Blood glucose detection chip, intelligent blood glucose detector and preparation method

Abstract

The present invention provides a blood sugar detection chip, an intelligent blood sugar detector and a preparation method. The present invention embeds an artificial neural network on a hardware chip, uses the light modulation layer on the hardware chip as the input layer of the artificial neural network, uses the image sensor as the linear layer of the artificial neural network, and uses the filtering effect of the light modulation layer on the incident light as the connection weight from the input layer to the linear layer, so that when the blood sugar detection chip is used for subsequent blood sugar detection processing, it is no longer necessary to perform complex signal processing and algorithm processing corresponding to the input layer and the linear layer, which can greatly reduce the power consumption and delay of the artificial neural network processing. The present invention projects the image information, spectral information, angle of the incident light and phase information of the incident light at different positions of the part to be tested for blood sugar into the photocurrent response of the hardware chip, and realizes the full connection of the electrical signal in the processor, thereby realizing low-power, safe and reliable, fast, accurate and non-invasive blood sugar detection.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a blood sugar detection chip, an intelligent blood sugar detection meter and a preparation method thereof. Background Art

[0002] Diabetes Mellitus (DM) is a multi-cause metabolic disease caused by insufficient insulin secretion or impaired insulin utilization. It is characterized by chronic hyperglycemia, accompanied by disorders in carbohydrate, fat and protein metabolism, and can cause a series of serious complications. As one of the major diseases threatening human health, there is currently no cure for diabetes. Diabetes not only greatly reduces the quality of life of patients, but also increases the global economic burden. Therefore, research on the prevention and treatment of diabetes is urgent.

[0003] With the continuous improvement of living standards, diabetes has become the third largest disease in the world that seriously affects human health. In order to effectively monitor the blood sugar concentration of diabetic patients, home portable blood glucose meters and blood glucose test strips have been widely used. At present, the blood sugar detection on the market is mainly invasive or micro-invasive detection of blood samples, which brings great pain to patients and there is a risk of virus infection through blood. Therefore, non-invasive blood sugar detection has become an urgent need for the treatment of diabetes.

[0004] At present, the main methods of non-invasive blood glucose detection include optical and radiation methods, reverse iontophoresis analysis, electromagnetic wave method, ultrasonic method and tissue fluid extraction method. For example, the near-infrared spectroscopy detection method mainly uses the relationship between blood glucose concentration and its near-infrared spectral absorption, irradiates the skin with near-infrared light, and reflects the blood glucose concentration from the change in the intensity of the reflected light. This method has the advantages of fast measurement and no need for chemical reagents and consumables. However, due to the large individual differences of the subjects being measured and the very weak signals obtained, key technologies such as the selection of measurement sites, the selection of measurement conditions, and the method of extracting weak chemical information from overlapping spectra need to be further solved. In addition, the signal processing system is large and cannot be carried around. There is also a subcutaneous tissue fluid detection method, which reflects the blood glucose concentration by measuring the glucose concentration of the tissue fluid exuded from the subcutaneous tissue. According to this principle, a watch that detects glucose can be made, and the blood glucose concentration can be continuously monitored in real time. However, this method has poor accuracy and slow response speed, so it is difficult to replace the existing invasive blood glucose meter. Summary of the invention

[0005] In view of the problems existing in the prior art, the embodiments of the present invention provide a blood glucose detection chip, an intelligent blood glucose detection meter and a preparation method thereof.

[0006] Specifically, the embodiment of the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a blood sugar detection chip, comprising: a light modulation layer and an image sensor, wherein the blood sugar detection chip is connected to a processor; the light modulation layer corresponds to an input layer of an artificial neural network and a connection weight from the input layer to a linear layer, the image sensor corresponds to a linear layer of the artificial neural network; the processor corresponds to a nonlinear layer and an output layer of the artificial neural network;

[0008] The light modulation layer is arranged on the surface of the photosensitive area of ​​the image sensor, and the light modulation layer includes a light modulation structure, and the light modulation structure is used to perform different spectrum modulations on the incident light reflected and / or transmitted through the human body part to be tested and entering different positions of the light modulation structure, so as to obtain the incident light carrying information corresponding to different positions on the surface of the photosensitive area; the human body part to be tested is a part with blood sugar information;

[0009] The image sensor is used to convert the information carried by the incident light corresponding to different positions after being modulated by the light modulation layer into electrical signals corresponding to the different positions, and send the electrical signals corresponding to the different positions to the processor; the electrical signals are image signals modulated by the light modulation layer;

[0010] The processor is used to perform full connection processing and nonlinear activation processing on the electrical signals corresponding to different position points to obtain blood sugar detection results.

[0011] Furthermore, the information carried by the incident light includes at least one of light intensity distribution information, spectrum information, angle information of the incident light, and phase information of the incident light.

[0012] Further, the blood sugar detection chip includes a trained light modulation structure and an image sensor, and the processor is a trained processor;

[0013] The trained light modulation structure, image sensor and processor refer to the light modulation structure, image sensor and processor that meet the training convergence conditions by training blood glucose detection chips including different light modulation structures and image sensors and processors with different full connection parameters and nonlinear activation parameters using input training samples and output training samples corresponding to the blood glucose detection task;

[0014] The input training samples include incident light reflected or transmitted by a human body part to be tested with different blood sugar values; and the output training samples include the corresponding blood sugar values.

[0015] Furthermore, when training a blood glucose detection chip including different light modulation structures and image sensors and a processor with different fully connected parameters and nonlinear activation parameters, the different light modulation structures are designed and implemented by adopting computer optical simulation design.

[0016] Furthermore, the light source used to irradiate the part of the human body to be tested is a near-infrared light source.

[0017] Furthermore, the light modulation structure in the light modulation layer includes a regular structure and / or an irregular structure; and / or the light modulation structure in the light modulation layer includes a discrete structure and / or a continuous structure.

[0018] Furthermore, the light modulation structure in the light modulation layer includes a unit array composed of a plurality of micro-nano units, each micro-nano unit corresponds to one or more pixel points on the image sensor; and the structures of the various micro-nano units are the same or different.

[0019] Furthermore, the micro-nano unit includes a regular structure and / or an irregular structure; and / or the micro-nano unit includes a discrete structure and / or a continuous structure.

[0020] Furthermore, the micro-nano unit includes multiple groups of micro-nano structure arrays, and the structures of each group of micro-nano structure arrays are the same or different.

[0021] Furthermore, each group of micro-nano structure arrays has a broadband filtering or narrowband filtering function.

[0022] Furthermore, each group of micro-nano structure arrays is a periodic structure array or a non-periodic structure array.

[0023] Furthermore, the micro-nano unit includes one or more groups of empty structures in the multiple groups of micro-nano structure arrays.

[0024] Furthermore, the micro-nano unit has four-fold rotational symmetry.

[0025] Further, the light modulation layer is composed of one or more filter layers;

[0026] The filter layer is a filter layer made of one or more of semiconductor materials, metal materials, liquid crystals, quantum dot materials, and perovskite materials; and / or, the filter layer is a filter layer made of one or more of photonic crystals, metasurfaces, random structures, nanostructures, metal surface plasmon SPP micro-nano structures, and adjustable Fabry-Perot resonant cavities.

[0027] Furthermore, the semiconductor material includes one or more of silicon, silicon oxide, silicon nitride, titanium oxide, a composite material mixed in a preset proportion, and a direct bandgap compound semiconductor material; and / or, the nanostructure includes one or more of nanodot two-dimensional materials, nanocolumn two-dimensional materials, and nanowire two-dimensional materials.

[0028] Furthermore, the thickness of the light modulation layer is 0.1λ to 10λ, wherein λ represents the central wavelength of the incident light.

[0029] In a second aspect, an embodiment of the present invention further provides an intelligent blood glucose detector, comprising the blood glucose detection chip as described in the first aspect.

[0030] In a third aspect, an embodiment of the present invention further provides a method for preparing the blood glucose detection chip as described in the first aspect, comprising:

[0031] Preparing a light modulation layer including a light modulation structure on the surface of the photosensitive area of ​​the image sensor;

[0032] Generate a processor capable of fully connected signal processing and nonlinear activation processing;

[0033] connecting the image sensor and the processor;

[0034] The light modulation layer is used to perform different spectrum modulations on the 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; the incident light carrying information includes light intensity distribution information, spectrum information, angle information of the incident light, and phase information of the incident light;

[0035] The image sensor is used to convert the information carried by the incident light corresponding to different position points after modulation by the light modulation layer into electrical signals corresponding to different position points, and send the electrical signals corresponding to different position points to the processor; the processor is used to perform full connection processing and nonlinear activation processing on the electrical signals corresponding to different position points to obtain blood glucose detection results.

[0036] Furthermore, the method for preparing the blood sugar detection chip further includes: a training process for the blood sugar detection chip, specifically including:

[0037] Using input training samples and output training samples corresponding to the blood glucose detection task, blood glucose detection chips including different light modulation structures and image sensors and processors with different fully connected parameters and nonlinear activation parameters are trained to obtain light modulation structures, image sensors and processors that meet the training convergence conditions, and the light modulation structures, image sensors and processors that meet the training convergence conditions are used as the trained light modulation structures, image sensors and processors.

[0038] Furthermore, a light modulation layer including a light modulation structure is prepared on the surface of the image sensor, including:

[0039] Growing one or more layers of a preset material on the surface of the image sensor;

[0040] Etching the one or more layers of the preset material into a light modulation structure pattern to obtain a light modulation layer including the light modulation structure;

[0041] or performing embossing transfer on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure;

[0042] or obtaining a light modulation layer including a light modulation structure by externally dynamically modulating the one or more layers of the preset material;

[0043] or performing zone printing on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure;

[0044] or performing partitioned growth on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure;

[0045] Alternatively, quantum dots are transferred to the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure.

