A method for preparing a photoelectric memristor array and application of the photoelectric memristor array

Abstract

The present invention relates to the field of intelligent sensing technology, and in particular to a method for preparing a photoelectric memristor array and an application of the photoelectric memristor array. The photoelectric memristor device array comprises a substrate layer NSTO and a top electrode layer ITO from bottom to top; the substrate layer is an NSTO oxide semiconductor, a laser direct writing technology is used to define the pattern of the memristor device on the substrate layer, and then a pulsed laser deposition technology PLD is used to deposit transparent indium tin oxide ITO thereon, and the photoelectric memristor device array achieves low latency, low energy efficiency and high working efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent sensing technology, and in particular to a method for preparing a photoelectric memristor array and applications of the photoelectric memristor array. Background Art

[0002] With the advent of the era of artificial intelligence, the development of technologies such as autonomous driving has put forward higher requirements for improving the working efficiency of sensor systems and reducing power consumption. In traditional sensor systems, the modules that perceive, store and process information are separated from each other. This separate architecture inevitably requires the sensor system to transmit a large amount of data between different modules when performing tasks. For example, the sensor module needs to first collect analog signals and convert them into digital signals, and then transmit them to the storage and processing modules in turn. The transmission of large amounts of data leads to serious energy consumption and time delays. This makes traditional sensor systems unsuitable for application scenarios in the field of autonomous driving that require real-time detection of dynamic and multi-dimensional information, which have extremely high requirements for timeliness. Summary of the invention

[0003] In view of this, the present invention provides a method for preparing a photoelectric memristor array and an application of the photoelectric memristor array, so as to achieve low latency, low energy efficiency and high working efficiency.

[0004] In a first aspect, the present invention provides a method for preparing a photoelectric memristor array, wherein the photoelectric memristor array comprises, from bottom to top, a substrate layer NSTO and a top electrode layer ITO;

[0005] The substrate layer is NSTO oxide semiconductor. The laser direct writing technology is used to define the pattern of the memristor device on the substrate layer, and then the pulsed laser deposition technology PLD is used to deposit transparent indium tin oxide ITO on it.

[0006] Optionally, 25 ITO / NSTO heterostructure optoelectronic synapse devices are prepared on the substrate layer NSTO to form a sensing array.

[0007] Optionally, a substrate layer NSTO is first provided, and then a pulsed laser deposition technology PLD is used to deposit a 100 nm to 500 nm thick ITO transparent electrode on the NSTO substrate, and the above steps are repeated to prepare a 5×5 photoelectric memristor device array.

[0008] Optionally, photoelectric memristor devices are prepared on the substrate layer at a distance of 240 μm to form an array.

[0009] Optionally, since the surface of the substrate layer NSTO contains oxygen vacancies, by applying an electric field or light pulse stimulation to the photoelectric memristor device, electrons will be captured or decaptured, thereby achieving resistance switching by regulating the Schottky barrier at the ITO / NSTO interface; after removing the electric field or light pulse, the captured or released electrons will be released or captured again by the oxygen vacancies, and the conductivity will spontaneously decay, thereby obtaining a photoelectric memristor device array that produces resistance switching and dynamically changing conductivity under the stimulation of both light and electrical signals.

[0010] In a second aspect, the present invention provides an application of a photoelectric memristor array, wherein the application is implemented based on the photoelectric memristor array prepared by the preparation method of the first aspect.

