgO, and TiO ２ The multifunctional heterojunction metal oxide gas sensor device according to any one of claims 12 to 16, comprising at least one selected from the group consisting of.

18. The multifunctional heterojunction metal oxide gas sensor device according to any one of claims 12 to 17, wherein the combined film thickness of the first metal oxide layer and the second metal oxide layer is in the range of 10 to 200 nm.

19. The multifunctional heterojunction metal oxide gas sensor device according to any one of claims 12 to 18, further comprising a heating element disposed adjacent to the substrate.

20. The multifunctional heterojunction metal oxide gas sensor device according to any one of claims 12 to 19, wherein the gas sensor device includes at least three stacked metal oxide layers.

21. The multifunctional heterojunction metal oxide gas sensor device according to any one of claims 12 to 20, wherein the first metal oxide layer is a p-type layer and the second metal oxide layer is a different p-type layer.

22. The multifunctional heterojunction metal oxide gas sensor device according to any one of claims 12 to 21, wherein the first metal oxide layer is an n-type layer and the second metal oxide layer is a different n-type layer.

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