CN114912601A - Optical artificial neural network environment-friendly monitoring chip and preparation method thereof - Google Patents
Optical artificial neural network environment-friendly monitoring chip and preparation method thereof Download PDFInfo
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Abstract
The invention provides an optical artificial neural network environment-friendly monitoring chip and a preparation method thereof, the chip provided by the invention simulates an artificial neural network in a hardware mode and is used for online identification or analysis of environmental pollutants, an optical filter layer is used as an input layer of the artificial neural network, an image sensor is used as a linear layer of the artificial neural network, and the filtering effect of the optical filter layer on incident light entering the optical filter layer is used as the connection weight from the input layer to the linear layer, so that the subsequent complex signal processing and algorithm processing corresponding to the input layer and the linear layer are not required to be carried out when the environment-friendly monitoring chip is used for carrying out environment-friendly monitoring intelligent processing, and the power consumption and the time delay during artificial neural network processing can be greatly reduced.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an optical artificial neural network environment-friendly monitoring chip and a preparation method thereof.
Background
The environmental monitoring is to monitor and measure the index reflecting the environmental quality to determine the environmental pollution condition and the environmental quality. The environmental monitoring comprises the real-time monitoring of air, water quality, soil and the like, provides scientific, accurate and effective monitoring for environmental management, and makes reasonable solution measures according to the monitoring.
Traditional environmental pollution monitoring adopts the environmental monitoring instrument, carries out the environmental protection monitoring based on wet chemistry technique and the experimental analysis after the sample of breathing in, but these instruments are usually only limited to the single-point measurement, and detection range is less and the sample is single, and then influences the rate of accuracy of environmental protection monitoring result.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides an optical artificial neural network environment-friendly monitoring chip and a preparation method thereof.
Specifically, the embodiment of the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides an optical artificial neural network environmental monitoring chip, including: an optical filter layer, an image sensor, and a processor; the optical filter layer corresponds to an input layer of the artificial neural network and the connection weight from the input layer to the linear layer, and the image sensor corresponds to the linear layer of the artificial neural network; the processor corresponds to a nonlinear layer and an output layer of the artificial neural network;
the optical filter layer is arranged on the surface of a photosensitive area of the image sensor and comprises an optical modulation structure, and the optical filter layer is used for respectively carrying out different spectrum modulation on incident light entering different position points of the optical modulation structure through the optical modulation structure so as to obtain incident light carrying information corresponding to different position points on the surface of the photosensitive area; the incident light comprises reflected, transmitted and/or radiated light of the environmental contaminant;
the image sensor is used for converting incident light carrying information corresponding to different position points after being modulated by the optical filter layer into electric signals corresponding to the different position points and sending the electric signals corresponding to the different position points to the processor; the electric signal is an image signal modulated by the optical filter layer;
the processor is used for carrying out full connection processing and nonlinear activation processing on the electric signals corresponding to different position points to obtain an environment-friendly monitoring intelligent processing result;
wherein the environmental monitoring intelligent processing task comprises identification and/or qualitative analysis of environmental pollutants; the environment-friendly monitoring intelligent processing result comprises an environment-friendly monitoring intelligent processing result of the environmental pollutants and/or an environmental pollution qualitative analysis result.
Further, the incident light carrying information includes at least one of light intensity distribution information, spectrum information, angle information of the incident light, and phase information of the incident light.
Furthermore, the optical artificial neural network environment-friendly monitoring chip comprises a trained optical modulation structure, an image sensor and a processor;
the trained optical modulation structure, the image sensor and the processor are the optical modulation structure, the image sensor and the processor which meet the training convergence condition and are obtained by training an optical artificial neural network environment-friendly monitoring chip which comprises different optical modulation structures, image sensors and processors with different full-connection parameters and different nonlinear activation parameters by using an input training sample and an output training sample which correspond to the environment-friendly monitoring intelligent processing task;
the input training sample comprises incident light reflected, transmitted and/or radiated by a sample having a different environmental contaminant; the output training sample includes a level of an environmental contaminant.
Further, the samples with different environmental contaminants include air samples with different contaminants, water quality samples with different contaminants, and/or soil samples with different contaminants.
Further, when the optical artificial neural network environment-friendly monitoring chip comprising different optical modulation structures, image sensors and processors with different full-connection parameters and nonlinear activation parameters is trained, the different optical modulation structures are designed and realized in a computer optical simulation design mode.
Further, the light modulating structures in the optical filter layer comprise regular structures and/or irregular structures; and/or the light modulating structures in the optical filter layer comprise discrete structures and/or continuous structures.
Further, the light modulation structure in the optical filter layer comprises a unit array consisting of a plurality of micro-nano units, and each micro-nano unit corresponds to one or more pixel points on the image sensor; the structures of the micro-nano units are the same or different.
Further, the micro-nano unit comprises a regular structure and/or an irregular structure; and/or the micro-nano unit comprises a discrete structure and/or a continuous structure.
Further, the micro-nano unit comprises a plurality of groups of micro-nano structure arrays, and the structures of the micro-nano structure arrays are the same or different.
Furthermore, each group of micro-nano structure array has the function of broadband filtering or narrow-band filtering.
Furthermore, one or more groups of hollow structures are arranged in a plurality of groups of micro-nano structure arrays contained in the micro-nano unit.
Further, the micro-nano unit has polarization-independent characteristics.
Further, the micro-nano unit has quadruple rotational symmetry.
Further, the optical filter layer is composed of one or more filter layers;
the filter layer is prepared from one or more of semiconductor materials, metal materials, liquid crystals, quantum dot materials and perovskite materials; and/or the filter layer is prepared from one or more of a photonic crystal, a super surface, a random structure, a nano structure, a metal Surface Plasmon Polariton (SPP) micro-nano structure and an adjustable Fabry-Perot resonant cavity.
Further, the semiconductor material comprises one or more of silicon, silicon oxide, silicon nitride, titanium oxide, a composite material mixed according to a preset proportion and a direct band gap compound semiconductor material; and/or the nanostructure comprises one or more of a nanodot two-dimensional material, a nanocolumn two-dimensional material and a nanowire two-dimensional material.
Further, the optical filter layer has a thickness of 0.1 λ to 10 λ, where λ represents a center wavelength of incident light.
In a second aspect, an embodiment of the present invention provides an environmental monitoring apparatus, including: the light artificial neural network environment-friendly monitoring chip is described above.
In a third aspect, an embodiment of the present invention provides a method for preparing an optical artificial neural network environmental monitoring chip, including:
preparing an optical filter layer containing an optical modulation structure on the surface of a photosensitive area of the image sensor;
generating a processor with functions of carrying out full connection processing and nonlinear activation processing on signals;
connecting the image sensor and the processor;
the optical filter layer is used for respectively carrying out different spectrum modulation on incident light entering different position points of the optical modulation structure through the optical modulation structure so as to obtain incident light carrying information corresponding to the different position points on the surface of the photosensitive area; the incident light carrying information comprises at least one of light intensity distribution information, spectrum information, angle information of the incident light and phase information of the incident light;
the image sensor is used for converting incident light carrying information corresponding to different position points after being modulated by the optical filter layer into electric signals corresponding to the different position points and sending the electric signals corresponding to the different position points to the processor; the processor is used for carrying out full connection processing and nonlinear activation processing on the electric signals corresponding to different position points to obtain an environment-friendly monitoring intelligent processing result; the electrical signal is an image signal modulated by the optical filter layer, and the incident light includes reflected light, transmitted light and/or radiated light of the environmental pollutants.
Further, preparing an optical filter layer containing a light modulation structure on the surface of the photosensitive area of the image sensor includes:
growing one or more layers of preset materials on the surface of a photosensitive area of the image sensor;
etching the light modulation structure pattern of the one or more layers of preset materials to obtain an optical filter layer containing a light modulation structure;
or the one or more layers of preset materials are subjected to imprinting transfer to obtain an optical filter layer containing an optical modulation structure;
or the one or more layers of preset materials are subjected to additional dynamic modulation to obtain an optical filter layer containing an optical modulation structure;
or printing the one or more layers of preset materials in a partition mode to obtain an optical filter layer containing an optical modulation structure;
or carrying out partition growth on the one or more layers of preset materials to obtain an optical filter layer containing an optical modulation structure;
or quantum dot transfer is carried out on the one or more layers of preset materials to obtain the optical filter layer containing the optical modulation structure.
Further, the method also comprises the following steps: the training process of the optical artificial neural network environment-friendly monitoring chip specifically comprises the following steps:
and training an optical artificial neural network environment-friendly monitoring chip comprising different optical modulation structures, image sensors and processors with different full-connection parameters and different nonlinear activation parameters by using input training samples and output training samples corresponding to the environment-friendly monitoring intelligent processing task to obtain the optical modulation structures, the image sensors and the processors meeting the training convergence conditions, and taking the optical modulation structures, the image sensors and the processors meeting the training convergence conditions as the trained optical modulation structures, the image sensors and the processors.
The invention provides an optical artificial neural network environment-friendly monitoring chip and a preparation method thereof, which simulate an artificial neural network in a hardware mode and are used for on-line identification or analysis of environmental pollutants, namely, the embodiment of the invention realizes a brand-new environment-friendly monitoring chip capable of realizing the function of the artificial neural network, in the environment-friendly monitoring chip, an optical filter layer is arranged on the surface of a photosensitive area of an image sensor, the optical filter layer comprises an optical modulation structure, the optical filter layer is used for respectively carrying out different spectrum modulations on incident light entering different position points of the optical modulation structure through the optical modulation structure so as to obtain incident light carrying information corresponding to different position points on the surface of the photosensitive area, correspondingly, the image sensor is used for converting the incident light carrying information corresponding to the different position points into electric signals corresponding to the different position points, meanwhile, a processor connected with the image sensor is used for carrying out full connection processing and nonlinear activation processing on electric signals corresponding to different position points to obtain output signals of the artificial neural network, so that in the environment-friendly monitoring chip, the optical filter layer is used as an input layer of the artificial neural network, the image sensor is used as a linear layer of the artificial neural network, meanwhile, the filtering effect of the optical filter layer on incident light entering the optical filter layer corresponds to the connection weight from the input layer to the linear layer, namely, the optical filter layer and the image sensor in the environment-friendly monitoring chip realize the related functions of the input layer and the linear layer in the artificial neural network, namely, the embodiment of the invention strips the input layer and the linear layer in the artificial neural network realized by software in the prior art, and realizes the two-layer structure of the input layer and the linear layer in the artificial neural network by using a hardware mode, therefore, the follow-up complex signal processing and algorithm processing corresponding to the input layer and the linear layer is not needed when the environmental protection monitoring chip is used for carrying out the intelligent processing of the environmental protection monitoring of the artificial neural network, and the processor in the environmental protection monitoring chip is only needed to carry out the related processing of the full connection and the nonlinear activation of the electric signals, so that the power consumption and the time delay during the environmental protection monitoring of the artificial neural network can be greatly reduced. Therefore, the embodiment of the invention takes the optical filter layer as the input layer of the artificial neural network, takes the image sensor as the linear layer of the artificial neural network, takes the filtering effect of the optical filter layer on the incident light entering the optical filter layer as the connection weight of the input layer to the linear layer, projects the incident light carrying information of the environmental pollutants into an electric signal by using the optical filter layer and the image sensor, then, the full connection processing and the nonlinear activation processing of the electric signals are realized in the processor, so that the embodiment of the invention not only can save the complex signal processing and algorithm processing corresponding to the input layer and the linear layer in the prior art, the embodiment of the invention can detect the environmental pollutants in a large range, is not limited to single-point measurement of the traditional environmental monitoring instrument, has richer detection samples and improves the accuracy and diversity of environmental monitoring.