[0011] The relevant information of a moving vehicle is mapped into a photocurrent matrix using light pulses; a sequence of light pulses is proposed to represent a moving vehicle; a sensor array composed of ferroelectric photoelectric devices is constructed using the light response characteristics of a photoelectric memristor array to detect and record sequences of light pulses of different directions and wavelengths; the time when the light pulses are generated is used to represent the direction and speed of the vehicle; the vehicle is represented as a sequence of five consecutive light pulses, and when the vehicle is moving from top to bottom, the sensors in the array corresponding to the position of the vehicle receive the light pulses and generate photocurrents, while the sensors at other positions cannot receive the light pulses and will not generate photocurrents; the spatial orientation information of the vehicle is converted into time-related information and mapped into photocurrent values; the attenuation degree of the photocurrent value is used to further distinguish the driving speed of the vehicle so that the speed information is included in the photocurrent value; since the photoelectric memristor device will generate different photocurrents when stimulated by light pulses of different wavelengths, light pulses of different wavelengths are used to encode vehicle information of different colors, so that a set of photocurrent values ​​simultaneously contains three types of information: the driving direction, speed and color of the vehicle.

[0012] Optionally, a photocurrent value matrix containing the vehicle's driving direction, speed and color is input into a feedforward neural network simulated based on the Python language, and a back-propagation algorithm is implemented to complete automatic detection and identification of moving vehicles.

[0013] In the technical solution provided by the present invention, the photoelectric memristor device array includes a substrate layer NSTO and a top electrode layer ITO from bottom to top; the substrate layer is an NSTO oxide semiconductor, and the pattern of the memristor device is defined on the substrate layer by laser direct writing technology, and then transparent indium tin oxide ITO is deposited thereon by pulsed laser deposition technology PLD. The photoelectric memristor device array achieves low latency, low energy efficiency and high working efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A schematic diagram of a photoelectric memristor device array provided by an embodiment of the present invention;

[0016] Figure 2 An array schematic diagram of a photoelectric memristor device array provided in an embodiment of the present invention;

[0017] Figure 3 A schematic diagram of the response of the photoelectric memristor device array provided in an embodiment of the present invention to light pulses of different wavelengths;

[0018] Figure 4 The brain-like visual sensing system for identifying vehicle information provided in an embodiment of the present invention, a is a schematic diagram of the intelligent perception and recognition system for moving object information; b is a schematic diagram for encoding relevant information of the vehicle; c to e are schematic diagrams of the readout function of the output neurons of the neural network; f is the recognition accuracy of the vehicle information. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be clear that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention are also intended to include plural forms, unless the context clearly indicates other meanings.

[0022] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0023] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0024] Compared with traditional sensor systems, the human sensory system can store information in situ and perform real-time preprocessing such as feature extraction on the information. It can eliminate redundant data before transmitting the extracted data to the brain for further analysis, which has the characteristics of integrated sensing, storage and computing. The ability of multi-sensory signal fusion processing and integrated sensing, storage and computing enables the human brain to respond quickly in a changing environment with extremely high efficiency and extremely low power consumption.

[0025] Inspired by the human perception system, a new brain-inspired sensing system was designed to mimic the ability of the human perception system to integrate sensing, storage and computing. In the designed system, the perception and preprocessing of optical signals are integrated into a single device, which realizes in-sensor computing and ultimately achieves real-time perception and recognition of multi-dimensional information of moving vehicles.

[0026] In view of the fact that current sensor hardware is difficult to efficiently realize automatic perception and recognition of dynamic information, the present invention provides a system with sensing, storage and computing integrated functions composed of optoelectronic memristor device array hardware based on indium tin oxide / niobium-doped strontium titanate (ITO / NSTO) heterostructure and neural network. The system has a simple structure and is suitable for large-scale preparation. Each hardware on the array can realize the change of the interface Schottky barrier through multiple modulations of electrical and optical stimulation, and realize multi-level storage, data preprocessing and multi-modal perception capabilities. The system can realize parallel perception and recognition of multi-dimensional information of moving vehicles (driving direction, speed and vehicle color).

[0027] The present invention provides a method for preparing a photoelectric memristor array, such as Figure 1 and Figure 2 As shown, the photoelectric memristor device array includes a substrate layer NSTO and a top electrode layer ITO from bottom to top; the substrate layer is an NSTO oxide semiconductor, and the pattern of the memristor device is defined on the substrate layer using laser direct writing technology, and then transparent indium tin oxide ITO is deposited thereon using pulsed laser deposition technology PLD.