[0046] An embodiment of the present invention realizes a new blood glucose detection chip capable of realizing the function of an artificial neural network, in which an artificial neural network is embedded in a hardware manner, wherein a light modulation layer is arranged on the surface of a photosensitive area of ​​an image sensor, and the light modulation layer includes a light modulation structure, and the light modulation layer is used to perform different spectrum modulations 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, and accordingly, the image sensor is used to convert the incident light-carrying information corresponding to different positions into electrical signals corresponding to different positions, and at the same time, the processor connected to the image sensor is used to perform full-connection processing and nonlinear activation processing on the electrical signals corresponding to different positions, so as to obtain the output signal of the artificial neural network. It can be seen that in the blood glucose detection chip, the light modulation layer serves as an artificial The input layer of the neural network, the image sensor serves as the linear layer of the artificial neural network, and at the same time, the filtering effect of the light modulation layer on the incident light entering the light modulation layer corresponds to the connection weight from the input layer to the linear layer, that is, the light modulation layer and the image sensor in the blood glucose detection chip realize the relevant functions of the input layer and the linear layer in the artificial neural network, that is, the embodiment of the present invention separates the input layer and the linear layer in the artificial neural network implemented by software in the prior art, and implements the two-layer structure of the input layer and the linear layer in the artificial neural network by hardware, so that when the blood glucose detection chip is used for blood glucose detection processing in the subsequent process, there is no need to perform complex signal processing and algorithm processing corresponding to the input layer and the linear layer, and the processor in the blood glucose detection chip only needs to perform relevant processing with full connection and nonlinear activation of the electrical signal, which can greatly reduce the power consumption and delay during blood glucose detection. It can be seen that the embodiment of the present invention uses the light modulation layer as the input layer of the artificial neural network, the image sensor as the linear layer of the artificial neural network, and the filtering effect of the light modulation layer on the incident light entering the light modulation layer as the connection weight from the input layer to the linear layer. The light modulation layer and the image sensor are used to project the spatial spectrum information of the part to be tested for blood sugar into an electrical signal, and then the full connection processing and nonlinear activation processing of the electrical signal are implemented in the processor. It can be seen that the embodiment of the present invention can not only save the complex signal processing and algorithm processing corresponding to the input layer and the linear layer in the prior art, but also can greatly reduce the power consumption and delay during the processing of the artificial neural network.

[0047] In addition, it can be understood that the embodiments of the present invention can utilize the information carried by the incident light of the blood in the part of the human body to be tested, such as one or more of image information, spectral information, the angle of the incident light and the phase information of the incident light. Since the information carried by the incident light at different points of the blood in the part of the human body to be tested for blood glucose covers the image, composition, shape, three-dimensional depth, structure and other information of the part of the human body to be tested for blood glucose, when blood glucose testing is performed based on the information carried by the incident light at different points of the part of the human body to be tested for blood glucose, the image, composition, shape, three-dimensional depth, three-dimensional color and other information of the part of the human body to be tested for blood glucose can be covered, thereby solving the problem of difficulty in accurately realizing non-invasive blood glucose detection mentioned in the background technology part. It can be seen that the blood glucose detection chip provided by the embodiments of the present invention can simultaneously meet the effects of low power consumption, low latency and high recognition rate, thereby realizing non-invasive detection of blood glucose values ​​quickly and accurately.

[0048] It should be noted that the embodiments of the present invention realize low-power, safe and reliable, fast, accurate, and non-invasive blood glucose detection, thereby being able to well meet the needs of users for non-invasive and accurate blood glucose detection.

[0049] It can be seen that the embodiment of the present invention provides a new optoelectronic chip for accurately realizing non-invasive detection of blood sugar values. The chip embeds an artificial neural network part into an image sensor containing various light modulation layers to realize safe, reliable, fast and accurate non-invasive detection of blood sugar. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 is a schematic diagram of the structure of a blood sugar detection chip provided by an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the recognition principle of a blood sugar detection chip provided by an embodiment of the present invention;

[0053] Figure 3 is a disassembly diagram of a blood glucose detection chip provided by an embodiment of the present invention;

[0054] Figure 4a is a schematic diagram of detecting blood sugar value by identifying light transmitted by a user's finger provided by an embodiment of the present invention;

[0055] Figure 4bis a schematic diagram of identifying light reflected from a user's wrist to detect blood sugar levels provided by an embodiment of the present invention;

[0056] Figure 5 is a top view of a light modulation layer provided by one embodiment of the present invention;

[0057] Figure 6 is a top view of another light modulation layer provided by an embodiment of the present invention;

[0058] Figure 7 is a top view of another light modulation layer provided by an embodiment of the present invention;

[0059] Figure 8 is a top view of another light modulation layer provided by an embodiment of the present invention;

[0060] Fig. 9 is a top view of yet another light modulation layer provided by an embodiment of the present invention;

[0061] Fig.10 is a top view of yet another light modulation layer provided by an embodiment of the present invention;

[0062] Fig.11 This is a schematic diagram of the broadband filtering effect of a micro-nano structure provided by an embodiment of the present invention;

[0063] Fig.12 This is a schematic diagram of the narrow-band filtering effect of a micro-nano structure provided by an embodiment of the present invention;

[0064] Fig.13 is a schematic structural diagram of a front-illuminated image sensor provided by an embodiment of the present invention;

[0065] Fig.14 is a schematic structural diagram of a back-illuminated image sensor provided by an embodiment of the present invention;

[0066] Fig.15 It is a schematic flow chart of a method for preparing a blood glucose detection chip provided in another embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] like Figure 1As shown, a blood sugar detection chip provided by an embodiment of the present invention is used for blood sugar detection tasks, and includes: a light modulation layer 1 and an image sensor 2. The blood sugar detection chip is connected to a processor 3, and the processor 3 can be set in the chip or can be set independently of the chip; the light modulation layer 1 corresponds to the input layer of the artificial neural network and the connection weight from the input layer to the linear layer, and 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;

[0069] The light modulation layer 1 is arranged on the surface of the photosensitive area of ​​the image sensor, and the light modulation layer 1 includes a light modulation structure. The light modulation layer 1 is used to perform spectral modulation of the incident light reflected and / or transmitted through the human body part to be tested and entering different positions of the light modulation structure through the light modulation structure, with the intensity modulated according to the wavelength. That is, different intensities are modulated on the incident light of different wavelengths to obtain the incident light carrying information corresponding to different positions on the surface of the image sensor; the incident light carrying information includes light intensity distribution information, spectrum information, angle information of the incident light and phase information of the incident light; wherein the human body part to be tested is a part with blood sugar information, such as fingers, wrists, forearms, earlobes and other parts.

[0070] It can be seen that in this embodiment, it is necessary to use a light source to illuminate the part of the human body to be tested, and then allow the transmitted light or reflected light to enter the light modulation layer of the chip, and the light modulation structure in the light modulation layer adjusts the light incident on the light modulation structure. Since the light modulation structure in the light modulation layer is a pre-designed modulation pattern that can be used for blood sugar level detection, when the transmitted light or reflected light of the part of the human body to be tested enters the light modulation layer of the chip, the light modulation layer, image sensor and processor on the chip together as a whole of the artificial neural network identify the transmitted light or reflected light, and then obtain the blood sugar level detection result.

[0071] In this embodiment, the light source used to irradiate the part of the human body to be tested may be a near-infrared light source with a wavelength range of 780nm-2526nm.

[0072] In addition, it should be noted that the modulation pattern in the light modulation layer that can detect the blood sugar level can be set in the following manner:

[0073] For human blood sugar detection, the modulation pattern can be optically simulated on a computer to obtain the modulation intensity (transmittance) of the modulation pattern for different wavelength components of the incident light, which can be used as the connection weight from the input layer to the linear layer of the artificial neural network, and a nonlinear activation function can be implemented in the computer. The spectral signal data corresponding to a large amount of blood sugar value data can be collected in advance and data training can be performed. The required modulation pattern can be designed and prepared, and the input layer and linear layer of the artificial neural network can be implemented on the chip through hardware (optical modulation layer and image sensor). In this way, during the process of the user's blood sugar detection, the electrical signal obtained by modulating the incident light of different wavelengths can be algorithmically restored, so that the user's blood sugar value can be quickly and accurately detected.

[0074] The processor 3 is used to perform full-connection processing on the electrical signals corresponding to different position points to obtain the output signal of the artificial neural network, that is, to obtain the blood sugar test result.

[0075] In this embodiment, the information carried by the incident light includes image information of blood in the part of the human body to be detected and / or various optical spatial information. For example, the information carried by the incident light includes at least one of light intensity distribution information, spectral information, angle information of the incident light, and phase information of the incident light.

[0076] In this embodiment, the light modulation layer 1 is disposed on the surface of the image sensor, and the light modulation layer 1 includes a light modulation structure. The optical filter 1 layer is used to perform different spectrum modulations on the incident light entering different positions of the light modulation structure through the light modulation structure, so as to obtain information carried by the modulated incident light corresponding to different positions on the surface of the image sensor. It can be seen that in the embodiment, the light modulation layer corresponds to the input layer of the artificial neural network, and the modulation effect of the light modulation structure on the light modulation layer on the incident light can be regarded as the connection weight from the input layer to the linear layer;

[0077] In this embodiment, the light modulation layer 1 includes a light modulation structure, through which the spectrum of the incident light (such as reflected light, transmitted light, radiated light and other related action light of the target to be identified) entering different positions of the light modulation structure is modulated with different intensities, so as to obtain light field distribution signals corresponding to different positions on the surface of the image sensor 2 (the modulated incident light carries information).

[0078] In this embodiment, it can be understood that the modulation intensity is related to the specific structural form of the light modulation structure. For example, different modulation intensities can be achieved by designing different light modulation structures (such as changing the shape and / or size parameters of the light modulation structure).

[0079] In this embodiment, it can be understood that the light modulation structures at different positions on the light modulation layer 1 have different spectral modulation effects on the incident light, and the modulation intensity of the light modulation structure on different wavelength components of the incident light corresponds to the connection strength of the linear layer of the artificial neural network, that is, the connection weight from the input layer to the linear layer. It should be noted that the light modulation layer 1 is composed of a plurality of optical filter units, and the light modulation structures at different positions in each optical filter unit are different, so they have different spectral modulation effects on the incident light; the light modulation structures at different positions between the optical filter units can be the same or different, so they have the same or different spectral modulation effects on the incident light.

[0080] In this embodiment, the image sensor 2 converts the light intensity distribution signals corresponding to different position points into electrical signals corresponding to different position points, and sends the electrical signals corresponding to different position points to the processor 3. The image sensor 2 corresponds to the linear layer of the neural network.

[0081] In this embodiment, the processor 3 performs full connection processing and nonlinear activation processing on the electrical signals at different positions, thereby obtaining the output signal of the artificial neural network.