Therefore, the embodiment of the invention provides a novel photoelectric chip for realizing large-range environment-friendly monitoring, and the chip partially embeds the artificial neural network into the image sensor comprising various optical filter layers, so that safe, reliable, rapid and accurate environment-friendly monitoring is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an optical artificial neural network environmental monitoring chip according to a first embodiment of the present invention;
fig. 2 is a schematic diagram illustrating an identification principle of an optical artificial neural network environmental monitoring chip according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating disassembly of an optical artificial neural network environmental monitoring chip according to an embodiment of the present invention;
FIG. 4 is a schematic view of an environmental pollution monitoring process according to an embodiment of the present invention;
FIG. 5 is a top view of an optical filter layer according to an embodiment of the present invention;
FIG. 6 is a top view of another optical filter layer provided by an embodiment of the present invention;
FIG. 7 is a top view of yet another optical filter layer provided in accordance with an embodiment of the present invention;
FIG. 8 is a top view of yet another optical filter layer according to an embodiment of the present invention;
FIG. 9 is a top view of yet another optical filter layer provided in accordance with an embodiment of the present invention;
FIG. 10 is a top view of yet another optical filter layer provided in accordance with an embodiment of the present invention;
fig. 11 is a schematic diagram of a micro-nano structure broadband filtering effect according to an embodiment of the present invention;
fig. 12 is a schematic diagram of a narrow-band filtering effect of a micro-nano structure according to an embodiment of the present invention;
fig. 13 is a schematic flow chart of a method for manufacturing an environmental monitoring chip for an optical artificial neural network according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Traditional environmental pollution monitoring adopts the environmental monitoring instrument, carries out the environmental protection monitoring based on wet chemistry technique and the experimental analysis after the sample of breathing in, but these instruments are usually only limited to the single-point measurement, and detection range is less and the sample is single, and then influences the rate of accuracy of environmental protection monitoring result. Based on this, the embodiment of the present invention provides an optical artificial neural network environment-friendly monitoring chip, where an optical filter layer in the environment-friendly monitoring chip corresponds to an input layer of an artificial neural network, an image sensor corresponds to a linear layer of the artificial neural network, and a filtering effect of the optical filter layer on incident light entering the optical filter layer corresponds to a connection weight from the input layer to the linear layer. According to the embodiment of the invention, the input layer and the linear layer in the artificial neural network realized by software in the prior art are stripped, and the two-layer structure of the input layer and the linear layer in the artificial neural network is realized by using a hardware mode, so that the complicated signal processing and algorithm processing corresponding to the input layer and the linear layer are not required to be carried out when the intelligent chip is used for carrying out the artificial neural network intelligent processing in the follow-up process, and only the processor in the intelligent chip is required to carry out the related processing of full connection and nonlinear activation of the electric signal, so that the power consumption and the time delay in the artificial neural network processing can be greatly reduced. The present invention will be explained and illustrated in detail by specific examples.
As shown in fig. 1, an optical artificial neural network environmental monitoring chip provided in a first embodiment of the present invention is used for an environmental monitoring intelligent processing task, and includes: an optical filter layer 1, an image sensor 2 and a processor 3; the optical filter layer 1 corresponds to an input layer of the artificial neural network and the connection weight from the input layer to a linear layer, and the image sensor 2 corresponds to the linear layer of the artificial neural network; the processor 3 corresponds to a nonlinear layer and an output layer of the artificial neural network;
the optical filter layer 1 is arranged on the surface of a photosensitive area of the image sensor, the optical filter layer 1 comprises an optical modulation structure, and the optical filter layer 1 is used for respectively performing spectrum modulation with intensity modulation along with wavelength change on incident light entering different position points of the optical modulation structure through the optical modulation structure, namely performing different intensity modulation on the incident light with different wavelengths so as to obtain incident light carrying information corresponding to different position points on the surface of the photosensitive area; the incident light carrying information includes image information and/or various light space information of a target object to be processed by the optical artificial neural network environmental monitoring chip, for example, the incident light carrying information includes at least one of light intensity distribution information, spectrum information, angle information of the incident light, and phase information of the incident light; the incident light comprises reflected, transmitted and/or radiated light of the environmental contaminant;
the image sensor 2 is used for converting incident light carrying information corresponding to different position points after being modulated by the optical filter layer 1 into electric signals corresponding to the different position points and sending the electric signals corresponding to the different position points to the processor 3; the electric signal is an image signal modulated by the optical filter layer;
the processor 3 is used for carrying out full connection processing and nonlinear activation processing on the electric signals corresponding to different position points to obtain an environment-friendly monitoring intelligent processing result;
wherein the environmental monitoring intelligent processing task comprises identification and/or qualitative analysis of environmental pollutants; the environment-friendly monitoring intelligent processing result comprises an environment-friendly monitoring intelligent processing result of the environmental pollutants and/or an environmental pollution qualitative analysis result.
In this embodiment, the optical filter layer 1 is disposed on a surface of a photosensitive region of the image sensor, the optical filter layer 1 includes an optical modulation structure, the optical filter layer 1 is configured to perform different spectrum modulations on incident light entering different positions of the optical modulation structure through the optical modulation structure, so as to obtain modulated incident light carrying information corresponding to the different positions on the surface of the photosensitive region, correspondingly, the image sensor 2 is configured to convert the incident light carrying information corresponding to the different positions into electrical signals corresponding to the different positions, that is, image signals modulated by the optical filter layer, and meanwhile, the processor 3 connected to the image sensor 2 is configured to perform full connection processing and nonlinear activation processing on the electrical signals corresponding to the different positions, so as to obtain an output signal of the artificial neural network.
In this embodiment, the optical filter layer 1 includes an optical modulation structure, and performs spectrum modulation with different intensities on incident light (for example, reflected light, transmitted light, and radiated light of environmental pollutants to be identified) entering different positions of the optical modulation structure through the optical modulation structure, so as to obtain incident light carrying information corresponding to different positions on the surface of the photosensitive region of the image sensor 2.
In this embodiment, it is understood that the modulation intensity is related to the specific structural form of the light modulation structure, for example, different modulation intensities can be realized by designing different light modulation structures (e.g., changing the shape and/or size parameters of the light modulation structure).
In this embodiment, it can be understood that the light modulation structures at different positions on the optical filter layer 1 have different spectrum modulation effects on the incident light, and the modulation intensities of the light modulation structures on different wavelength components of the incident light correspond to the connection intensities of the artificial neural network, that is, the input layer and the connection weights of the input layer to the linear layer. It should be noted that the optical filter layer 1 is composed of a plurality of optical filter units, and the optical modulation structures at different positions in each optical filter unit are different, so that the optical filter layer has different spectrum modulation effects on incident light; the light modulating structures at different locations between the optical filter cells may be the same or different and thus have the same or different spectral modulation effect on the incident light.
In this embodiment, the image sensor 2 converts the incident light carrying information corresponding to the different position points into electrical signals corresponding to the different position points, and sends the electrical signals corresponding to the different position points to the processor 3, and the image sensor 2 corresponds to the linear layer of the neural network.
In this embodiment, the processor 3 performs full-connection processing and nonlinear activation processing on the electrical signals at different position points, so as to obtain an output signal of the artificial neural network.
It will be appreciated that the processor 3 corresponds to the non-linear layer and the output layer of the neural network, and may also be understood to correspond to the remaining layers (all other layers) of the neural network except the input layer and the linear layer.
In addition, it should be added that the processor 3 may be disposed in the environmental monitoring chip, that is, the processor 3 may be disposed in the environmental monitoring chip together with the filter layer 1 and the image sensor 2, or may be disposed outside the environmental monitoring chip separately and connected to the image sensor 2 in the environmental monitoring chip through a data line or a connection device, which is not limited in this embodiment.
In addition, it should be noted that the processor 3 may be implemented by a computer, may also be implemented by an ARM or FPGA circuit board having a certain operation capability, and may also be implemented by a microprocessor, which is not limited in this embodiment. In addition, as mentioned above, the processor 3 may be integrated into the environmental monitoring chip or may be disposed separately from the environmental monitoring chip. When the processor 3 is arranged outside the environmental protection monitoring chip, the electrical signal in the image sensor 2 can be read out to the processor 3 through the signal reading circuit, and then the processor 3 performs full connection processing and nonlinear activation processing on the read electrical signal.
In this embodiment, it can be understood that, when performing the nonlinear activation processing, the processor 3 may be implemented by using a nonlinear activation function, for example, a Sigmoid function, a Tanh function, a ReLU function, and the like, which is not limited in this embodiment.
In this embodiment, the optical filter layer 1 corresponds to an input layer of the artificial neural network and a connection weight from the input layer to a linear layer, the image sensor 2 corresponds to the linear layer of the artificial neural network, and converts information carried by incident light at different spatial position points into an electrical signal, the processor 3 corresponds to a nonlinear layer and an output layer of the artificial neural network, and fully connects the electrical signals at different positions, and obtains an output signal of the artificial neural network through a nonlinear activation function, thereby realizing identification processing of environmental pollutants.