[0028] In the embodiment of the present invention, 25 ITO / NSTO heterostructure optoelectronic synapse devices are prepared on the substrate layer NSTO to form a sensor array.

[0029] In the embodiment of the present invention, a substrate layer NSTO is first provided, and then a pulsed laser deposition technology PLD is used to deposit a 100nm-500nm thick ITO transparent electrode on the NSTO substrate, and the above steps are repeated to prepare a 5×5 photoelectric memristor device array.

[0030] In the embodiment of the present invention, photoelectric memristor devices are prepared on the substrate layer at a distance of 240 μm to form an array.

[0031] In the embodiment of the present invention, since the surface of the substrate layer NSTO contains oxygen vacancies, electrons will be captured or decaptured by applying an electric field or light pulse stimulation to the photoelectric memristor device, thereby achieving resistance change by regulating the Schottky barrier at the ITO / NSTO interface; after removing the electric field or light pulse, the captured or released electrons will be released or captured again by the oxygen vacancies, and the conductivity will spontaneously decay, thereby obtaining a photoelectric memristor device array that produces resistance change and dynamically changing conductivity under the stimulation of both light and electrical signals.

[0032] The present invention provides an application of a photoelectric memristor array, which is implemented based on the photoelectric memristor array prepared by the preparation method; the relevant information of a moving vehicle is mapped into a photocurrent matrix using a light pulse; a light pulse sequence is proposed to represent a moving vehicle; a sensor array composed of ferroelectric photoelectric devices is constructed using the light response characteristics of the photoelectric memristor array to detect and record light pulse sequences of different directions and wavelengths; since the photocurrent decays over time, and the device will generate different photocurrents when excited by light pulses of different wavelengths, the time and wavelength of the light pulse will affect the size of the photocurrent. The time when the light pulse is generated is used to represent the direction and speed of the vehicle; the vehicle is represented as a sequence of five continuous light pulses, and when the vehicle is moving from top to bottom, the sensor in the array corresponding to the position of the vehicle receives the light pulse and generates a photocurrent, while the sensors at other positions cannot receive the light pulse and will not generate a photocurrent; since the photocurrent decays over time, the earlier the photocurrent is generated, the more it decays. In this way, the spatial orientation information of the vehicle is converted into time-related information and mapped into photocurrent values. The attenuation degree of the photocurrent value is used to further distinguish the vehicle's driving speed so that the speed information can be included in the photocurrent value. Since the photoelectric memristor device will generate different photocurrents when stimulated by light pulses of different wavelengths, light pulses of different wavelengths are used to encode vehicle information of different colors, so that a set of photocurrent values ​​simultaneously contains three kinds of information: the vehicle's driving direction, speed and color.

[0033] In the embodiment of the present invention, a photocurrent value matrix containing the vehicle's driving direction, speed and color is input into a feedforward neural network simulated based on Python language, and a back propagation algorithm is implemented to complete automatic detection and identification of moving vehicles.

[0034] In the embodiment of the present invention, Figure 3 As shown in the figure, the photoelectric memristor device array can be modulated to different conductance states by single optical pulses of 405nm, 660nm and 880nm with a width of 10s. The conductance state under optical pulse modulation also coexists with volatility and non-volatility. Specifically, the device conductivity relaxes after the optical pulse is removed, but cannot relax back to the initial state, indicating that the device resistance change under optical modulation also coexists with volatility and non-volatility. Among them, volatile conductivity can be used to sense and process optical signals in the environment, and non-volatile conductivity can be used to store signals.