[0082] It can be understood that processor 3 corresponds to the nonlinear layer and output layer of the neural network, and can also be understood as corresponding to the remaining layers (all other layers) in the neural network except the input layer and the linear layer.

[0083] In addition, it should be noted that the processor 3 can be set in the blood glucose detection chip, that is, the processor 3 can be set in the blood glucose detection chip together with the filter layer 1 and the image sensor 2, or it can be set separately outside the blood glucose detection chip and connected to the image sensor 2 in the blood glucose detection chip through a data line or a connecting device. This embodiment does not limit this.

[0084] In addition, it should be noted that the processor 3 can be implemented by a computer, or by an ARM or FPGA circuit board with certain computing power, or by a microprocessor, and this embodiment does not limit this. In addition, as mentioned above, the processor 3 can be integrated in the blood glucose detection chip, or it can be independently arranged outside the blood glucose detection chip. When the processor 3 is independently arranged outside the blood glucose detection chip, the electrical signal in the image sensor 2 can be read into the processor 3 through the signal readout circuit, and then the processor 3 performs full connection processing and nonlinear activation processing on the readout electrical signal.

[0085] In this embodiment, it can be understood that when the processor 3 performs nonlinear activation processing, it can be implemented by using a nonlinear activation function, for example, a Sigmoid function, a Tanh function, a ReLU function, etc., and this embodiment does not limit this.

[0086] In this embodiment, the light modulation layer 1 corresponds to the input layer of the artificial neural network and the connection weight from the input layer to the linear layer. The image sensor 2 corresponds to the linear layer of the artificial neural network, and converts the light intensity distribution signal of different spatial positions into electrical signals. The processor 3 corresponds to the nonlinear layer and output layer of the artificial neural network, and fully connects the electrical signals at different positions. The output signal of the artificial neural network is obtained through a nonlinear activation function to realize the detection of blood sugar.

[0087] like Figure 2 As shown on the left, the blood sugar detection chip includes a light modulation layer 1 and an image sensor 2. The processor 3 is connected to the image sensor 2 in the blood sugar detection chip. Figure 2 In the embodiment, the processor 3 is implemented by a signal readout circuit and a computer. Figure 2As shown on the right, the light modulation layer 1 in the blood glucose detection 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 output layer of the artificial neural network, and the filtering effect of the light modulation layer 1 on the incident light entering the light modulation layer 1 corresponds to the connection weight from the input layer to the linear layer. It can be seen that the light modulation layer and the image sensor in the blood glucose detection chip provided in this embodiment realize the relevant functions of the input layer and the linear layer in the artificial neural network by hardware, so that when the blood glucose detection chip is used for subsequent recognition processing, there is no need to perform complex signal processing and algorithm processing corresponding to the input layer and the linear layer (for example, calculations such as the connection weight from the input layer to the linear layer are omitted), which can greatly reduce the power consumption and delay during artificial neural network processing. In addition, the embodiment of the present invention simultaneously utilizes the image information, spectral information, angle of incident light and phase information of the blood of the part of the human body to be tested for blood glucose, that is, the information carried by the incident light at different spatial points of the blood of the part of the human body to be tested for blood glucose. Since the information carried by the incident light at different points of the blood of the part of the human body to be tested for blood glucose covers the image, composition, shape, three-dimensional depth, structure and other information of the part of the human body to be tested for blood glucose, when blood glucose is tested based on the information carried by the incident light at different points of the part of the human body to be tested for blood glucose, the image, composition, shape, three-dimensional depth, three-dimensional color and other information of the part of the human body to be tested for blood glucose can be covered, thereby solving the problem of difficulty in accurately realizing non-invasive blood glucose detection mentioned in the background technology part. It can be seen that the blood glucose detection chip provided by the embodiment of the present invention can simultaneously meet the effects of low power consumption, low latency and high recognition rate, thereby more accurately realizing non-invasive recognition of blood glucose values ​​of blood glucose information of the part of the human body to be tested.

[0088] like Figure 2 As shown on the right, the incident light spectrum P at different positions of the light modulation layer 1 λ Projected / connected to the image sensor photocurrent response I N In the figure, processor 3 includes a signal readout circuit and a computer. The signal readout circuit in processor 3 reads out the photocurrent response and transmits it to the computer, which performs full connection processing and nonlinear activation processing on the electrical signal and finally outputs the result.

[0089] like Figure 3As shown, the light modulation structure on the light modulation layer 1 is integrated above the image sensor 2, modulates the incident light, and 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 image information of the incident light. That is, after the incident light passes through the light modulation layer 1, it is converted into an electrical signal by the image sensor 2 to form an image containing the spectrum information of the incident light. Finally, the processor 3 connected to the image sensor 2 processes the electrical signal containing the spectrum information and image information of the incident light. It can be seen that the blood glucose detection chip provided by the embodiment of the present invention can simultaneously meet the effects of low power consumption, low latency and high recognition rate, so that the blood glucose value can be detected quickly and accurately. In addition, the chip provided by this embodiment is small in size and can be integrated in a smaller device, so that it is convenient to carry.

[0090] In this embodiment, the information carried by the incident light 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.

[0091] For example, in one implementation, the information carried by the incident light may include light intensity distribution information. In other implementations, multiple information including image information, spectral information, angle of incident light and phase information of incident light of the blood in the part to be detected of the human body can be simultaneously utilized to identify the target object, thereby more accurately realizing intelligent identification of blood sugar in the blood of the part to be detected of the human body.

[0092] An embodiment of the present invention realizes a new blood glucose detection chip capable of realizing the function of an artificial neural network, in which an artificial neural network is embedded in a hardware manner, wherein a light modulation layer is arranged on the surface of a photosensitive area of ​​an image sensor, and the light modulation layer includes a light modulation structure, and the light modulation layer is used to perform different spectrum modulations 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, and accordingly, the image sensor is used to convert the incident light-carrying information corresponding to different positions into electrical signals corresponding to different positions, and at the same time, the processor connected to the image sensor is used to perform full-connection processing and nonlinear activation processing on the electrical signals corresponding to different positions, so as to obtain the output signal of the artificial neural network. It can be seen that in the blood glucose detection chip, the light modulation layer serves as an artificial The input layer of the neural network, the image sensor serves as the linear layer of the artificial neural network, and at the same time, the filtering effect of the light modulation layer on the incident light entering the light modulation layer corresponds to the connection weight from the input layer to the linear layer, that is, the light modulation layer and the image sensor in the blood glucose detection chip realize the relevant functions of the input layer and the linear layer in the artificial neural network, that is, the embodiment of the present invention separates the input layer and the linear layer in the artificial neural network implemented by software in the prior art, and implements the two-layer structure of the input layer and the linear layer in the artificial neural network by hardware, so that when the blood glucose detection chip is used for blood glucose detection processing in the subsequent process, there is no need to perform complex signal processing and algorithm processing corresponding to the input layer and the linear layer, and the processor in the blood glucose detection chip only needs to perform relevant processing with full connection and nonlinear activation of the electrical signal, which can greatly reduce the power consumption and delay during blood glucose detection.It can be seen that the embodiment of the present invention uses the light modulation layer as the input layer of the artificial neural network, the image sensor as the linear layer of the artificial neural network, and the filtering effect of the light modulation layer on the incident light entering the light modulation layer as the connection weight from the input layer to the linear layer. The light modulation layer and the image sensor are used to project the spatial spectrum information of the part to be tested for blood glucose into an electrical signal, and then the full connection processing and nonlinear activation processing of the electrical signal are implemented in the processor. It can be seen that the embodiment of the present invention can not only save the complex signal processing and algorithm processing corresponding to the input layer and the linear layer in the prior art, but also the embodiment of the present invention can use one or more information of the image information, spectrum information, angle of the incident light and phase information of the blood of the part of the human body to be tested for blood glucose, that is, the spatial spectrum information of the part to be tested for blood glucose. The incident light carries information at different spatial points of the blood in the part of the human body to be tested for blood glucose. Since the incident light carries information at different points of the blood in the part of the human body to be tested for blood glucose covers the image, composition, shape, three-dimensional depth, structure and other information of the part of the human body to be tested for blood glucose, when blood glucose testing is performed based on the information carried by the incident light at different points of the part of the human body to be tested for blood glucose, the image, composition, shape, three-dimensional depth, three-dimensional color and other information of the part of the human body to be tested for blood glucose can be covered, thereby solving the problem of difficulty in accurately realizing non-invasive detection of blood glucose mentioned in the background technology part. It can be seen that the blood glucose detection chip provided by the embodiment of the present invention can simultaneously meet the effects of low power consumption, low latency and high recognition rate, thereby realizing non-invasive detection of blood glucose values ​​quickly and accurately.

[0093] It should be noted that the embodiments of the present invention realize low-power, safe and reliable, fast, accurate, and non-invasive blood glucose detection, thereby being able to well meet the needs of users for non-invasive and accurate blood glucose detection.

[0094] It can be seen that the embodiment of the present invention provides a new optoelectronic chip for accurately realizing non-invasive detection of blood sugar values. The chip embeds an artificial neural network part into an image sensor containing various light modulation layers to realize safe, reliable, fast and accurate non-invasive detection of blood sugar.

[0095] It should be noted that the novel optoelectronic chip for accurately detecting blood sugar levels provided by the embodiments of the present invention has the advantages of high measurement accuracy, portability, real-time online detection, and simple operation. It can greatly improve the quality of life of diabetic patients and has broad market prospects.

[0096] Based on the content of the above embodiment, in this embodiment (the processor may be independent of the chip or may be located in the chip), the blood glucose detection chip includes a trained light modulation structure and an image sensor, and the processor is a trained processor;

[0097] The trained light modulation structure, image sensor and processor refer to the light modulation structure, image sensor and processor that meet the training convergence conditions by training blood glucose detection chips including different light modulation structures and image sensors and processors with different full connection parameters and nonlinear activation parameters using input training samples and output training samples corresponding to the blood glucose detection task;

[0098] The input training samples include incident light reflected or transmitted by a human body part to be tested with different blood sugar values; and the output training samples include the corresponding blood sugar values.