As shown in the left side of fig. 2, the optical artificial neural network environmental monitoring chip includes an optical filter layer 1, an image sensor 2 and a processor 3, and in fig. 2, the processor 3 is implemented by using a signal readout circuit and a computer. As shown in the right side of fig. 2, the optical filter layer 1 in the optical artificial neural network environmental monitoring chip corresponds to the input layer of the artificial neural network, the image sensor 2 corresponds to the linear layer of the artificial neural network, the processor 3 corresponds to the nonlinear layer and the output layer of the artificial neural network, and the filtering effect of the optical filter layer 1 on incident light entering the optical filter layer 1 corresponds to the connection weight from the input layer to the linear layer, so that it can be seen that the optical filter layer and the image sensor in the environmental monitoring chip provided by this embodiment implement the related functions of the input layer and the linear layer in the artificial neural network in a hardware manner, so that the complex signal processing and algorithm processing corresponding to the input layer and the linear layer are not required to be performed again when the environmental monitoring chip is used for identification processing, thereby greatly reducing the power consumption and delay when the artificial neural network is processed, the embodiment of the invention can detect the environmental pollutants in a large range, is not limited to single-point measurement of the traditional environmental monitoring instrument, has richer detection samples and improves the accuracy and diversity of environmental monitoring.
As shown in the right side of FIG. 2, the spectrum P of the incident light at different positions of the optical filter layer 1 λ Photocurrent response I of projection/connection to image sensor N The processor 3 comprises a signal reading circuit and a computer, the signal reading circuit in the processor 3 reads the photocurrent response and transmits the photocurrent response to the computer, the computer performs full connection processing and nonlinear activation processing on the electrical signal, and finally outputs the result.
As shown in fig. 3, the optical modulation structure on the optical filter layer 1 is integrated above the image sensor 2, modulates the incident light, projects/connects the spectrum information of the incident light to different pixel points of the image sensor 2 to obtain an electrical signal containing the spectrum information and the image information of the incident light, i.e., the incident light passes through the optical filter layer 1, and is converted into an electrical signal by the image sensor 2 to form an image containing the spectrum information of the incident light, and finally, the electrical signal containing the spectrum information and the image information of the incident light is processed by the processor 3 connected to the image sensor 2.
In this embodiment, the incident light carrying information may include one or more (including two) of light intensity distribution information, spectrum information, angle information of the incident light, and phase information of the incident light.
For example, in one implementation, the incident light carrying information may include light intensity distribution information, and in other implementations, the environmental pollutants may be identified by simultaneously using multiple information of image information, spectrum information, angle of the incident light, and phase information of the incident light of the environmental pollutants, so that the identification of the environmental pollutants may be implemented more accurately.
Therefore, the environmental monitoring chip of the optical artificial neural network provided by the embodiment can simultaneously utilize the image information, the spectrum information, the angle of the incident light and the phase information of the incident light of the environmental pollutants, namely, the incident light at different points of the space carries information, and an artificial neural network is embedded in hardware, so that the information of the components, the image shape, the three-dimensional depth and the like of the environmental pollutants can be further extracted from the spatial image, the spectrum, the angle and the phase information, the environmental pollutants can be accurately identified and/or qualitatively analyzed, furthermore, the environment-friendly monitoring result can be accurately obtained, the embodiment of the invention can detect the environmental pollutants in a large range, is not limited to single-point measurement of the traditional environment monitoring instrument, has richer detection samples and higher accuracy, and the spectral optical artificial neural network environment-friendly monitoring chip with low power consumption, low time delay and high accuracy is realized.
The optical artificial neural network environment-friendly monitoring chip provided by the embodiment of the invention realizes a brand-new intelligent chip capable of realizing the function of an artificial neural network, in the intelligent chip, an optical filter layer is used as an input layer of the artificial neural network, an image sensor is used as a linear layer of the artificial neural network, meanwhile, the filtering action of incident light entering the optical filter layer by the optical filter layer corresponds to the connection weight from the input layer to the linear layer, namely, the optical filter layer and the image sensor in the intelligent chip realize the related functions of the input layer and the linear layer in the artificial neural network, namely, the embodiment of the invention peels off the input layer and the linear layer in the artificial neural network realized by software in the prior art, and realizes the two-layer structure of the input layer and the linear layer in the artificial neural network by using a hardware mode, so that the input layer and the linear layer do not need to carry out the intelligent processing of the artificial neural network subsequently by using the intelligent chip The complex signal processing and algorithm processing only needs to be carried out by the processor in the intelligent chip and the relevant processing of the full connection and the nonlinear activation of the electric signals, so that the power consumption and the time delay during the processing of the artificial neural network can be greatly reduced.
In addition, it should be noted that, in the prior art, when environmental pollutants are analyzed, the method is limited to single-point measurement, the detection range is small, the sample is single, and the accuracy of the environmental monitoring result is difficult to ensure. Therefore, based on this, in one implementation manner, the incident light carrying information may include light intensity distribution information and spectrum information, so that when the optical artificial neural network environmental monitoring chip provided by the present application is used to perform an intelligent recognition task, the light intensity distribution information and the spectrum information of the environmental pollutants may be simultaneously utilized, which is visible, since the incident light carrying information covers the image, component, shape, three-dimensional depth, structure and other information of the environmental pollutants, when the incident light carrying information at different points in the environmental pollutants space is used for recognition, the image, component, shape, three-dimensional depth, structure and other multidimensional information of the environmental pollutants may be covered, thereby more accurately realizing environmental monitoring. In addition, in another implementation manner, the incident light carrying information may further include light intensity distribution information, spectral information, and angle information of the incident light, so that information such as an image, a component, a shape, a three-dimensional depth, a three-dimensional structure, and the like of the environmental pollutant can be captured more comprehensively, and thus, environmental monitoring can be achieved more accurately. In addition, in another implementation manner, the incident light carrying information may further include light intensity distribution information, spectral information, incident light angle information, and incident light phase information, so that information such as an image, a component, a shape, a three-dimensional depth, a three-dimensional structure, and the like of the environmental pollutant can be captured more comprehensively, and thus, environmental monitoring can be achieved more accurately.
The light artificial neural network environment-friendly monitoring chip provided by the embodiment of the invention comprises a light filter layer, an image sensor and a processor, wherein the light filter layer is arranged on the surface of a photosensitive area of the image sensor and comprises a light modulation structure, the light filter layer is used for respectively carrying out different spectrum modulation on incident light entering different positions of the light modulation structure through the light modulation structure so as to obtain incident light carrying information corresponding to different positions on the surface of the photosensitive area, correspondingly, the image sensor is used for converting the incident light carrying information corresponding to different positions into electric signals corresponding to different positions, meanwhile, the processor connected with the image sensor is used for carrying out full connection processing and nonlinear activation processing on the electric signals corresponding to different positions so as to obtain output signals of the artificial neural network, therefore, in the environment-friendly monitoring chip, the optical filter layer is used as an input layer of the artificial neural network, the image sensor is used as a linear layer of the artificial neural network, and meanwhile, the filtering effect of incident light entering the optical filter layer by the optical filter layer corresponds to the connection weight from the input layer to the linear layer, namely, the optical filter layer and the image sensor in the environment-friendly monitoring chip realize the related functions of the input layer and the linear layer in the artificial neural network, namely, the embodiment of the invention peels off the input layer and the linear layer in the artificial neural network realized by software in the prior art, and realizes the two-layer structure of the input layer and the linear layer in the artificial neural network by using a hardware mode, so that the subsequent complex signal processing and algorithm processing corresponding to the input layer and the linear layer are not needed when the environment-friendly monitoring intelligent processing of the artificial neural network is carried out by using the environment-friendly monitoring chip, the processor in the environmental protection monitoring chip is only required to carry out relevant processing of full connection and nonlinear activation of the electric signals, so that the power consumption and the time delay of the artificial neural network during environmental protection monitoring can be greatly reduced. Therefore, the embodiment of the invention takes the optical filter layer as the input layer of the artificial neural network, the image sensor as the linear layer of the artificial neural network, the filtering effect of the optical filter layer on the incident light entering the optical filter layer as the connection weight from the input layer to the linear layer, the incident light carrying information of the environmental pollutants is projected into an electric signal by using the optical filter layer and the image sensor, and then the full connection processing and nonlinear activation processing of the electric signal are realized in the processor, so that the embodiment of the invention not only can save the complex signal processing and algorithm processing corresponding to the input layer and the linear layer in the prior art, but also actually simultaneously utilizes the image information, the spectrum information, the angle of the incident light and the phase information of the incident light of the environmental pollutants, namely the incident light carrying information at different points of the space of the environmental pollutants, therefore, because the incident light carrying information at different points of the environment pollutant space covers the image, the composition, the shape, the three-dimensional depth, the structure and other information of the environment pollutant, when the identification processing is carried out according to the incident light carrying information at different points of the environment pollutant space, the multi-dimensional information of the image, the composition, the shape, the three-dimensional depth, the structure and the like of the environment pollutant can be covered, the environment pollutant can be accurately identified and/or qualitatively analyzed, and the environment-friendly monitoring result can be accurately obtained. Therefore, the optical artificial neural network environment-friendly monitoring chip provided by the embodiment of the invention not only can realize the effects of low power consumption and low time delay, but also can realize the effect of high accuracy.
Based on the content of the above embodiment, in this embodiment, the optical artificial neural network environmental monitoring chip includes a trained optical modulation structure, an image sensor, and a processor;
the trained optical modulation structure, the image sensor and the processor are the optical modulation structure, the image sensor and the processor which meet the training convergence condition and are obtained by training an optical artificial neural network environment-friendly monitoring chip which comprises different optical modulation structures, image sensors and processors with different full-connection parameters and different nonlinear activation parameters by using an input training sample and an output training sample which correspond to the environment-friendly monitoring intelligent processing task;
the input training sample comprises incident light reflected or transmitted or radiated by a sample having a different environmental contaminant; the output training sample includes a level of an environmental contaminant.
In this embodiment, the optical artificial neural network environmental monitoring chip can be used for an environmental monitoring intelligent processing task of environmental pollutants. In this embodiment, the reflected light, the transmitted light and/or the radiated light of the environmental pollutants enter the trained optical artificial neural network environmental monitoring chip to obtain an environmental monitoring intelligent processing result of the environmental pollutants. The environment-friendly monitoring intelligent processing result comprises an environment-friendly monitoring intelligent processing result of the environmental pollutants and/or an environmental pollution qualitative analysis result.