[0035] In the embodiment of the present invention, a neuromorphic vision system with ITO / NSTO optoelectronic memristor array hardware as the core is constructed to realize the real-time monitoring function of vehicles traveling at intersections. The system can be used to simultaneously perceive and identify multi-dimensional information of vehicles traveling, such as Figure 4 As shown, the driving direction, speed and vehicle color are included. The array hardware consists of 5×5 ITO / NSTO devices, and all 25 devices show good consistency. Figure 4 As shown in Figure b, taking a vehicle traveling downward as an example, it illustrates how the array senses and processes the vehicle's driving direction information. Initially, all devices in the array are set to the same conductance state for 40.00μS using a negative voltage pulse of -2.2V. The position of the vehicle is represented as a sequence of light pulses. When the vehicle travels from top to bottom, the device corresponding to the position of the vehicle in the array can receive the light pulse and be activated to generate current, while the devices at other positions cannot receive the light pulse and will not generate a current response. Since current decays over time, the earlier the current is generated, the more it decays. In this way, the spatial direction information of the vehicle is mapped and stored as a current value. As shown in Figure 2b, the device corresponding to the position of the vehicle in the array can receive the light pulse and be activated to generate current, while the devices at other positions cannot receive the light pulse and will not generate a current response. Since current decays over time, the earlier the current is generated, the more it decays. In this way, the spatial direction information of the vehicle is mapped and stored as a current value. Figure 4 As shown in Fig. 2b, other driving directions, including upward, left, and right, can also be mapped and stored as current values ​​in this way. In addition, the attenuation degree of the EPSC value can also be used to estimate the driving speed of the vehicle, such as Figure 4 In addition, since the memristor device will generate different current values ​​under light pulses of different wavelengths, different wavelengths are used to encode color information, such as Figure 4 As shown in b, for example, a 660nm red light is used to represent a red vehicle, and a 405nm blue light is used to represent a blue vehicle. In this way, a set of current values ​​can encode and store three types of information in parallel: the vehicle's direction, speed, and vehicle color, demonstrating the system's powerful parallel perception and processing capabilities for multi-dimensional information about moving vehicles.

[0036] Figure 4 Figure cf shows the readout function of the neuromorphic vision system, where 25 feature vectors from a 5×5 ITO / NSTO optoelectronic synapse array are input into a simulated neural network to automatically identify the information of a moving vehicle. Figure 4 As shown in the right panel of a, the neural network consists of 25 input neurons and 8 output neurons. These 8 output neurons are divided into three categories to represent different types of information: O1~O4 are used to represent direction, where O1 represents downward, O2 represents upward, O3 represents left, and O4 represents right; O5 and O6 are used for speed, where O5 represents normal speed and O6 represents speeding; O7 and O8 are used to represent color, where O7 represents red and O8 represents blue. 200 sets of feature vectors were collected in advance and trained using a neural network based on the back-propagation algorithm. During the training process, the readout function value of the target neuron continued to increase with the increase in training cycles, and eventually exceeded other neurons. The trained neuromorphic vision system can recognize three types of information of moving vehicles in parallel. For example, to identify a blue, speeding, and downward-moving vehicle, 25 sets of newly collected feature vectors are input into this neuromorphic vision system. As shown Figure 4 As shown in Figure ce, with the increase of iteration cycles, the readout function values ​​of different output neurons gradually separate, and the identified target output neuron shows the largest f value. Then, accurate recognition can only be achieved when all features are correctly classified. Figure 4 As shown in Figure f, 100% recognition accuracy can be achieved within 10 cycles, demonstrating that the neuromorphic vision system can efficiently and accurately perceive and recognize dynamic information. This application scenario proves that the system has great application prospects in time-critical applications that significantly improve efficiency and reduce latency due to its integrated perception, storage and computing functions, as well as dynamic storage characteristics.