[0099] It can be understood that since the filtering effect of the optical modulation layer on the incident light entering the optical modulation layer corresponds to the connection weight from the input layer to the linear layer of the artificial neural network, during training, changing the optical modulation structure in the optical modulation layer is equivalent to changing the connection weight from the input layer to the linear layer of the artificial neural network. Through the training convergence conditions, the optical modulation structure suitable for the current recognition task is determined, as well as the fully connected parameters and nonlinear activation parameters suitable for the current recognition task, thereby completing the training of the blood glucose detection chip.

[0100] It is understandable that for non-invasive blood sugar testing, a large amount of spectral signal data corresponding to different parts of the human body with blood sugar values ​​can be collected first, and the response of the incident light passing through the micro-nano modulation structure can be simulated by computer. Through data training, the required micro-nano modulation structure can be reversely designed and integrated on the image sensor. In the process of the user testing blood sugar, the electrical signal obtained by modulating the incident light of different wavelengths can be restored by algorithm, so that the user's blood sugar value can be quickly and accurately detected.

[0101] Specifically, for human blood sugar detection, the modulation intensity (transmittance) of the modulation structure for different wavelength components of the incident light can be obtained by optically simulating the micro-nano modulation structure on the computer, and it can be used as the connection weight from the input layer to the linear layer of the artificial neural network, and the nonlinear activation function can be implemented in the computer. The spectral signal data corresponding to a large amount of blood sugar data can be collected in advance and data training can be performed. The required micro-nano modulation structure can be designed and prepared, and the input layer and linear layer of the artificial neural network can be implemented on the chip. In the process of the user's blood sugar detection, the electrical signal obtained by modulating the incident light of different wavelengths can be restored by algorithm, so that the user's blood sugar value can be quickly and accurately detected.

[0102] It is understandable that after the blood glucose detection chip is trained, the blood glucose detection chip can be used to perform recognition tasks. Specifically, after the incident light of the image information of the blood glucose information of the part to be detected by the human body and the spatial spectrum information enters the light modulation layer 1 of the trained blood glucose detection chip, the light modulation structure in the light modulation layer 1 will modulate the incident light, and the intensity of the modulated light signal is detected by the image sensor 2 and converted into an electrical signal, and then the processor 3 performs full connection processing and nonlinear activation processing to obtain the blood glucose value detection result.

[0103] like Figure 4a As shown, the complete process of identifying the blood sugar information of the part to be detected in the human body to detect the blood sugar value is: the light source 100 (the light source of the mobile phone screen can be used) is irradiated onto the part to be detected 200 (finger) of the human body with blood sugar information, and then the transmitted light of the blood sugar information of the part to be detected in the human body is collected by the blood sugar detection chip 300, and after being processed by the light modulation layer, image sensor and processor in the blood sugar detection chip, the blood sugar value detection result can be obtained.

[0104] Another example Figure 4b As shown, the complete process of identifying the blood sugar information of the part to be detected in the human body to detect the blood sugar value is: the light source 100 (the light source of the mobile phone screen can be used) is irradiated to the part to be detected 200 (wrist) of the human body with blood sugar information, and then the reflected light of the blood sugar information of the part to be detected in the human body is collected by the blood sugar detection chip 300, and after being processed by the light modulation layer, image sensor and processor in the blood sugar detection chip, the blood sugar value detection result can be obtained.

[0105] In this embodiment, the light source 100 may be a near-infrared light source with a wavelength range of 780 nm-2526 nm.

[0106] There are many options for the body parts to be tested, including but not limited to fingers, wrists, forearms, earlobes, etc. The detection methods include but are not limited to transmission detection, reflection detection, and a combination of transmission detection and reflection detection.

[0107] It should be noted that the chip directly prepares micro-nano modulation structures on the surface of the photosensitive area of ​​the image sensor. Several discrete or continuous micro-nano structures constitute a unit. The micro-nano modulation structures at different positions have different spectral modulation effects on the incident light, and together constitute a light modulation layer. The modulation intensity of these micro-nano modulation structures on different wavelength components of the incident light corresponds to the connection strength (linear layer weight) of the artificial neural network. The spatial spectrum information of the part to be tested for blood sugar is projected into an electrical signal using the light modulation layer and the image sensor, and the electrical signals corresponding to different positions are sent to the processor. The processor fully connects the electrical signals corresponding to different positions to realize the artificial neural network of spectral imaging.

[0108] For non-invasive blood glucose testing, the spectral signal data corresponding to a large amount of blood glucose data can be collected first, and the response of the incident light passing through the micro-nano modulation structure can be simulated by computer. Through data training, the required micro-nano modulation structure can be reversely designed and integrated on top of the image sensor. In the process of the user's blood glucose detection, the electrical signal obtained by modulating the incident light of different wavelengths can be restored by algorithm, so that the user's blood glucose value can be quickly and accurately detected. The chip uses the spectral information of the blood sugar content in the human body and embeds an artificial neural network in the hardware to improve the speed and accuracy of blood glucose detection. In addition, the chip solution can be mass-produced using the existing CMOS process, reducing the size, power consumption and cost of the device.

[0109] In this embodiment, the optical modulation layer 1 is used as the input layer of the neural network, and the modulation intensity of different wavelength components in the incident light of the blood glucose information of the part to be detected by the optical modulation structure in the optical modulation layer is used as the connection weight from the input layer to the linear layer of the neural network. In order to minimize the loss function of the neural network, the modulation intensity of different wavelength components in the incident light of the blood glucose information of the part to be detected by the optical modulation layer can be adjusted by adjusting the structure of the optical modulation layer, thereby realizing the adjustment of the connection weight from the input layer to the linear layer, and then optimizing the training of the neural network.

[0110] Therefore, the optical modulation structure in this embodiment is obtained based on neural network training. The training samples are optically simulated by a computer to obtain the sample modulation intensity of different wavelength components of the incident light of the optical modulation structure for the blood glucose information of the human body part to be detected in the blood glucose detection intelligent processing task. The sample modulation intensity is used as the connection weight from the input layer to the linear layer of the neural network. The neural network is trained using the training samples corresponding to the blood glucose detection intelligent processing task until the neural network converges and the corresponding training sample optical modulation structure is used as the optical modulation layer corresponding to the blood glucose detection intelligent processing task.

[0111] Based on the contents of the above embodiments, in this embodiment, when training different light modulation structures, image sensors and processors with different fully connected parameters, the different light modulation structures are designed and implemented by adopting computer optical simulation design.

[0112] This embodiment designs the light modulation structure through computer optical simulation, adjusts the light modulation structure through optical simulation, and determines the corresponding light modulation structure as the size of the light modulation structure that needs to be produced finally when the neural network converges, thereby saving prototype production time and cost, improving product efficiency, and easily solving complex optical problems. For example, the light modulation structure can be simulated and designed through FDTD software, and the light modulation structure can be changed in the optical simulation, so that the modulation intensity of the light modulation structure for different incident lights can be accurately predicted, and it can be used as the connection weight between the input layer and the linear layer of the neural network to train the blood glucose detection chip and accurately obtain the light modulation structure.

[0113] It is understandable that multiple reflected light or transmitted light collections can be performed on human body parts with different blood sugar values ​​in advance and data training can be performed, the required micro-nano modulation structures can be designed and prepared, and the input layer and linear layer of the artificial neural network can be implemented on the chip.

[0114] It can be seen that this embodiment designs the light modulation structure by adopting the computer optical simulation design method, which saves the time and cost of prototyping the light modulation structure and improves product efficiency.

[0115] The optical artificial neural network blood glucose detection chip based on micro-nano modulation structure and image sensor provided in this embodiment has a structural schematic diagram as shown in FIG. Figure 2 As shown, it includes a light modulation layer 1, an image sensor 2 and a processor 3. The light modulation layer 1 corresponds to the input layer and the linear layer of the optical artificial neural network, wherein a plurality of discrete or continuous micro-nano modulation structures constitute a unit, and these modulation structures have different broadband spectrum modulation effects on the incident light. The micro-nano modulation structures contained in different units can be the same or different, and can also be spatially reconstructed according to the image. Each unit corresponds to a plurality of image sensor photosensitive pixels in the vertical direction. The incident light spectrum P at different positions of the light modulation layer 1 λ Projected / connected to the image sensor photocurrent response I N In the figure, processor 3 includes a signal readout circuit and a computer. The signal readout circuit in processor 3 reads out the photocurrent response and transmits it to the computer. The computer performs full connection processing and nonlinear activation processing of the electrical signal and finally outputs the blood glucose test result.

[0116] From a vertical perspective, Figure 2 As shown, each micro-nano modulation structure in the optical modulation layer is obtained by pre-training and designing an artificial neural network, and can be prepared by directly growing one or more layers of dielectric or metal materials on the image sensor and then etching. The overall size of each modulation unit in the optical modulation layer is usually λ 2 ~10 5 λ 2, the thickness is usually 0.1λ~10λ, λ is the central wavelength of the target band. Each modulation unit structure in the optical modulation layer corresponds to multiple pixels on the image sensor. The optical modulation layer is directly prepared on the image sensor, and the image sensor and the processor are connected through electrical contacts.

[0117] It can be understood that both the light modulation layer and the CIS wafer (CIS wafer is a special image sensor) can be manufactured by the semiconductor CMOS integration process. The light modulation layer can be monolithically integrated on the image sensor directly from the wafer level, and the chip can be prepared by a one-time flow using the CMOS process, thereby achieving monolithic integration at the wafer level, which is beneficial to reducing the distance between the sensor and the light modulation layer, reducing the size of the device, and reducing packaging costs.

[0118] It can be seen that this embodiment uses the light modulation layer on the hardware chip as the input layer of the artificial neural network, uses the image sensor on the hardware chip as the linear layer of the artificial neural network, and uses the filtering effect of the light modulation layer on the hardware chip on the incident light as the connection weight from the input layer to the linear layer, and then projects the spatial spectral information of the part to be detected containing blood sugar information into the photocurrent response of the image sensor, and realizes full connection of the electrical signal in the processor, so that non-destructive detection of blood sugar can be quickly realized.

[0119] The blood glucose detection chip based on micro-nano modulation structure and image sensor provided in this embodiment has the following effects: A. The artificial neural network part is embedded in the image sensor containing various light modulation layers, and the chip processes the reflected light or transmitted light of the measured part to realize safe, reliable, fast and accurate non-destructive detection of blood glucose. B. The preparation of the chip can be completed in one-time tape-out through CMOS process, which is conducive to reducing the failure rate of the device, improving the yield of the finished product of the device, and reducing the cost. C. The monolithic integration is realized at the wafer level, which can minimize the distance between the sensor and the light modulation layer, which is conducive to reducing the size of the unit, reducing the device volume and packaging cost, and is easy to integrate into a wearable real-time blood glucose detector later.