It can be understood that, because the filtering action of the optical filter layer on the incident light entering the optical filter layer corresponds to the connection weight from the artificial neural network input layer to the linear layer, therefore, when training, changing the optical modulation structure in the optical filter layer is equivalent to changing the connection weight from the artificial neural network input layer to the linear layer, through training convergence conditions, determining the optical modulation structure suitable for the current environment-friendly monitoring intelligent processing task, and being suitable for the full connection parameter and the nonlinear activation parameter of the current environment-friendly monitoring intelligent processing task, thereby completing the training of the optical artificial neural network environment-friendly monitoring chip.
In this embodiment, the environmental protection monitoring intelligent processing task of the environmental pollutant is taken as an example for explanation, it can be understood that, when the environmental protection monitoring intelligent processing task is performed by using the environmental protection monitoring chip, firstly, the optical artificial neural network environmental protection monitoring chip needs to be trained, where training the optical artificial neural network environmental protection monitoring chip refers to collecting a large amount of environmental pollutant samples (including air, water, soil and other samples) as a training set in the early stage, and determining the optical modulation structure applicable to the current environmental protection monitoring intelligent processing task, and the full connection parameter and the nonlinear activation parameter applicable to the current environmental protection monitoring intelligent processing task.
It can be understood that, because the filtering action of the optical filter layer on the incident light entering the optical filter layer corresponds to the connection weight from the artificial neural network input layer to the linear layer, therefore, when training, changing the optical modulation structure in the optical filter layer is equivalent to changing the connection weight from the artificial neural network input layer to the linear layer, through training convergence conditions, the optical modulation structure suitable for the current environment-friendly monitoring intelligent processing task is determined, and the full-connection parameter and the nonlinear activation parameter suitable for the current environment-friendly monitoring intelligent processing task are determined, thereby completing the training of the environment-friendly monitoring chip.
It can be understood that, the input training sample includes incident light reflected, transmitted and/or radiated by samples with different environmental pollutants, in order to accurately perform environmental monitoring, the embodiment selects samples with typical representatives in the ecological environment state, such as air samples with different environmental pollutants, water quality samples with different environmental pollutants and soil samples with different environmental pollutants, to perform training, so as to reflect the current state of the ecological environment, and then accurately train the environmental monitoring chip, so that the environmental monitoring intelligent processing result finally obtained by applying the environmental monitoring chip can accurately reflect the content of the environmental pollutants in the object to be measured (such as air, water or soil). It can be appreciated that after the environmental monitoring chip is trained, the environmental monitoring smart process task can be performed using the environmental monitoring chip. Specifically, after incident light carrying image information and spatial spectrum information of the environmental pollutants enters the optical filter layer 1 of the trained environmental monitoring chip, the optical modulation structure in the optical filter layer 1 modulates the incident light, the intensity of the modulated optical signal is detected by the image sensor 2 and converted into an electric signal, and then the processor 3 performs full connection processing and nonlinear activation processing, so that the detection result of the content of the environmental pollutants can be obtained.
As shown in fig. 4, the complete process for environmental pollution monitoring includes: the light source 200 under the detection instrument irradiates on the environmental pollutant sample 300, then the reflected light or transmitted light of the environmental pollutant is collected by the optical artificial neural network environment-friendly monitoring chip 100, or the light of the environmental pollutant directly radiating outwards is collected by the optical artificial neural network environment-friendly monitoring chip 100, and after the light is processed by the optical filter layer, the image sensor and the processor in the environment-friendly monitoring chip, the environment-friendly monitoring intelligent processing result can be obtained.
The trained optical artificial neural network environment-friendly monitoring chip comprises a trained optical modulation structure, an image sensor and a processor; the trained optical modulation structure, the image sensor and the processor are the optical modulation structure, the image sensor and the processor which meet the training convergence condition and are obtained by training an optical artificial neural network environment-friendly monitoring chip of the processor which comprises different optical modulation structures, image sensors and different full-connection parameters and nonlinear activation parameters by using an input training sample and an output training sample corresponding to the recognition processing task.
For example, for the environmental monitoring intelligent processing task, the input training sample corresponding to the environmental monitoring intelligent processing task is an environmental pollutant sample, and the output training sample corresponding to the environmental monitoring intelligent processing task is an environmental monitoring intelligent processing result of the environmental pollutant sample. It can be understood that, for the environmental monitoring intelligent processing task, since the environmental monitoring chip provided by this embodiment is also advantageous in that the image information, the spectrum information, the angle information of the incident light, and the phase information of the incident light at different points in the environmental pollutant space can be acquired, in order to fully utilize this advantage, the real environmental pollutants are preferentially adopted for the environmental pollutant sample as the input training sample, rather than the two-dimensional image of the environmental pollutants. Of course, this does not represent that the two-dimensional image may not be used as an environmental contaminant sample.
In this embodiment, the optical filter layer 1 serves as an input layer of the neural network, the image sensor 2 serves as a linear layer of the neural network, and in order to minimize a loss function of the neural network, the modulation intensities of the optical modulation structures in the optical filter layer on different wavelength components in incident light of an environmental pollutant are used as connection weights of the input layer of the neural network to the linear layer, and the modulation intensities of the different wavelength components in the incident light of the environmental pollutant can be adjusted by adjusting the structure of the filter, so that the adjustment of the connection weights of the input layer to the linear layer is realized, and further, training of the neural network is optimized.
Therefore, in this embodiment, the optical modulation structure is obtained based on neural network training, optical simulation is performed on the environmental pollutant sample through a computer, sample modulation intensities of different wavelength components of incident light of the environmental pollutant in the environmental pollutant sample to the environmental monitoring intelligent processing task by the optical modulation structure are obtained, the sample modulation intensities are used as connection weights from an input layer of the neural network to a linear layer to perform nonlinear activation, the training sample corresponding to the environmental monitoring intelligent processing task is used for neural network training, and the corresponding optical modulation structure of the environmental pollutant sample is used as an optical filter layer corresponding to the environmental monitoring intelligent processing task until the neural network converges.
Therefore, in the embodiment, by implementing the input layer (optical filter layer) and the linear layer (image sensor) of the neural network on the physical layer, not only can complicated signal processing and algorithm processing corresponding to the input layer and the linear layer in the prior art be omitted, but also the embodiment of the present invention actually utilizes the image information, the spectrum information, the angle of the incident light and the phase information of the incident light of the environmental pollutant at the same time, that is, the incident light carrying information at different points of the environmental pollutant space covers the image, the component, the shape, the three-dimensional depth, the structure and other information of the environmental pollutant, so that when the identification processing is performed according to the incident light carrying information at different points of the environmental pollutant space, the image, the component, the shape, the three-dimensional depth, the image sensor and the like of the environmental pollutant can be covered, The structure and the like of the optical artificial neural network environment-friendly monitoring chip provided by the embodiment of the invention can realize the effects of low power consumption and low time delay and also can realize the effect of high accuracy.
Based on the content of the foregoing embodiment, in this embodiment, when training an optical artificial neural network environmental monitoring chip that includes different optical modulation structures, an image sensor, and a processor with different full connection parameters and nonlinear activation parameters, the different optical modulation structures are designed and implemented by adopting a computer optical simulation design.
In the embodiment, the optical modulation structure is designed through computer optical simulation, and the optical modulation structure is adjusted through the optical simulation until the optical modulation structure is determined to be the final optical modulation structure size to be manufactured when the neural network converges, so that the prototype manufacturing time and cost are saved, the product efficiency is improved, and the complex optical problem is easily solved. Because the environmental pollutants contain different component substances, the reflected light, the transmitted light and/or the radiated light of the different component substances are different, namely, the incident light entering the light modulation structure is different, and further, the light modulation intensities corresponding to the different component substances are different. In addition, this embodiment can detect environmental pollution on a large scale, is not restricted to the single-point measurement of the environmental monitoring instrument among the traditional approach, and the detection sample that obtains is abundanter, and environmental protection monitoring result accuracy is higher.
For example, the light modulation structure can be simulated and designed through FDTD software, and the light modulation structure is changed in optical simulation, so that the modulation intensity of the light modulation structure on different incident lights can be accurately predicted, the modulation intensity can be used as the connection weight of a neural network input layer and a linear layer, an optical artificial neural network environment-friendly monitoring chip is trained, and the light modulation structure can be accurately obtained.
Therefore, the light modulation structure is designed in a computer optical simulation design mode, so that the time and the cost for manufacturing the prototype of the light modulation structure are saved, and the product efficiency is improved.
Based on the content of the above embodiments, in the present embodiment, the light modulation structure in the optical filter layer includes a regular structure and/or an irregular structure.
In this embodiment, the light modulation structure in the optical filter layer may only include a regular structure, may also only include an irregular structure, and may also include both a regular structure and an irregular structure.
In the present embodiment, it is understood that, here, the optical modulation structure including a regular structure may include: the minimum modulation units contained in the light modulation structure are regular structures, the arrangement mode of the minimum modulation units contained in the light modulation structure is regular, the minimum modulation units contained in the light modulation structure are regular structures, and the arrangement mode of the minimum modulation units is also regular.
The minimum modulation unit contained in the light modulation structure can be in a regular pattern such as a rectangle, a square and a circle. The arrangement mode of the minimum modulation units included in the light modulation structure may be that the minimum modulation units are arranged in a regular array form, in a regular circular form, in a regular trapezoidal form, in a regular polygonal form, and the like.
In this embodiment, the light modulation structure including the irregular structure may refer to: the minimum modulation units included in the light modulation structure are irregular structures, for example, the minimum modulation units may be irregular patterns such as irregular polygons and random shapes. Further, where the light modulating structure comprises an irregular structure, it may also mean: the arrangement of the minimum modulation units included in the light modulation structure is irregular, for example, the arrangement may be in an irregular polygon form, a random arrangement form, or the like. Further, where the light modulating structure comprises an irregular structure, it may also mean: the minimum modulation units included in the light modulation structure are irregular structures, and the arrangement mode of the minimum modulation units is also irregular.
Based on the content of the above embodiments, in the present embodiment, the optical modulation structure in the optical filter layer includes a discrete type structure and/or a continuous type structure.
In this embodiment, the optical modulation structure in the optical filter layer may include a discrete structure, a continuous structure, or both a discrete structure and a continuous structure.