[0037] The present invention adopts a photoelectric memristor array, and utilizes that each device in the array can be modulated by light pulses to change its conductivity, and the device conductivity responds differently to light pulses of different wavelengths. The modulated conductivity state has a semi-volatile (volatile and non-volatile coexisting) characteristic, and completes the perception, storage and recognition of multi-dimensional dynamic information (driving direction, speed and vehicle color) of moving vehicles (using the device conductivity's ability to respond to light pulses to achieve the perception function, using volatile conductivity to achieve the information processing function, and using non-volatile conductivity to achieve memory power supply). The present invention provides a hardware foundation for realizing automatic detection of dynamic multi-dimensional information.

[0038] In the technical solution provided by the present invention, the photoelectric memristor device array includes a substrate layer NSTO and a top electrode layer ITO from bottom to top; the substrate layer is an NSTO oxide semiconductor, and the pattern of the memristor device is defined on the substrate layer by laser direct writing technology, and then transparent indium tin oxide ITO is deposited thereon by pulsed laser deposition technology PLD. The photoelectric memristor device array achieves low latency, low energy efficiency and high working efficiency.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for preparing a photoelectric memristor array, characterized in that: The photoelectric memristor device array comprises a substrate layer NSTO and a top electrode layer ITO from bottom to top; The substrate layer is NSTO oxide semiconductor. The laser direct writing technology is used to define the pattern of the memristor device on the substrate layer, and then the pulsed laser deposition technology PLD is used to deposit transparent indium tin oxide ITO on it.

2. The method according to claim 1, characterized in that 25 ITO / NSTO heterostructure optoelectronic synapse devices were prepared on the substrate layer NSTO to form a sensing array.

3. The method according to claim 1, characterized in that First, a substrate layer NSTO is provided, and then a pulsed laser deposition technology PLD is used to deposit a 100nm-500nm thick ITO transparent electrode on the NSTO substrate, and the above steps are repeated to prepare a 5×5 photoelectric memristor device array.

4. The method according to claim 3, characterized in that Photoelectric memristor devices are prepared on the substrate layer at a distance of 240 μm to form an array.

5. The method according to claim 3, characterized in that: Since the surface of the substrate layer NSTO contains oxygen vacancies, electrons will be captured or decaptured by applying an electric field or light pulse stimulation to the photoelectric memristor device, thereby achieving resistance change by regulating the Schottky barrier at the ITO / NSTO interface; after removing the electric field or light pulse, the captured or released electrons will be released or captured again by the oxygen vacancies, and the conductivity will spontaneously decay, thereby obtaining a photoelectric memristor device array that produces resistance change and dynamically changing conductivity under the stimulation of both light and electrical signals.

6. An application of a photoelectric memristor array, the application being realized based on the photoelectric memristor array prepared by the preparation method of claim 1, characterized in that: The relevant information of the moving vehicle is mapped into a photocurrent matrix using light pulses; a sequence of light pulses is proposed to represent the moving vehicle; a sensor array composed of ferroelectric optoelectronic devices is constructed using the light response characteristics of the photoelectric memristor array to detect and record sequences of light pulses of different directions and wavelengths; the time when the light pulses are generated is used to represent the direction and speed of the vehicle; The vehicle is represented as a sequence of five continuous light pulses. When the vehicle travels from top to bottom, the sensors in the array corresponding to the vehicle position receive the light pulses and generate photocurrents, while the sensors at other positions cannot receive the light pulses and thus do not generate photocurrents. The spatial orientation information of the vehicle is converted into time-related information and mapped into photocurrent values. The attenuation degree of the photocurrent value is used to further distinguish the vehicle's driving speed so that the speed information is included in the photocurrent value. Since the photoelectric memristor device will generate different photocurrents when stimulated by light pulses of different wavelengths, light pulses of different wavelengths are used to encode vehicle information of different colors, so that a set of photocurrent values ​​simultaneously contains three types of information: the vehicle's driving direction, speed and color.

7. The use according to claim 6, characterized in that: The photocurrent value matrix containing the vehicle's driving direction, speed and color is input into the feedforward neural network simulated based on Python language, and the back propagation algorithm is implemented to complete the automatic detection and identification of moving vehicles.

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