[0120] It can be seen that this embodiment uses the light modulation layer as the input layer and linear layer of the artificial neural network, uses the light modulation layer on the hardware chip as the input layer of the artificial neural network, uses the image sensor on the hardware chip as the linear layer of the artificial neural network, and uses the filtering effect of the light modulation layer on the hardware chip on the incident light as the connection weight from the input layer to the linear layer, thereby achieving low-power, safe and reliable, fast, accurate, and non-invasive blood glucose detection.

[0121] Based on the contents of the above embodiments, in the present embodiment, the light modulation structure in the light modulation layer includes a regular structure and / or an irregular structure; and / or, the light modulation structure in the light modulation layer includes a discrete structure and / or a continuous structure.

[0122] In this embodiment, the light modulation structure in the light modulation layer may include only regular structures, or only irregular structures, or both regular structures and irregular structures.

[0123] In this embodiment, the optical modulation structure includes a regular structure, which may refer to: the minimum modulation unit included in the optical modulation structure is a regular structure, such as the minimum modulation unit can be a regular shape such as a rectangle, a square, and a circle. In addition, the optical modulation structure includes a regular structure, which may refer to: the arrangement of the minimum modulation unit included in the optical modulation structure is regular, such as the arrangement can be a regular array form, a circular form, a trapezoidal form, a polygonal form, etc. In addition, the optical modulation structure includes a regular structure, which may refer to: the minimum modulation unit included in the optical modulation structure is a regular structure, and the arrangement of the minimum modulation unit is also regular, etc.

[0124] In this embodiment, the light modulation structure includes an irregular structure, which may refer to: the smallest modulation unit included in the light modulation structure is an irregular structure, such as the smallest modulation unit may be an irregular polygon, a random shape, or other irregular graphics. In addition, the light modulation structure includes an irregular structure, which may refer to: the arrangement of the smallest modulation unit included in the light modulation structure is irregular, such as the arrangement may be an irregular polygon, a random arrangement, etc. In addition, the light modulation structure includes an irregular structure, which may refer to: the smallest modulation unit included in the light modulation structure is an irregular structure, and the arrangement of the smallest modulation unit is also irregular, etc.

[0125] In this embodiment, the light modulation structure in the light modulation layer may include a discrete structure, a continuous structure, or both a discrete structure and a continuous structure.

[0126] In this embodiment, the light modulation structure includes a continuous structure, which may mean that the light modulation structure is composed of a continuous modulation pattern; the light modulation structure includes a discrete structure, which may mean that the light modulation structure is composed of a discrete modulation pattern.

[0127] It can be understood that the continuous modulation pattern here may refer to a straight line pattern, a wavy line pattern, a broken line pattern, etc.

[0128] It can be understood that the discrete modulation pattern here may refer to a modulation pattern formed by discrete graphics (such as discrete points, discrete squares, discrete irregular polygons, etc.).

[0129] In this embodiment, it should be noted that the light modulation structure has different modulation effects on light of different wavelengths, and specific modulation methods include but are not limited to scattering, absorption, interference, surface plasmon, resonance enhancement, etc. By designing different filter structures, the corresponding transmission spectra of light passing through different groups of filter structures are different.

[0130] Based on the contents of the above embodiments, in this embodiment, the light modulation layer is a single-layer structure or a multi-layer structure.

[0131] In this embodiment, it should be noted that the optical modulation layer may be a single-layer filter structure or a multi-layer filter structure, for example, a two-layer, three-layer, four-layer or other multi-layer structure.

[0132] In this embodiment, if Figure 1 As shown, the light modulation layer 1 is a single-layer structure, and the thickness of the light modulation layer 1 is related to the target wavelength range. For a wavelength of 400 nm to 10 μm, the thickness of the grating structure can be 50 nm to 5 μm.

[0133] It can be understood that since the function of the optical modulation layer 1 is to perform spectral modulation on the incident light, it is preferred to use a material with a high refractive index and low loss. For example, silicon, germanium, germanium silicon materials, silicon compounds, germanium compounds, III-V materials, etc. may be selected for preparation, wherein silicon compounds include but are not limited to silicon nitride, silicon dioxide, silicon carbide, etc.

[0134] 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 modulation layer 1 can be set to a multi-layer structure, and the optical modulation structure corresponding to each layer can be set to a different structure, thereby increasing the optical modulation layer's ability to modulate the spectrum of the incident light, so that more or more complex connection weights can be formed between the input layer and the linear layer, thereby improving the accuracy of the blood glucose detection chip when processing intelligent tasks.

[0135] In addition, it should be noted that, for a filter layer comprising a multi-layer structure, the material of each layer of the structure may be the same or different. For example, for a two-layer optical modulation layer 1, the first layer may be a silicon layer, and the second layer may be a silicon nitride layer.

[0136] It should be noted that the thickness of the light modulation layer 1 is related to the target wavelength range. For a wavelength of 400 nm to 10 μm, the total thickness of the multilayer structure can be 50 nm to 5 μm.

[0137] Based on the contents of the above embodiments, in this embodiment, the light modulation structure in the light modulation layer includes a unit array composed of multiple micro-nano units, each micro-nano unit corresponds to one or more pixel points on the image sensor; the structures of each micro-nano unit are the same or different.

[0138] In this embodiment, in order to obtain connection weights distributed in an array (connection weights for connecting the input layer and the linear layer) so that the processor can perform subsequent full connection and nonlinear activation processing, preferably, in this embodiment, the light modulation structure is in the form of an array structure. 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 pixels on the image sensor. It should be noted that the structures of each micro-nano unit may be the same or different. In addition, it should be noted that 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 a plurality of groups of micro-nano structure arrays, and the structures of each group of micro-nano structure arrays may be the same or different.

[0139] Combine the following Figures 5 to 9 For example, in this embodiment, Figure 5 As shown, the light modulation layer 1 includes a plurality of repeated continuous or discrete 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 non-periodic structure), and each micro-nano unit corresponds to one or more pixel points on the image sensor 2; Figure 6 As shown, the optical modulation 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 (similar to Figure 5 The difference is Figure 6 Each micro-nano unit is a periodic structure), and each micro-nano unit corresponds to one or more pixel points on the image sensor 2; Figure 7 As shown, the light modulation 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 each of which is a periodic structure), and each of which corresponds to one or more pixels on the image sensor 2. Figure 6 The difference is Figure 7 The unit shape of the periodic array in each micro-nano unit has four-fold rotational symmetry; Figure 8 As shown, the optical modulation layer 1 includes a plurality of micro-nano units, such as 11, 22, 33, 44, 55, and 66. Figure 6 The difference is that each micro-nano unit has a different structure, and each micro-nano unit corresponds to one or more pixels on the image sensor 2. In this embodiment, the light modulation layer 1 includes multiple different micro-nano units, that is, different areas on the blood sugar detection chip have different modulation effects on the incident light, thereby increasing the degree of freedom of design and further improving the accuracy of blood sugar detection. Fig. 9 As shown, the optical modulation 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. Figure 5The difference is that each micro-nano unit is composed of a discrete non-periodic array structure, and each micro-nano unit corresponds to one or more pixel points on the image sensor 2.

[0140] In this embodiment, the micro-nano unit has different modulation effects on light of different wavelengths, and specific modulation methods include but are not limited to scattering, absorption, interference, surface plasmon, resonance enhancement, etc. By designing different filter structures, the corresponding transmission spectra of light passing through different groups of filter structures are different.

[0141] Based on the contents of the above embodiments, in this embodiment, the micro-nano unit includes a regular structure and / or an irregular structure; and / or, the micro-nano unit includes a discrete structure and / or a continuous structure.

[0142] In this embodiment, the micro-nano unit may include only regular structures, only irregular structures, or both regular structures and irregular structures.

[0143] In this embodiment, the micro-nano unit includes a regular structure, which may refer to: the minimum modulation unit included in the micro-nano unit is a regular structure, such as the minimum modulation unit can be a regular shape such as a rectangle, a square, and a circle. In addition, the micro-nano unit includes a regular structure, which may refer to: the arrangement of the minimum modulation unit included in the micro-nano unit is regular, such as the arrangement can be a regular array form, a circular form, a trapezoidal form, a polygonal form, etc. In addition, the micro-nano unit includes a regular structure, which may refer to: the minimum modulation unit included in the micro-nano unit is a regular structure, and the arrangement of the minimum modulation unit is also regular, etc.

[0144] In this embodiment, the micro-nano unit includes an irregular structure, which may refer to: the minimum modulation unit included in the micro-nano unit is an irregular structure, such as the minimum modulation unit may be an irregular polygon, a random shape, or other irregular graphics. In addition, the micro-nano unit includes an irregular structure, which may refer to: the arrangement of the minimum modulation unit included in the micro-nano unit is irregular, such as the arrangement may be an irregular polygon, a random arrangement, etc. In addition, the micro-nano unit includes an irregular structure, which may refer to: the minimum modulation unit included in the micro-nano unit is an irregular structure, and the arrangement of the minimum modulation unit is also irregular, etc.

[0145] In this embodiment, the micro-nano units in the light modulation layer may include a discrete structure, a continuous structure, or both a discrete structure and a continuous structure.

[0146] In this embodiment, the micro-nano unit including a continuous structure may refer to that the micro-nano unit is composed of a continuous modulation pattern; the micro-nano unit including a discrete structure may refer to that the micro-nano unit is composed of a discrete modulation pattern.

[0147] It can be understood that the continuous modulation pattern here may refer to a straight line pattern, a wavy line pattern, a broken line pattern, etc.

[0148] It can be understood that the discrete modulation pattern here may refer to a modulation pattern formed by discrete graphics (such as discrete points, discrete triangles, discrete stars, etc.).

[0149] In this embodiment, it should be noted that different micro-nano units have different modulation effects on light of different wavelengths, and specific modulation methods include but are not limited to scattering, absorption, interference, surface plasmon, resonance enhancement, etc. By designing different micro-nano units, the corresponding transmission spectra of light passing through different groups of micro-nano units are different.