In this embodiment, where the light modulation structure includes a continuous type structure, it may mean: the light modulation structure is formed by continuous modulation patterns; where the light modulating structure comprises a discrete structure may refer to: the light modulating structure is formed by a discrete modulation pattern.
It is understood that the continuous modulation pattern may refer to a rectilinear pattern, a wavy pattern, a polygonal pattern, and the like.
It is to be understood that a discrete modulation pattern herein may refer to a modulation pattern formed by discrete patterns (e.g., discrete dots, discrete triangles, discrete stars, etc.).
In this embodiment, it should be noted that the optical modulation structure has different modulation effects on light with different wavelengths, and specific modulation methods include, but are not limited to, scattering, absorption, interference, surface plasmon, resonance enhancement, and the like. By designing different filter structures, corresponding transmission spectrums are different after light passes through different groups of filter structures.
Based on the content of the above embodiments, in the present embodiment, the optical filter layer is a single-layer structure or a multi-layer structure.
In this embodiment, the optical filter layer may have a single-layer filter structure, or may have a multi-layer filter structure, for example, a multi-layer structure including two, three, or four layers.
In the present embodiment, as shown in fig. 1, the optical filter layer 1 is a single-layer structure, the thickness of the optical filter layer 1 is related to the target wavelength range, and the thickness of the grating structure may be 50nm to 5 μm for wavelengths of 400nm to 10 μm.
It is understood that since the optical filter layer 1 serves to spectrally modulate incident light, it is preferable to fabricate materials with high refractive index and low loss, such as silicon, germanium, silicon-germanium materials, silicon compounds, germanium compounds, III-V materials, and the like, wherein silicon compounds include, but are not limited to, silicon nitride, silicon dioxide, silicon carbide, and the like.
In addition, it should be noted that, in order to form more or more complex connection weights between the input layer and the linear layer, preferably, the optical filter layer 1 may be set to be a multilayer structure, and the optical modulation structures corresponding to the respective layers may be set to be different structures, so as to increase the spectral modulation capability of the optical filter layer on incident light, so as to form more or more complex connection weights between the input layer and the linear layer, and further improve the accuracy of the environmental monitoring chip in processing the environmental monitoring intelligent processing task.
In addition, for the filter layer including a multi-layer structure, the material of each layer structure may be the same or different, for example, for the optical filter layer 1 having two layers, the first layer may be a silicon layer, and the second layer may be a silicon nitride layer.
The thickness of the optical filter layer 1 is related to the target wavelength range, and the total thickness of the multilayer structure may be 50nm to 5 μm for wavelengths of 400nm to 10 μm.
Based on the content of the above embodiment, in this embodiment, the light modulation structure in the optical filter layer includes a unit array composed of a plurality of micro-nano units, and each micro-nano unit corresponds to one or more pixel points on the image sensor; the structures of the micro-nano units are the same or different.
In this embodiment, in order to obtain connection weights (used for connecting connection weights between the input layer and the linear layer) distributed in an array so as to facilitate the processor to perform subsequent full-connection and nonlinear activation processing, preferably, in this embodiment, the light modulation structure is in an array structure form, specifically, the light modulation structure includes a unit array composed of a plurality of micro-nano units, and each micro-nano unit corresponds to one or more pixel points on the image sensor. It should be noted that the structures of the micro-nano units may be the same or different. In addition, the structure of each micro-nano unit may be periodic or non-periodic. In addition, it should be noted that each micro-nano unit may further include multiple groups of micro-nano structure arrays, and the structures of the micro-nano structure arrays of each group are the same or different. It should be noted that the structure of each micro-nano unit is designed according to a corresponding environment-friendly monitoring intelligent processing task, for example, the environment-friendly monitoring intelligent processing task is to detect the content of an environmental pollutant, and the structure of each micro-nano unit is obtained by training based on the incident light reflected or transmitted or radiated by an environmental pollutant sample as input and the corresponding content of the environmental pollutant as output. Therefore, according to the embodiment, different micro-nano modulation structures can be correspondingly designed according to different intelligent processing tasks of environmental monitoring, and then the environmental monitoring can be accurately and quickly carried out.
As illustrated in fig. 5 to 9, in this embodiment, as shown in fig. 5, the optical filter layer 1 includes a plurality of repeated continuous or discrete micro-nano units, such as 11, 22, 33, 44, 55, and 66, each micro-nano unit has the same structure (and each micro-nano unit has a non-periodic structure), and each micro-nano unit corresponds to one or more pixel points on the image sensor 2; as shown in fig. 6, the optical filter layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, 66, each micro-nano unit has the same structure (different from fig. 5 in that each micro-nano unit in fig. 6 is a periodic structure), and each micro-nano unit corresponds to one or more pixel points on the image sensor 2; as shown in fig. 7, the optical filter layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, 66, each micro-nano unit has the same structure (and each micro-nano unit is a periodic structure), each micro-nano unit corresponds to one or more pixel points on the image sensor 2, and the difference from fig. 6 is that the unit shape of the periodic array in each micro-nano unit in fig. 7 has quadruple rotational symmetry; as shown in fig. 8, the optical filter layer 1 includes a plurality of micro-nano units, such as 11, 22, 33, 44, 55, and 66, and the difference from fig. 6 lies in that each micro-nano unit has a different structure, and each micro-nano unit corresponds to one or more pixel points on the image sensor 2, in this embodiment, the optical filter layer 1 includes a plurality of micro-nano units that are different from each other, that is, modulation effects of different regions on incident light on the environmental monitoring chip are different, so that the degree of freedom of design is improved, and further, the accuracy of identification can be improved. As shown in fig. 9, the optical filter layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, and 66, each of which has the same structure, and is different from fig. 5 in that each of the micro-nano units is composed of a discrete non-periodic array structure, and each of the micro-nano units corresponds to one or more pixels on the image sensor 2.
In this embodiment, the micro-nano unit has different modulation effects on light with different wavelengths, and specific modulation methods include, but are not limited to, scattering, absorption, interference, surface plasmons, resonance enhancement, and the like. By designing different filter structures, corresponding transmission spectrums are different after light passes through different groups of filter structures.
Based on the content of the above embodiment, in this embodiment, the micro-nano unit includes a regular structure and/or an irregular structure; and/or the micro-nano unit comprises a discrete structure and/or a continuous structure.
In this embodiment, the micro-nano unit may only include a regular structure, may also only include an irregular structure, and may also include both a regular structure and an irregular structure.
In this embodiment, the micro-nano unit including a regular structure may refer to: the minimum modulation units contained in the micro-nano units are of a regular structure, the arrangement mode of the minimum modulation units contained in the micro-nano units is regular, the minimum modulation units contained in the micro-nano units are of a regular structure, and the arrangement mode of the minimum modulation units is also regular. The minimum modulation units can be regular graphs such as rectangles, squares and circles, and the arrangement mode of the minimum modulation units contained in the micro-nano units can be a regular array form, a circular form, a trapezoidal form, a polygonal form and the like.
In this embodiment, the micro-nano unit including an irregular structure may refer to: the minimum modulation units contained in the micro-nano units are of irregular structures, the arrangement modes of the minimum modulation units contained in the micro-nano units are irregular, the minimum modulation units contained in the micro-nano units are of irregular structures, and meanwhile, the arrangement modes of the minimum modulation units are also irregular.
The minimum modulation units contained in the micro-nano units can be irregular patterns such as irregular polygons and random shapes, and the arrangement mode of the minimum modulation units contained in the micro-nano units can be an irregular polygon form, a random arrangement form and the like.
In this embodiment, the micro-nano unit in the optical filter layer may include a discrete structure, may also include a continuous structure, and may also include both a discrete structure and a continuous structure.
In this embodiment, the micro-nano unit including a continuous structure may refer to: the micro-nano unit is formed by continuous modulation patterns; here, the micro-nano unit including a discrete structure may mean: the micro-nano unit is formed by discrete modulation patterns.
It is understood that the continuous modulation pattern may refer to a rectilinear pattern, a wavy pattern, a polygonal pattern, and the like.
It is to be understood that a discrete modulation pattern herein may refer to a modulation pattern formed by discrete patterns (e.g., discrete dots, discrete triangles, discrete stars, etc.).
In this embodiment, it should be noted that different micro-nano units have different modulation effects on light with different wavelengths, and specific modulation methods include, but are not limited to, scattering, absorption, interference, surface plasmons, resonance enhancement, and the like. By designing different micro-nano units, corresponding transmission spectrums are different after light passes through different groups of micro-nano units.
Based on the content of the above embodiment, in this embodiment, the micro-nano unit includes multiple sets of micro-nano structure arrays, and the structures of the micro-nano structure arrays of the sets are the same or different.
In this embodiment, as shown in fig. 5, the optical filter layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, and 66, each micro-nano unit includes a plurality of micro-nano structure arrays, for example, the micro-nano unit 11 includes 4 different micro-nano structure arrays 110, 111, 112, and 113, and the filter unit 44 includes 4 different micro-nano structure arrays 440, 441, 442, and 443. As shown in fig. 10, the optical filter layer 1 includes a plurality of micro-nano units, such as 11, 22, 33, 44, 55, and 66, each micro-nano unit includes a plurality of micro-nano structure arrays, for example, the micro-nano unit 11 includes 4 identical micro-nano structure arrays 110, 111, 112, and 113.
It should be noted that, here, the micro-nano units including four groups of micro-nano structure arrays are only used for illustration, and do not play a limiting role, and in practical application, the micro-nano units including six groups, eight groups, or other numbers of micro-nano structure arrays may also be set as required.
In this embodiment, each micro-nano structure array in the micro-nano unit has different modulation effects on light with different wavelengths, and the modulation effects on input light between each group of filtering structures are also different, and specific modulation modes include, but are not limited to, scattering, absorption, interference, surface plasmons, resonance enhancement, and the like. By designing different micro-nano structure arrays, corresponding transmission spectrums are different after light passes through different groups of micro-nano structure arrays.
Based on the content of the above embodiment, in this embodiment, each group of micro-nano structure arrays has a function of broadband filtering or narrowband filtering.
In this embodiment, in order to obtain the modulation intensities of the different wavelength components of the incident light of the environmental pollutant as the connection weights of the neural network input layer and the linear layer, the broadband filtering and the narrowband filtering are realized by adopting different micro-nano structure arrays, so that the micro-nano structure arrays in this embodiment perform the broadband filtering or the narrowband filtering on the incident light of the environmental pollutant to obtain the modulation intensities of the different wavelength components of the incident light of the environmental pollutant. As shown in fig. 11 and 12, each group of micro-nano structure array in the optical filter layer has a function of broadband filtering or narrowband filtering.
It can be understood that each group of micro-nano structure arrays can have a broadband filtering function, can also have a narrowband filtering function, can also partially have a broadband filtering function, and partially have a narrowband filtering function. In addition, the wide band filtering range and the narrow band filtering range of each group of micro-nano structure array can be the same or different. For example, by designing the dimensional parameters such as the period, duty ratio, radius, side length and the like of each group of micro-nano structures in the micro-nano unit, the micro-nano unit has a narrow-band filtering effect, that is, only light with one (or a few) wavelength can pass through the micro-nano unit. For another example, by designing the dimensional parameters such as the period, duty ratio, radius, side length and the like of each group of micro-nano structures in the micro-nano unit, the micro-nano unit has a broadband filtering effect, that is, light with more wavelengths or all wavelengths can be allowed to pass through.
It can be understood that, in specific use, the filtering state of each group of micro-nano structure array can be determined in a mode of performing broadband filtering, narrowband filtering or a combination thereof according to an application scene.
Based on the content of the foregoing embodiment, in this embodiment, each group of micro-nano structure arrays is a periodic structure array or a non-periodic structure array.
In this embodiment, each micro-nano structure array may be a periodic structure array, or may be a non-periodic structure array, or may be a partial periodic structure array and a partial non-periodic structure array. The periodic structure array is easy to carry out optical simulation design, and the non-periodic structure array can realize more complex modulation effect.
In this embodiment, as shown in fig. 5, the optical filter layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, and 66, each micro-nano unit is composed of a plurality of micro-nano structure arrays, the structures of the micro-nano structure arrays are different from each other, and the micro-nano structure arrays are aperiodic structures. The aperiodic structure refers to that the shapes of modulation holes on the micro-nano structure array are arranged according to a non-periodic arrangement mode. As shown in fig. 5, the micro-nano unit 11 includes 4 different aperiodic structure arrays 110, 111, 112, and 113, the micro-nano unit 44 includes 4 different aperiodic structure arrays 440, 441, 442, and 443, and the micro-nano structure array with aperiodic structure is designed by training neural network data aiming at the recognition processing task in the previous stage, and is usually an irregularly shaped structure. As shown in fig. 6, the optical filter layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, 66, each of which is composed of a plurality of sets of micro-nano structure arrays, the structures of the micro-nano structure arrays are different from each other, and unlike fig. 5, the micro-nano structure array is a periodic structure. The periodic structure refers to that the shapes of modulation holes on the micro-nano structure array are arranged according to a periodic arrangement mode, and the size of the period is usually 20 nm-50 mu m. As shown in fig. 6, the micro-nano unit 11 includes 4 different periodic structure arrays 110, 111, 112, and 113, the micro-nano unit 44 includes 4 different periodic structure arrays 440, 441, 442, and 443, and the filter structure of the periodic structure is designed by training neural network data for the recognition processing task at the previous stage, and is usually an irregularly shaped structure. As shown in fig. 7, the optical filter layer 1 includes a plurality of micro-nano units, such as 11, 22, 33, 44, 55, and 66, which are different from each other, each micro-nano unit is composed of a plurality of micro-nano structure arrays, the micro-nano structure arrays are different from each other, and the micro-nano structure arrays are periodic structures. The periodic structure refers to the shape of the filter structure arranged in a periodic arrangement, and the period is usually 20nm to 50 μm. As shown in fig. 7, the micro-nano structure arrays of the micro-nano unit 11 and the micro-nano unit 12 are different from each other, the micro-nano unit 11 includes 4 different periodic structure arrays 110, 111, 112, and 113, the micro-nano unit 44 includes 4 different periodic structure arrays 440, 441, 442, and 443, and the micro-nano structure array of the periodic structure is designed by neural network data training aiming at the recognition processing task in the early stage, and is usually an irregular structure.
It should be noted that each micro-nano unit in fig. 5 to 9 includes four micro-nano structure arrays, the four micro-nano structure arrays are formed by four modulation holes with different shapes, and the four micro-nano structure arrays are used for modulating incident light differently. It should be noted that, here, the micro-nano units including four groups of micro-nano structure arrays are only used for illustration, and do not play a limiting role, and in practical application, the micro-nano units including six groups, eight groups, or other numbers of micro-nano structure arrays may also be set as required. In the present embodiment, the four different shapes may be (without limitation) a circle, a cross, a regular polygon, and a rectangle.
In this embodiment, each group of micro-nano structure arrays in the micro-nano unit has different modulation effects on light with different wavelengths, and the modulation effects on input light between each group of micro-nano structure arrays are also different, and specific modulation modes include, but are not limited to, scattering, absorption, interference, surface plasmons, resonance enhancement, and the like. By designing different micro-nano structure arrays, corresponding transmission spectrums are different after light passes through different groups of micro-nano structure arrays.
Based on the content of the foregoing embodiment, in this embodiment, the micro-nano unit includes one or more groups of hollow structures in a plurality of groups of micro-nano structure arrays.
For example, referring to an example shown in fig. 9, in this embodiment, as shown in fig. 9, the optical filter layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, and 66, each micro-nano unit is composed of a plurality of micro-nano structure arrays, structures corresponding to the plurality of micro-nano structure arrays are different from each other, the micro-nano structure arrays are periodic structures, and different from the above embodiment, for any micro-nano unit, one or more groups of empty structures are included, and the empty structures are used for directly passing through incident light. It can be understood that when the multiple groups of micro-nano structure arrays include one or more groups of hollow structures, a richer spectrum modulation effect can be formed, so that a spectrum modulation requirement under a specific scene (or a specific connection weight requirement between an input layer and a linear layer under a specific scene) is met.
As shown in fig. 9, each micro-nano unit includes a group of micro-nano structure arrays and three groups of empty structures, the micro-nano unit 11 includes 1 aperiodic structure array 111, the micro-nano unit 22 includes 1 aperiodic structure array 221, the micro-nano unit 33 includes 1 aperiodic structure array 331, the micro-nano unit 44 includes 1 aperiodic structure array 441, the micro-nano unit 55 includes 1 aperiodic structure array 551, and the micro-nano unit 66 includes 1 aperiodic structure array 661, where the micro-nano structure arrays are used for performing different modulations on incident light. It should be noted that, here, the example is only given by including a group of micro-nano structure arrays and three groups of hollow structures, and does not play a limiting role, and in practical application, micro-nano units including a group of micro-nano structure arrays and five groups of hollow structures or other numbers of groups of micro-nano structure arrays may also be set according to needs. In this embodiment, the micro-nano structure array may be made of modulation holes that are circular, cross-shaped, regular polygon, and rectangular (but not limited thereto).
It should be noted that, the micro-nano units may not include any empty structure in the multi-group micro-nano structure array, that is, the multi-group micro-nano structure array may be a non-periodic structure array or a periodic structure array.
Based on the content of the foregoing embodiment, in this embodiment, the micro-nano unit has a polarization-independent property.
In the embodiment, the micro-nano unit has a polarization-independent characteristic, so that the optical filter layer is insensitive to the polarization of incident light, and the optical artificial neural network environment-friendly monitoring chip insensitive to an incident angle and polarization is realized. The optical artificial neural network environment-friendly monitoring chip provided by the embodiment of the invention is insensitive to the incident angle and the polarization characteristic of incident light, namely, the measurement result is not influenced by the incident angle and the polarization characteristic of the incident light, so that the stability of the spectral measurement performance can be ensured, and the stability of intelligent processing of environment-friendly monitoring can be further ensured. The micro-nano cells may also have polarization dependent properties.
Based on the content of the foregoing embodiment, in this embodiment, the micro-nano unit has quadruple rotational symmetry.
In this embodiment, it should be noted that the quadruple rotational symmetry belongs to a specific case of the polarization-independent characteristic, and the requirement of the polarization-independent characteristic can be satisfied by designing the micro-nano unit to have a structure with quadruple rotational symmetry.
As illustrated below with reference to the example shown in fig. 7, in this embodiment, as shown in fig. 7, the optical filter layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, and 66, each micro-nano unit is composed of a plurality of micro-nano structure arrays, the structures corresponding to the plurality of micro-nano structure arrays are different from each other, and the micro-nano structure arrays are periodic structures, which is different from the above embodiments, the structure corresponding to each micro-nano structure array may be a structure having quadruple rotational symmetry, such as a circle, a cross, a regular polygon, and a rectangle, that is, after the structure is rotated by 90 °, 180 °, and 270 °, the structure coincides with the original structure, so that the structure has a polarization-independent characteristic, and the same environmental pollutant detection effect can be obtained when different polarized light is incident.
Based on the content of the above embodiments, in the present embodiment, the optical filter layer is composed of one or more filter layers;
the filter layer is prepared from one or more of semiconductor materials, metal materials, liquid crystals, quantum dot materials and perovskite materials; and/or the filter layer is prepared from one or more of a photonic crystal, a super Surface, a random structure, a nano structure, a metal Surface Plasmon Polaritons (SPP) micro-nano structure and a tunable Fabry-Perot Cavity (FP Cavity).
The semiconductor material comprises one or more of silicon, silicon oxide, silicon nitride, titanium oxide, composite materials mixed according to a preset proportion and direct band gap compound semiconductor materials; and/or the nanostructure comprises one or more of a nanodot two-dimensional material, a nanorod two-dimensional material, and a nanowire two-dimensional material.
The photonic crystal, the super surface and the random structure combination can adopt CMOS (complementary metal oxide semiconductor) process compatibility, and have better modulation effect, and micro-nano modulation structure micropores can also be filled with other materials for surface smoothing; the quantum dots and the perovskite can utilize the spectral modulation characteristics of the material to minimize the volume of a single modulation structure; the SPP is small in size, and polarization-related light modulation can be realized; the liquid crystal can be dynamically regulated and controlled by voltage, so that the spatial resolution is improved; the adjustable Fabry-Perot resonant cavity can be dynamically adjusted and controlled, and the spatial resolution is improved.
Based on the contents of the above embodiments, in the present embodiment, the thickness of the optical filter layer is 0.1 λ to 10 λ, where λ represents the center wavelength of the incident light.