[0150] Based on the contents of the above embodiments, in this embodiment, the micro-nano unit includes multiple groups of micro-nano structure arrays, and the structures of each group of micro-nano structure arrays are the same or different.

[0151] In this embodiment, if Figure 5 As shown, the optical modulation layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, and 66, and each micro-nano unit includes a plurality of groups of micro-nano structure arrays. For example, the micro-nano unit 11 includes four different micro-nano structure arrays 110, 111, 112, and 113, and the filter unit 44 includes four different micro-nano structure arrays 440, 441, 442, and 443. Fig.10 As shown, the optical modulation layer 1 includes multiple micro-nano units, such as 11, 22, 33, 44, 55, and 66. Each micro-nano unit includes multiple groups of micro-nano structure arrays. For example, the micro-nano unit 11 includes 4 identical micro-nano structure arrays 110, 111, 112, and 113.

[0152] It should be noted that the micro-nano unit including four groups of micro-nano structure arrays is only used as an example here, which does not serve as a limitation. In practical applications, the micro-nano unit can also be provided with six groups, eight groups or other number of groups of micro-nano structure arrays as needed.

[0153] In this embodiment, each group of micro-nano structure arrays in the micro-nano unit has different modulation effects on light of different wavelengths, and each group of filter structures has different modulation effects on input light, and specific modulation methods include but are not limited to scattering, absorption, interference, surface plasmon, resonance enhancement, etc. By designing different micro-nano structure arrays, the corresponding transmission spectra of light passing through different groups of micro-nano structure arrays are different.

[0154] Based on the contents of the above embodiments, in this embodiment, each group of micro-nano structure arrays has the function of broadband filtering or narrowband filtering.

[0155] In this embodiment, in order to obtain the modulation intensity of different wavelength components of the incident light of the blood sugar information of the body to be detected as the connection weight of the neural network input layer and the linear layer, different micro-nano structure arrays are used to achieve broadband filtering and narrowband filtering. Therefore, in this embodiment, the micro-nano structure array performs broadband filtering or narrowband filtering on the incident light of the blood sugar information of the body to be detected to obtain the modulation intensity of different wavelength components of the incident light of the blood sugar information of the body to be detected. Fig.11 and Fig.12 As shown, each group of micro-nanostructure arrays in the light modulation layer has the function of broadband filtering or narrowband filtering.

[0156] It is understandable that, for each group of micro-nano structure arrays, they may all have broadband filtering effects, or they may all have narrowband filtering effects, or they may partially have broadband filtering effects and partially have narrowband filtering effects. In addition, the broadband filtering range and narrowband filtering range of each group of micro-nano structure arrays may also be the same or different. For example, by designing the period, duty cycle, radius, side length and other dimensional parameters of each group of micro-nano structures in the micro-nano unit, it has a narrowband filtering effect, that is, only one (or fewer) wavelengths of light can pass through. For another example, by designing the period, duty cycle, radius, side length and other dimensional parameters of each group of micro-nano structures in the micro-nano unit, it has a broadband filtering effect, that is, allowing more wavelengths or all wavelengths of light to pass through.

[0157] It is understandable that, in specific use, the filtering state of each group of micro-nano structure arrays can be determined by performing broadband filtering, narrowband filtering or a combination thereof according to the application scenario.

[0158] Based on the contents of the above embodiments, in this embodiment, each group of micro-nano structure arrays is a periodic structure array or a non-periodic structure array.

[0159] In this embodiment, each group of micro-nano structure arrays can be periodic structure arrays, non-periodic structure arrays, or partly periodic structure arrays and partly non-periodic structure arrays. Among them, the periodic structure array is easy to perform optical simulation design, and the non-periodic structure array can achieve more complex modulation effects.

[0160] In this embodiment, if Figure 5As shown, the optical modulation 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 non-periodic structures. The non-periodic structure refers to that the modulation hole shapes on the micro-nano structure array are arranged in a non-periodic arrangement. Figure 5 As shown, the micro-nano unit 11 includes four different non-periodic structure arrays 110, 111, 112 and 113, and the micro-nano unit 44 includes four different non-periodic structure arrays 440, 441, 442 and 443. The micro-nano structure arrays with non-periodic structures are designed by training neural network data for the intelligent processing task of blood glucose detection in the early stage, and are usually irregularly shaped structures. Figure 6 As shown, the optical modulation 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. Figure 5 The difference is that the micro-nano structure array is a periodic structure. The periodic structure refers to the arrangement of the modulated holes on the micro-nano structure array in a periodic arrangement. The basic unit of the periodic structure array is also obtained by collecting a large amount of blood sugar data in the early stage and training and designing the artificial neural network. The size of the period is usually 20nm~50μm. Figure 6 As shown, the micro-nano unit 11 includes four different periodic structure arrays 110, 111, 112 and 113, and the micro-nano unit 44 includes four different periodic structure arrays 440, 441, 442 and 443. The filter structure of the periodic structure is obtained by training and designing the neural network data for the blood sugar detection task in the early stage, and is usually an irregular structure. Figure 7 As shown, the optical modulation layer 1 includes a plurality of different 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 micro-nano structure arrays are different from each other, and the micro-nano structure arrays are periodic structures. The periodic structure refers to the arrangement of the shapes on the filter structure in a periodic arrangement, and the size of the period is usually 20nm to 50μm. Figure 7 As shown, the micro-nano structure arrays of the micro-nano unit 11 and the micro-nano unit 12 are different. The micro-nano unit 11 includes 4 different periodic structure arrays 110, 111, 112 and 113, and the micro-nano unit 44 includes 4 different periodic structure arrays 440, 441, 442 and 443. The micro-nano structure arrays with periodic structures are designed and obtained by neural network data training in the early stage for blood glucose detection tasks, and are usually irregularly shaped structures.

[0161] It should be noted that Figures 5 to 9Each micro-nano unit includes four groups of micro-nano structure arrays, and the four groups of micro-nano structure arrays are formed by modulation holes of four different shapes, and the four groups of micro-nano structure arrays are used to have different modulation effects on the incident light. It should be noted that here, only the micro-nano unit including four groups of micro-nano structure arrays is used as an example, which does not play a limiting role. In practical applications, micro-nano units including six groups, eight groups or other number groups of micro-nano structure arrays can also be set as needed. In this embodiment, the four different shapes can be circular, cross, regular polygon and rectangular (not limited to this).

[0162] In this embodiment, each group of micro-nano structure arrays in the micro-nano unit has different modulation effects on light of different wavelengths, and each group of micro-nano structure arrays has different modulation effects on input light, and specific modulation methods include but are not limited to scattering, absorption, interference, surface plasmon, resonance enhancement, etc. By designing different micro-nano structure arrays, the corresponding transmission spectra of light passing through different groups of micro-nano structure arrays are different.

[0163] Based on the contents of the above embodiments, in this embodiment, the micro-nano unit includes a plurality of micro-nano structure arrays, and there is one or more groups of empty structures.

[0164] Combine the following Fig. 9 The example shown is used for illustration. In this embodiment, Fig. 9 As shown, the optical modulation layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, and 66, each of which 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. Different from the above-mentioned embodiments, any micro-nano unit includes one or more groups of empty structures, and the empty structures are used to pass incident light directly. It can be understood that when a plurality of groups of micro-nano structure arrays include one or more groups of empty structures, a richer spectrum modulation effect can be formed, thereby meeting the spectrum modulation requirements in a specific scenario (or meeting the specific connection weight requirements between the input layer and the linear layer in a specific scenario).

[0165] like Fig. 9As shown, each micro-nano unit includes a group of micro-nano structure arrays and three groups of empty structures, micro-nano unit 11 includes a non-periodic structure array 111, micro-nano unit 22 includes a non-periodic structure array 221, micro-nano unit 33 includes a non-periodic structure array 331, micro-nano unit 44 includes a non-periodic structure array 441, micro-nano unit 55 includes a non-periodic structure array 551, and micro-nano unit 66 includes a non-periodic structure array 661, wherein the micro-nano structure array is used to modulate the incident light in different ways. It should be noted that here, only one group of micro-nano structure arrays and three groups of empty structures are used as an example, which does not play a limiting role. In practical applications, a micro-nano unit including a group of micro-nano structure arrays and five groups of empty structures or other number groups of micro-nano structure arrays can also be set according to needs. In this embodiment, the micro-nano structure array can be made of circular, cross-shaped, regular polygonal and rectangular modulation holes (not limited to this).

[0166] It should be noted that the multiple groups of micro-nano structure arrays included in the micro-nano unit may not include any empty structures, that is, the multiple groups of micro-nano structure arrays may be non-periodic structure arrays or periodic structure arrays.

[0167] Based on the contents of the above embodiments, in this embodiment, the micro-nano unit has polarization-independent characteristics.

[0168] In this embodiment, since the micro-nano unit has a polarization-independent characteristic, the light modulation layer is insensitive to the polarization of the incident light, thereby realizing a blood glucose detection chip that is insensitive to both the incident angle and polarization. The blood glucose detection chip provided by the embodiment of the present invention is insensitive to the incident angle and polarization characteristics of the incident light, that is, the measurement result will not be affected by the incident angle and polarization characteristics of the incident light, thereby ensuring the stability of the spectral measurement performance, and further ensuring the stability of blood glucose detection. It should be noted that the micro-nano unit may also have a polarization-dependent characteristic.

[0169] Based on the contents of the above embodiments, in this embodiment, the micro-nano unit has four-fold rotational symmetry.

[0170] In this embodiment, it should be noted that four-fold rotational symmetry is a specific case of polarization-independent characteristics. By designing the micro-nano unit to have a structure with four-fold rotational symmetry, the requirements of polarization-independent characteristics can be met.

[0171] Combine the following Figure 7 The example shown is used for illustration. In this embodiment, Figure 7As shown, the optical modulation layer 1 includes a plurality of repeated micro-nano units, such as 11, 22, 33, 44, 55, and 66, each of which is composed of a plurality of groups of micro-nano structure arrays. The structures corresponding to the plurality of groups of micro-nano structure arrays are different from each other. The micro-nano structure arrays are periodic structures. Different from the above-mentioned embodiments, the structure corresponding to each group of micro-nano structure arrays can be a circle, a cross, a regular polygon, a rectangle, and the like having four-fold rotational symmetry, that is, after the structure is rotated 90°, 180°, and 270°, it overlaps with the original structure, so that the structure has the characteristic of being independent of polarization, so that the same intelligent recognition effect can be achieved when different polarized light is incident.