In this embodiment, it should be noted that if the thickness of the optical filter layer is much smaller than the central wavelength of the incident light, the effective spectrum modulation effect cannot be achieved; if the thickness of the optical filter layer is much larger than the center wavelength of the incident light, it is difficult to fabricate in the process and a large optical loss is introduced. Therefore, in order to reduce optical loss, facilitate preparation, and ensure effective spectrum modulation effect in the present embodiment, the overall size (area) of each micro-nano cell in the optical filter layer 1 is generally λ 2 ～10 5 λ 2 The thickness is usually 0.1 λ to 10 λ (λ represents the central wavelength of the incident light of the environmental contaminant). As shown in FIG. 5, the overall size of each micro-nano unit is 0.5 μm 2 ～40000μm 2 The dielectric material in the optical filter layer 1 is polysilicon with a thickness of 50 nm-2 μm.
Based on the content of the above embodiments, in the present embodiment, the image sensor is any one or more of the following:
a CMOS Image Sensor (CIS), a Charge Coupled Device (CCD), a Single Photon Avalanche Diode (SPAD) array, and a focal plane photodetector array.
In this embodiment, it should be noted that, by using the wafer-level CMOS image sensor CIS, monolithic integration is implemented at the wafer level, which may reduce the distance between the image sensor and the optical filter layer to the greatest extent, and is beneficial for reducing the size of the cell and reducing the device volume and packaging cost, the SPAD may be used for weak light detection, and the CCD may be used for strong light detection.
In this embodiment, the optical filter layer and the image sensor may be manufactured by a Complementary Metal Oxide Semiconductor (CMOS) integration process, which is beneficial to reducing the failure rate of the device, improving the yield of the device, and reducing the cost. For example, the optical filter layer can be prepared by growing one or more layers of dielectric material directly on the image sensor, etching, depositing a metal material before removing the sacrificial layer used for etching, and finally removing the sacrificial layer.
Based on the content of the above embodiment, in the present embodiment, the types of the artificial neural network include: a feed-forward neural network.
In this embodiment, a feed Forward Neural Network (FNN), also called a Deep feed forward Network (DFN), and a Multi-Layer Perceptron (MLP) are the simplest Neural networks, and each neuron is arranged in a Layer. Each neuron is connected to only the neuron in the previous layer. And receiving the output of the previous layer and outputting the output to the next layer, wherein no feedback exists between the layers. The feedforward neural network has a simple structure, is easy to realize on hardware, has wide application, can approach any continuous function and square integrable function with any precision, and can accurately realize any limited training sample set. The feed forward network is a static non-linear mapping. Complex non-linear processing capabilities can be obtained by complex mapping of simple non-linear processing elements.
Based on the content of the above embodiments, in this embodiment, a light-transmitting medium layer is disposed between the optical filter layer and the image sensor.
In this embodiment, it should be noted that, by disposing the transparent medium layer between the optical filter layer and the image sensor, the optical filter layer and the image sensor layer can be effectively separated from each other, and mutual interference between the optical filter layer and the image sensor layer is avoided.
Based on the content of the above embodiments, in the present embodiment, the image sensor is a front-illuminated type, including: the optical filter layer is integrated on one surface, far away from the optical detection layer, of the metal wire layer; or the like, or a combination thereof,
the image sensor is of a back-illuminated type, including: the optical filter comprises an optical detection layer and a metal wire layer which are arranged from top to bottom, wherein an optical filter layer is integrated on one surface of the optical detection layer, which is far away from the metal wire layer.
In the present embodiment, in the front-illuminated image sensor, the silicon detection layer is below the metal wire layer, and the optical filter layer 1 is directly integrated onto the metal wire layer.
In the present embodiment, the back-illuminated image sensor is different from the front-illuminated image sensor in that the silicon detection layer is above the metal line layer and the optical filter layer 1 is directly integrated onto the silicon detection layer.
It should be noted that, for the back-illuminated image sensor, the silicon detection layer is above the metal line layer, so that the influence of the metal line layer on incident light can be reduced, and the quantum efficiency of the device can be improved.
According to the above, in the embodiment, the optical filter layer is used as the input layer of the artificial neural network, the image sensor is used as the linear layer of the artificial neural network, and the filtering effect of the optical filter layer on the incident light entering the optical filter layer is used as the connection weight from the input layer to the linear layer. In addition, the image information, the spectrum information, the angle information of the incident light and the phase information of the incident light at different points of the environment pollutant space are simultaneously utilized, so that the identification processing of the environment pollutants can be more accurately realized.
Based on the same inventive concept, another embodiment of the present invention provides an environmental monitoring apparatus, including: the light artificial neural network environmental protection monitoring chip is described in the above embodiment.
Because the environment monitoring instrument provided by this embodiment includes the optical artificial neural network environment-friendly monitoring chip described in the above embodiment, the environment monitoring instrument provided by this embodiment has all the beneficial effects of the optical artificial neural network environment-friendly monitoring chip described in the above embodiment, and this embodiment is not described in detail since the above embodiment has been described in detail.
Based on the same inventive concept, another embodiment of the present invention provides a method for manufacturing an optical artificial neural network environmental monitoring chip according to the above embodiment, as shown in fig. 13, the method specifically includes the following steps:
the optical filter layer is used for respectively carrying out different spectrum modulation on incident light entering different position points of the optical modulation structure through the optical modulation structure so as to obtain incident light carrying information corresponding to the different position points on the surface of the photosensitive area; the incident light carrying information comprises at least one of light intensity distribution information, spectrum information, angle information of the incident light and phase information of the incident light;
the image sensor is used for converting incident light carrying information corresponding to different position points after being modulated by the optical filter layer into electric signals corresponding to the different position points and sending the electric signals corresponding to the different position points to the processor; the processor is used for carrying out full connection processing and nonlinear activation processing on the electric signals corresponding to different position points to obtain an environment-friendly monitoring intelligent processing result; the electrical signal is an image signal modulated by the optical filter layer, and the incident light includes reflected light, transmitted light and/or radiated light of the environmental pollutant.
In this embodiment, the method further includes a training process of the optical artificial neural network environmental monitoring chip, specifically including:
and training an optical artificial neural network environment-friendly monitoring chip comprising different optical modulation structures, image sensors and processors with different full-connection parameters and different nonlinear activation parameters by using input training samples and output training samples corresponding to the environment-friendly monitoring intelligent processing task to obtain the optical modulation structures, the image sensors and the processors meeting the training convergence conditions, and taking the optical modulation structures, the image sensors and the processors meeting the training convergence conditions as the trained optical modulation structures, the trained image sensors and the trained processors.
Preparing an optical filter layer containing a light modulation structure on the surface of a photosensitive area of the image sensor, wherein the optical filter layer comprises:
growing one or more layers of preset materials on the surface of a photosensitive area of the image sensor;
performing dry etching on the one or more layers of preset materials to obtain an optical filter layer containing an optical modulation structure;
or the one or more layers of preset materials are subjected to imprinting transfer to obtain an optical filter layer containing an optical modulation structure;
or the one or more layers of preset materials are subjected to additional dynamic regulation to obtain an optical filter layer containing an optical modulation structure;
or printing the one or more layers of preset materials in a partition mode to obtain an optical filter layer containing an optical modulation structure;
or carrying out partition material growth on the one or more layers of preset materials to obtain an optical filter layer containing an optical modulation structure;
or quantum dot transfer is carried out on the one or more layers of preset materials to obtain the optical filter layer containing the optical modulation structure.
When the optical artificial neural network environment-friendly monitoring chip is used for an identification processing task of an environmental pollutant, an input training sample and an output training sample corresponding to the identification processing task are utilized to train the optical artificial neural network environment-friendly monitoring chip comprising different optical modulation structures, image sensors and processors with different full-connection parameters and nonlinear activation parameters, and the optical modulation structures, the image sensors and the processors meeting the training convergence conditions are obtained.
In this embodiment, it should be noted that, as shown in fig. 1, the optical filter layer 1 may be prepared by directly growing one or more layers of dielectric materials on the image sensor 2, then etching, depositing a metal material before removing the sacrificial layer for etching, and finally removing the sacrificial layer. By designing the size parameters of the light modulation structure, each unit can have different modulation effects on light with different wavelengths in a target range, and the modulation effects are insensitive to incident angles and polarization. Each cell in the optical filter layer 1 corresponds to one or more pixels on the image sensor 2. 1 is prepared directly on 2.
In this embodiment, it should be noted that, assuming that the image sensor 2 is of a backside-illuminated structure, the optical filter layer 1 may be prepared by directly etching on a silicon detector layer of the backside-illuminated image sensor and then depositing metal.
In addition, it should be noted that the optical modulation structure on the optical filter layer may be dry-etched by performing a pattern of the optical modulation structure on one or more layers of the preset materials, where the dry-etching is to directly remove an unnecessary portion of the one or more layers of the preset materials on the surface of the photosensitive area of the image sensor, so as to obtain the optical filter layer including the optical modulation structure; or one or more layers of preset materials are subjected to imprinting transfer, the imprinting transfer is to prepare a required structure on other substrates through etching, and the structure is transferred to a photosensitive area of the image sensor through PDMS and other materials to obtain an optical filter layer containing an optical modulation structure; or one or more layers of preset materials are subjected to external dynamic regulation and control, wherein the external dynamic regulation and control adopts active materials, and then an external electrode is used for regulating and controlling the light modulation characteristics of corresponding areas by changing voltage to obtain an optical filter layer containing a light modulation structure; or one or more layers of preset materials are printed in a partition mode, and the partition printing is to obtain an optical filter layer containing the light modulation structure by adopting a printing technology in the partition mode; or carrying out partition material growth on one or more layers of preset materials to obtain an optical filter layer containing an optical modulation structure; or quantum dot transfer is carried out on one or more layers of preset materials to obtain the optical filter layer containing the optical modulation structure.
In addition, it should be noted that, because the preparation method provided in this embodiment is the preparation method of the optical artificial neural network environmental monitoring chip in the foregoing embodiment, for details of some principles, structures, and other aspects, reference may be made to the description of the foregoing embodiment, and details of this embodiment are not repeated here.
Based on this, the optical artificial neural network environmental monitoring chip provided by the embodiment of the invention realizes a brand-new intelligent chip capable of realizing the function of the artificial neural network, in the intelligent chip, the optical filter layer is used as the input layer of the artificial neural network, the image sensor is used as the linear layer of the artificial neural network, meanwhile, the filtering effect of incident light entering the optical filter layer by the optical filter layer corresponds to the connection weight from the input layer to the linear layer, namely, the optical filter layer and the image sensor in the intelligent chip realize the related functions of the input layer and the linear layer in the artificial neural network, namely, the embodiment of the invention separates the input layer and the linear layer in the artificial neural network realized by software in the prior art, and realizes the two-layer structure of the input layer and the linear layer in the artificial neural network by using a hardware mode, so that the input layer and the linear layer do not need to be separated when the intelligent processing of the artificial neural network is carried out by using the intelligent chip subsequently The complex signal processing and algorithm processing corresponding to the linear layer only need to be carried out by the processor in the intelligent chip and the related processing of full connection and nonlinear activation of the electric signal, thus greatly reducing the power consumption and time delay during the processing of the artificial neural network, and the embodiment of the invention can simultaneously utilize the image information, the spectrum information, the angle of the incident light and the phase information of the incident light of the environmental pollutant, namely the incident light carrying information at different points of the environmental pollutant space, therefore, the incident light carrying information at different points of the environmental pollutant space covers the information of the image, the component, the shape, the three-dimensional depth, the structure and the like of the environmental pollutant, therefore, when the identification processing is carried out according to the incident light carrying information at different points of the environmental pollutant space, the image, the component, the shape, the three-dimensional depth, the image and the time delay of the environmental pollutant can be covered, The chip for monitoring the environmental protection monitoring of the optical artificial neural network provided by the embodiment of the invention not only can realize the effects of low power consumption and low time delay, but also can improve the accuracy of the environmental protection monitoring result, thereby being better applied to the field of environmental protection monitoring processing.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (21)
1. The utility model provides a light artificial neural network environmental protection monitoring chip which for environmental protection monitoring intelligence processing task, includes: an optical filter layer, an image sensor, and a processor; the optical filter layer corresponds to an input layer of the artificial neural network and the connection weight from the input layer to the linear layer, and the image sensor corresponds to the linear layer of the artificial neural network; the processor corresponds to a nonlinear layer and an output layer of the artificial neural network;
the optical filter layer is arranged on the surface of a photosensitive area of the image sensor and comprises an optical modulation structure, and the optical filter layer is used for respectively carrying out different spectrum modulation on incident light entering different position points of the optical modulation structure through the optical modulation structure so as to obtain incident light carrying information corresponding to different position points on the surface of the photosensitive area; the incident light comprises reflected, transmitted and/or radiated light of the environmental contaminant;
the image sensor is used for converting incident light carrying information corresponding to different position points after being modulated by the optical filter layer into electric signals corresponding to the different position points and sending the electric signals corresponding to the different position points to the processor; the electric signal is an image signal modulated by the optical filter layer;
the processor is used for carrying out full connection processing and nonlinear activation processing on the electric signals corresponding to different position points to obtain an environment-friendly monitoring intelligent processing result;
wherein the environmental monitoring intelligent processing task comprises identification and/or qualitative analysis of environmental pollutants; the environment-friendly monitoring intelligent processing result comprises an environment-friendly monitoring intelligent processing result of the environmental pollutants and/or an environmental pollution qualitative analysis result.
2. The optical artificial neural network environmental protection monitoring chip according to claim 1, wherein the incident light carrying information includes at least one of light intensity distribution information, spectrum information, angle information of the incident light, and phase information of the incident light.
3. The optical artificial neural network environmental protection monitoring chip according to claim 1, wherein the optical artificial neural network environmental protection monitoring chip comprises a trained optical modulation structure, an image sensor and a processor;
the trained optical modulation structure, the image sensor and the processor are the optical modulation structure, the image sensor and the processor which meet the training convergence condition and are obtained by training an optical artificial neural network environment-friendly monitoring chip which comprises different optical modulation structures, image sensors and processors with different full-connection parameters and different nonlinear activation parameters by using an input training sample and an output training sample which correspond to the environment-friendly monitoring intelligent processing task;
the input training sample comprises incident light reflected, transmitted and/or radiated by samples having different environmental contaminants; the output training sample includes a level of an environmental contaminant.
4. The environmental monitoring chip for the optical artificial neural network according to claim 3, wherein the samples with different environmental pollutants comprise air samples with different pollutants, water quality samples with different pollutants and/or soil samples with different pollutants.
5. The optical artificial neural network environmental protection monitoring chip according to claim 3, wherein when training the optical artificial neural network environmental protection monitoring chip containing different optical modulation structures, image sensors and processors with different full connection parameters and nonlinear activation parameters, the different optical modulation structures are designed by adopting a computer optical simulation design mode.
6. The optical artificial neural network environmental protection monitoring chip according to any one of claims 1 to 4, wherein the optical modulation structure in the optical filter layer comprises a regular structure and/or an irregular structure; and/or the light modulating structures in the optical filter layer comprise discrete structures and/or continuous structures.
7. The optical artificial neural network environment-friendly monitoring chip according to any one of claims 1 to 4, wherein the optical modulation structure in the optical filter layer comprises a unit array consisting of a plurality of micro-nano units, and each micro-nano unit corresponds to one or more pixel points on the image sensor; the structures of the micro-nano units are the same or different.
8. The chip for environment-friendly monitoring of the optical artificial neural network according to claim 7, wherein the micro-nano units comprise regular structures and/or irregular structures; and/or the micro-nano unit comprises a discrete structure and/or a continuous structure.
9. The chip for monitoring the environmental protection of the optical artificial neural network according to claim 7, wherein the micro-nano unit comprises a plurality of groups of micro-nano structure arrays, and the structures of the micro-nano structure arrays in each group are the same or different.
10. The chip for environment-friendly monitoring of the optical artificial neural network according to claim 9, wherein each group of the micro-nano structure array has a function of broadband filtering or narrowband filtering.
11. The environmental monitoring chip for the optical artificial neural network as claimed in claim 9, wherein each micro-nano structure array is a periodic structure array or a non-periodic structure array.
12. The chip for environment-friendly monitoring of the optical artificial neural network according to claim 9, wherein the micro-nano units comprise one or more groups of micro-nano structure arrays with one or more groups of hollow structures.
13. The optical artificial neural network environmental monitoring chip according to claim 9, wherein the micro-nano units have polarization-independent characteristics.
14. The optical artificial neural network environmental protection monitoring chip according to claim 13, wherein the micro-nano unit has quadruple rotational symmetry.
15. The optical artificial neural network environmental protection monitoring chip according to claim 1, wherein the optical filter layer is composed of one or more filter layers;
the filter layer is prepared from one or more of semiconductor materials, metal materials, liquid crystals, quantum dot materials and perovskite materials; and/or the filter layer is prepared from one or more of a photonic crystal, a super surface, a random structure, a nano structure, a metal Surface Plasmon Polariton (SPP) micro-nano structure and an adjustable Fabry-Perot resonant cavity.
16. The optical artificial neural network environmental protection monitoring chip according to claim 15, wherein the semiconductor material comprises one or more of silicon, silicon oxide, silicon nitride, titanium oxide, composite materials mixed according to a preset proportion and direct band gap compound semiconductor materials; and/or the nanostructure comprises one or more of a nanodot two-dimensional material, a nanocolumn two-dimensional material and a nanowire two-dimensional material.
17. The optical artificial neural network environmental protection monitoring chip according to claim 1, wherein the thickness of the optical filter layer is 0.1 λ -10 λ, where λ represents the central wavelength of the incident light.
18. An environmental monitoring apparatus, comprising: the optical artificial neural network environmental monitoring chip of any one of claims 1 to 17.
19. The method for preparing the environmental monitoring chip for the optical artificial neural network according to any one of claims 1 to 17, comprising the following steps:
preparing an optical filter layer containing an optical modulation structure on the surface of a photosensitive area of the image sensor;
generating a processor with functions of carrying out full connection processing and nonlinear activation processing on signals;
connecting the image sensor and the processor;
the optical filter layer is used for respectively carrying out different spectrum modulation on incident lights entering different position points of the optical modulation structure through the optical modulation structure so as to obtain incident light carrying information corresponding to the different position points on the surface of the photosensitive area; the incident light carrying information comprises at least one of light intensity distribution information, spectrum information, angle information of the incident light and phase information of the incident light;
the image sensor is used for converting incident light carrying information corresponding to different position points after being modulated by the optical filter layer into electric signals corresponding to the different position points and sending the electric signals corresponding to the different position points to the processor; the processor is used for carrying out full connection processing and nonlinear activation processing on the electric signals corresponding to different position points to obtain an environment-friendly monitoring intelligent processing result; the electrical signal is an image signal modulated by the optical filter layer, and the incident light includes reflected light, transmitted light and/or radiated light of the environmental pollutants.
20. The method for manufacturing an environmental monitoring chip for an optical artificial neural network according to claim 19, wherein the step of manufacturing an optical filter layer including an optical modulation structure on a surface of a photosensitive region of the image sensor includes:
growing one or more layers of preset materials on the surface of a photosensitive area of the image sensor;
etching the light modulation structure pattern of the one or more layers of preset materials to obtain an optical filter layer containing a light modulation structure;
or the one or more layers of preset materials are subjected to imprinting transfer to obtain an optical filter layer containing an optical modulation structure;
or through carrying out additional dynamic modulation on the one or more layers of preset materials, obtaining an optical filter layer containing an optical modulation structure;
or printing the one or more layers of preset materials in a partition mode to obtain an optical filter layer containing an optical modulation structure;
or carrying out partition growth on the one or more layers of preset materials to obtain an optical filter layer containing an optical modulation structure;
or quantum dot transfer is carried out on the one or more layers of preset materials to obtain the optical filter layer containing the optical modulation structure.
21. The method for preparing the environmental protection monitoring chip for the optical artificial neural network according to claim 19, further comprising: the training process of the optical artificial neural network environment-friendly monitoring chip specifically comprises the following steps:
and training an optical artificial neural network environment-friendly monitoring chip comprising different optical modulation structures, image sensors and processors with different full-connection parameters and different nonlinear activation parameters by using input training samples and output training samples corresponding to the environment-friendly monitoring intelligent processing task to obtain the optical modulation structures, the image sensors and the processors meeting the training convergence conditions, and taking the optical modulation structures, the image sensors and the processors meeting the training convergence conditions as the trained optical modulation structures, the image sensors and the processors.
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