[0172] Based on the contents of the above embodiments, in this embodiment, the optical modulation layer is composed of one or more filter layers;

[0173] The filter layer is a filter layer made of one or more of semiconductor materials, metal materials, liquid crystals, quantum dot materials, and perovskite materials; and / or, the filter layer is a filter layer made of one or more of photonic crystals, metasurfaces, random structures, nanostructures, metal surface plasmon SPP (Surface Plasmon Polaritons, SPP) micro-nano structures, and adjustable Fabry-Perot cavity (Fabry-perot Cavity, FP cavity).

[0174] The semiconductor material includes one or more of silicon, silicon oxide, silicon nitride, titanium oxide, a composite material mixed in a preset proportion, and a direct bandgap compound semiconductor material; and / or, the nanostructure includes one or more of nanodot two-dimensional materials, nanocolumn two-dimensional materials, and nanowire two-dimensional materials.

[0175] Among them, photonic crystals, as well as the combination of metasurfaces and random structures are compatible with CMOS technology and can have better modulation effects. Other materials can also be filled in the micropores of the micro-nano modulation structure to smooth the surface. Quantum dots and perovskites can utilize the spectral modulation characteristics of the material itself to minimize the volume of a single modulation structure. SPP has a small volume and can achieve polarization-dependent light modulation. Liquid crystals can be dynamically controlled by voltage to improve spatial resolution. Adjustable Fabry-Perot cavities can be dynamically controlled to improve spatial resolution.

[0176] Based on the content of the above embodiment, in this embodiment, the thickness of the light modulation layer is 0.1λ to 10λ, where λ represents the central wavelength of the incident light.

[0177] In this embodiment, it should be noted that if the thickness of the optical modulation layer is much smaller than the central wavelength of the incident light, it cannot play an effective spectrum modulation role; if the thickness of the optical modulation layer is much larger than the central wavelength of the incident light, it is difficult to prepare in terms of process, and will introduce large optical losses. Therefore, in this embodiment, in order to reduce optical losses, facilitate preparation, and ensure effective spectrum modulation, the overall size (area) of each micro-nano unit in the optical modulation layer 1 is usually λ 2 ~10 5 λ 2 The thickness is usually 0.1λ~10λ (λ represents the central wavelength of the incident light of the blood sugar information of the part of the human body to be detected). Figure 5 As shown, the overall size of each micro-nano unit is 0.5 μm 2 ~40000μm 2 The dielectric material in the light modulation layer 1 is polysilicon with a thickness of 50 nm to 2 μm.

[0178] It is understandable that the image sensor 2 can use a CIS wafer, and the light modulation layer 1 is directly prepared on the CIS wafer. The light modulation layer 1 includes multiple repeated modulation units, each of which includes 4 different continuous non-periodic structure arrays. The basic unit of the non-periodic structure array is obtained by collecting a large amount of different blood sugar information in the early stage and training and designing the artificial neural network data. It is usually an irregularly shaped structure. Each non-periodic structure array has different wide-spectrum modulation effects on the incident light, and the overall size of each modulation unit is 0.5μm. 2 ~40000μm 2 The dielectric material in the optical modulation layer 1 is polysilicon with a thickness of 50nm to 2μm. It can be understood that the CIS wafer includes a silicon detector layer and a metal wire layer, and the response range is from the visible to the near-infrared band; the CIS wafer is bare and the Bayer filter array and microlens array are not prepared. Each modulation unit corresponds to multiple sensor units on the CIS wafer.

[0179] Based on the contents of the above embodiments, in this embodiment, the image sensor is any one or more of the following:

[0180] CMOS image sensor (Contact Image Sensor, CIS), Charge Coupled Device (Charge Coupled Device, CCD), Single Photon Avalanche Diode (Single Photon Avalanche Diode, SPAD) array and focal plane photoelectric image sensor array.

[0181] In this embodiment, it should be noted that the use of a wafer-level CMOS image sensor CIS and monolithic integration at the wafer level can minimize the distance between the image sensor and the light modulation layer, which is beneficial for reducing the size of the unit, reducing the device volume and packaging cost. SPAD can be used for weak light detection, and CCD can be used for strong light detection.

[0182] In this embodiment, the light modulation layer and the image sensor can be manufactured by a complementary metal oxide semiconductor (CMOS) integrated process, which is conducive to reducing device failure rate, improving device yield and reducing cost. For example, the light modulation layer can be prepared by directly growing one or more layers of dielectric material on the image sensor, then etching, depositing metal material before removing the sacrificial layer used for etching, and finally removing the sacrificial layer.

[0183] Based on the contents of the above embodiments, in this embodiment, the types of the artificial neural network include: feedforward neural network.

[0184] In this embodiment, the feedforward neural network (FNN), also known as the deep feedforward network (DFN) and the multi-layer perceptron (MLP), is the simplest neural network, in which neurons are arranged in layers. Each neuron is only connected to the neurons in the previous layer. It receives the output of the previous layer and outputs it to the next layer, and there is no feedback between the layers. The feedforward neural network has a simple structure, is easy to implement on hardware, and is widely used. It can approximate any continuous function and square integrable function with arbitrary precision. And it can accurately implement any finite training sample set. The feedforward network is a static nonlinear mapping. Complex nonlinear processing capabilities can be obtained through the composite mapping of simple nonlinear processing units.

[0185] Based on the contents of the above embodiments, in this embodiment, a light-transmitting medium layer is disposed between the light modulation layer and the image sensor.

[0186] In this embodiment, it should be noted that a light-transmitting medium layer is provided between the light modulation layer and the image sensor, so as to effectively separate the light modulation layer from the image sensor layer and avoid mutual interference between the two.

[0187] Based on the contents of the above embodiments, in this embodiment, the image sensor is front-illuminated, comprising: a metal wire layer and a light detection layer arranged from top to bottom, and the light modulation layer is integrated on a side of the metal wire layer away from the light detection layer; or,

[0188] The image sensor is back-illuminated and comprises: a light detection layer and a metal wire layer arranged from top to bottom, and the light modulation layer is integrated on a side of the light detection layer away from the metal wire layer.

[0189] In this embodiment, if Fig.13 The image sensor shown is a front-illuminated image sensor, in which the silicon detection layer 21 is below the metal wire layer 22 , and the light modulation layer 1 is directly integrated onto the metal wire layer 22 .

[0190] In this embodiment, Fig.13 The difference is, Fig.14 The image sensor shown is a back-illuminated one, in which the silicon detection layer 21 is above the metal line layer 22 , and the light modulation layer 1 is directly integrated onto the silicon detection layer 21 .

[0191] It should be noted that, for a back-illuminated image sensor, the silicon detection layer 21 is above the metal wire layer 22, which can reduce the influence of the metal wire layer on the incident light, thereby improving the quantum efficiency of the device.

[0192] According to the above content, this embodiment uses the light modulation layer on the hardware chip as the input layer of the artificial neural network, uses the image sensor on the hardware chip as the linear layer of the artificial neural network, and uses the filtering effect of the light modulation layer on the hardware chip on the incident light as the connection weight from the input layer to the linear layer. The light modulation layer and image sensor in the blood glucose detection chip provided by this embodiment realize the relevant functions of the input layer and the linear layer in the artificial neural network by hardware, so that when the blood glucose detection chip is used for subsequent intelligent processing, there is no need to perform complex signal processing and algorithm processing corresponding to the input layer and the linear layer, which can greatly reduce the power consumption and delay during artificial neural network processing. In addition, the embodiment of the present invention simultaneously utilizes the image information, spectral information, angle of incident light and phase information of the blood of the part of the human body to be tested for blood glucose, that is, the information carried by the incident light at different spatial points of the blood of the part of the human body to be tested for blood glucose. Since the information carried by the incident light at different points of the blood of the part of the human body to be tested for blood glucose covers the image, composition, shape, three-dimensional depth, structure and other information of the part of the human body to be tested for blood glucose, when blood glucose is tested based on the information carried by the incident light at different points of the part of the human body to be tested for blood glucose, the image, composition, shape, three-dimensional depth, three-dimensional color and other information of the part of the human body to be tested for blood glucose can be covered, thereby solving the problem of difficulty in accurately realizing non-invasive detection of blood glucose mentioned in the background technology part. It can be seen that the blood glucose detection chip provided by the embodiment of the present invention can simultaneously meet the effects of low power consumption, low latency and high recognition rate, thereby realizing non-invasive detection of blood glucose values ​​quickly and accurately.

[0193] The optical artificial neural network blood glucose detection chip based on the optical filter and the image sensor in the embodiment of the present invention has the following effects: the artificial neural network is partially embedded in the image sensor containing various optical modulation layers to realize fast and accurate intelligent perception, recognition and / or decision-making functions. In addition, the embodiment of the present invention can also realize monolithic integration at the wafer level, so that the distance between the sensor and the optical modulation layer can be minimized, which is conducive to reducing the size of the unit, reducing the device volume and packaging cost.

[0194] Based on the same inventive concept, another embodiment of the present invention provides an intelligent blood glucose detector, comprising: a blood glucose detection chip as described in the above embodiment.

[0195] Preferably, the blood glucose detector in this embodiment can be a wearable blood glucose detector.

[0196] Since the smart blood glucose detector provided in this embodiment includes the blood glucose detection chip described in the above embodiment, the smart blood glucose detector provided in this embodiment has all the beneficial effects of the blood glucose detection chip described in the above embodiment. Since the above embodiment has already described this in detail, this embodiment will not repeat it.

[0197] Based on the same inventive concept, another embodiment of the present invention provides a method for preparing a blood glucose detection chip as described in the above embodiment, such as Fig.15 As shown, the specific steps include:

[0198] Step 1510: preparing a light modulation layer including a light modulation structure on the surface of the photosensitive area of ​​the image sensor;

[0199] Step 1520, generating a processor capable of performing full connection processing and nonlinear activation processing on the signal;

[0200] Step 1530: connecting the image sensor and the processor;

[0201] The light modulation layer is used to perform different spectrum modulations on the 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; the incident light carrying information includes light intensity distribution information, spectrum information, angle information of the incident light, and phase information of the incident light;

[0202] The image sensor is used to convert the information carried by the incident light corresponding to different position points after modulation by the light modulation layer into electrical signals corresponding to different position points, and send the electrical signals corresponding to different position points to the processor; the processor is used to perform full connection processing and nonlinear activation processing on the electrical signals corresponding to different position points to obtain blood glucose detection results.

[0203] In this embodiment, a training process for the optical artificial neural blood glucose detection chip is also included, which specifically includes:

[0204] Using input training samples and output training samples corresponding to the blood glucose detection task, blood glucose detection chips including different light modulation structures and image sensors and processors with different fully connected parameters and nonlinear activation parameters are trained to obtain light modulation structures, image sensors and processors that meet the training convergence conditions, and the light modulation structures, image sensors and processors that meet the training convergence conditions are used as the trained light modulation structures, image sensors and processors.

[0205] It can be understood that when training a blood glucose detection chip including different light modulation structures, image sensors and processors with different fully connected parameters, the different light modulation structures are designed and implemented by adopting computer optical simulation design.

[0206] It is understandable that multiple information collection and data training can be performed on various blood glucose value training samples in advance, the required micro-nano modulation structure can be designed and prepared, and the input layer and linear layer of the artificial neural network can be implemented on the chip.

[0207] In this embodiment, a light modulation layer including a light modulation structure is prepared on the surface of the photosensitive area of ​​the image sensor, including:

[0208] Growing one or more layers of a preset material on the surface of the image sensor;

[0209] Dry-etching the one or more layers of the preset material to form a light modulation structure pattern to obtain a light modulation layer including the light modulation structure;

[0210] or performing embossing transfer on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure;

[0211] Or by externally dynamically regulating the one or more layers of the preset material, a light modulation layer including a light modulation structure is obtained;

[0212] or performing zone printing on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure;

[0213] or performing partitioned material growth on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure;

[0214] Alternatively, quantum dots are transferred to the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure.

[0215] When the blood glucose detection chip is used for the task of detecting blood glucose levels in a part of the human body to be detected, the blood glucose detection chip including different light modulation structures, image sensors and processors with different fully connected parameters is trained using input training samples and output training samples corresponding to the task to obtain light modulation structures, image sensors and processors that meet the training convergence conditions.

[0216] In this embodiment, it should be noted that Figure 1 As shown, the light modulation layer 1 can be prepared by directly growing one or more layers of dielectric material on the image sensor 2, then etching, depositing metal material before removing the sacrificial layer used 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 of different wavelengths within the target range, and the modulation effect is insensitive to the incident angle and polarization. Each unit in the light modulation layer 1 corresponds to one or more pixels on the image sensor 2. 1 is prepared directly on 2.

[0217] In this embodiment, it should be noted that Fig.14 As shown, assuming that the image sensor 2 is a back-illuminated structure, the light modulation layer 1 can be prepared by directly etching the silicon image sensor layer 21 of the back-illuminated image sensor and then depositing metal.

[0218] In addition, it should be noted that the light modulation structure on the light modulation layer can be obtained by dry etching the light modulation structure pattern of one or more layers of preset materials. Dry etching is to directly remove the unnecessary parts of one or more layers of preset materials on the surface of the photosensitive area of ​​the image sensor to obtain a light modulation layer containing a light modulation structure; or one or more layers of preset materials are subjected to imprint transfer. Imprint transfer is to prepare the required structure on other substrates by etching, and then transfer the structure to the photosensitive area of ​​the image sensor through materials such as PDMS to obtain a light modulation layer containing a light modulation structure; or by externally dynamically regulating one or more layers of preset materials. The external dynamic regulation is to use active materials, and then the external electrodes regulate the light modulation characteristics of the corresponding areas by changing the voltage to obtain a light modulation layer containing a light modulation structure; or one or more layers of preset materials are subjected to zone printing. Zone printing is to use printing technology in zones to obtain a light modulation layer containing a light modulation structure; or one or more layers of preset materials are subjected to zone material growth to obtain a light modulation layer containing a light modulation structure; or one or more layers of preset materials are subjected to quantum dot transfer to obtain a light modulation layer containing a light modulation structure.

[0219] In addition, it should be noted that since the preparation method provided in this embodiment is the preparation method of the blood glucose detection chip in the above-mentioned embodiment, for detailed contents on some principles, structures or materials, etc., please refer to the introduction of the above-mentioned embodiment, and this embodiment will not go into details.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blood sugar detection chip, characterized in that: include: The light modulation layer and the image sensor, the blood sugar detection chip is connected to the processor; the light modulation layer corresponds to the input layer of the artificial neural network and the connection weight from the input layer to the linear layer, the image sensor corresponds to the linear layer of the artificial neural network; the processor corresponds to the nonlinear layer and the output layer of the artificial neural network; The light modulation layer is arranged on the surface of the photosensitive area of ​​the image sensor, and the light modulation layer includes a light modulation structure, and the light modulation structure is used to perform different spectrum modulations on the incident light reflected and / or transmitted through the human body part to be tested and entering different positions of the light modulation structure, so as to obtain the incident light carrying information corresponding to different positions on the surface of the photosensitive area; the human body part to be tested is a part with blood sugar information; The image sensor is used to convert the information carried by the incident light corresponding to different positions after being modulated by the light modulation layer into electrical signals corresponding to the different positions, and send the electrical signals corresponding to the different positions to the processor; The electrical signal is an image signal modulated by the light modulation layer; The processor is used to perform full connection processing and nonlinear activation processing on the electrical signals corresponding to different position points to obtain blood sugar test results; The light modulation structure in the light modulation layer includes a unit array composed of a plurality of micro-nano units, 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; the micro-nano units include a plurality of groups of micro-nano structure arrays, and the structures of the groups of micro-nano structure arrays are the same or different; The blood sugar detection chip includes a trained light modulation structure and an image sensor, and the processor is a trained processor; The trained light modulation structure, image sensor and processor refer to the light modulation structure, image sensor and processor that meet the training convergence conditions by training blood glucose detection chips including different light modulation structures and image sensors and processors with different full connection parameters and nonlinear activation parameters using input training samples and output training samples corresponding to the blood glucose detection task; The input training samples include incident light reflected or transmitted by a human body part to be tested with different blood sugar values; and the output training samples include the corresponding blood sugar values.

2. The blood sugar detection chip according to claim 1, characterized in that: The information carried by the incident light 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 blood sugar detection chip according to claim 1, characterized in that: When training a blood glucose detection chip including different light modulation structures and image sensors and a processor with different fully connected parameters and nonlinear activation parameters, the different light modulation structures are designed and implemented by adopting computer optical simulation design.

4. The blood glucose detection chip according to any one of claims 1 to 3, characterized in that: The light source used to irradiate the part of the human body to be tested is a near-infrared light source.

5. The blood glucose detection chip according to any one of claims 1 to 3, characterized in that: The light modulation structure in the light modulation layer includes a regular structure and / or an irregular structure; and / or, the light modulation structure in the light modulation layer includes a discrete structure and / or a continuous structure.

6. The blood sugar detection chip according to claim 1, characterized in that: The micro-nano unit includes a regular structure and / or an irregular structure; and / or, the micro-nano unit includes a discrete structure and / or a continuous structure.

7. The blood sugar detection chip according to claim 1, characterized in that: Each group of micro-nano structure arrays has the function of broadband filtering or narrowband filtering.

8. The blood sugar detection chip according to claim 1, characterized in that: Each group of micro-nano structure arrays is a periodic structure array or a non-periodic structure array.

9. The blood sugar detection chip according to claim 1, characterized in that: The micro-nano unit includes a plurality of micro-nano structure arrays, and there is one or more groups of empty structures.

10. The blood sugar detection chip according to claim 1, characterized in that: The micro-nano unit has four-fold rotational symmetry.

11. The blood sugar detection chip according to claim 1, characterized in that: The light modulation layer is composed of one or more filter layers; The filter layer is a filter layer made of one or more of semiconductor materials, metal materials, liquid crystals, quantum dot materials, and perovskite materials; and / or, the filter layer is a filter layer made of one or more of photonic crystals, metasurfaces, random structures, nanostructures, metal surface plasmon SPP micro-nano structures, and adjustable Fabry-Perot resonant cavities.

12. The blood sugar detection chip according to claim 11, characterized in that: The semiconductor material includes one or more of silicon, silicon oxide, silicon nitride, titanium oxide, a composite material mixed in a preset proportion, and a direct bandgap compound semiconductor material; and / or, the nanostructure includes one or more of nanodot two-dimensional materials, nanocolumn two-dimensional materials, and nanowire two-dimensional materials.

13. The blood sugar detection chip according to claim 1, characterized in that: The thickness of the light modulation layer is 0.1λ~10λ, where λ represents the central wavelength of the incident light.

14. An intelligent blood glucose detector, characterized in that: Comprising a blood glucose detection chip as described in any one of claims 1 to 13.

15. A method for preparing a blood glucose detection chip according to any one of claims 1 to 13, characterized in that: include: Preparing a light modulation layer including a light modulation structure on the surface of the photosensitive area of ​​the image sensor; Generate a processor capable of fully connected signal processing and nonlinear activation processing; connecting the image sensor and the processor; The light modulation layer is used to perform different spectrum modulations on the incident light entering different positions of the light modulation structure through the light modulation structure, so as to obtain information carried by the incident light corresponding to different positions on the surface of the photosensitive area; The image sensor is used to convert the information carried by the incident light corresponding to different positions after being modulated by the light modulation layer into electrical signals corresponding to different positions, and send the electrical signals corresponding to different positions to the processor; the processor is used to perform full connection processing and nonlinear activation processing on the electrical signals corresponding to different positions to obtain blood sugar test results; The method further includes: a training process for the blood sugar detection chip, specifically including: Using input training samples and output training samples corresponding to the blood glucose detection task, blood glucose detection chips including different light modulation structures and image sensors and processors with different fully connected parameters and nonlinear activation parameters are trained to obtain light modulation structures, image sensors and processors that meet the training convergence conditions, and the light modulation structures, image sensors and processors that meet the training convergence conditions are used as the trained light modulation structures, image sensors and processors.

16. The method for preparing the blood sugar detection chip according to claim 15, characterized in that: A light modulation layer including a light modulation structure is prepared on the surface of the image sensor, comprising: Growing one or more layers of a preset material on the surface of the image sensor; Etching the one or more layers of the preset material into a light modulation structure pattern to obtain a light modulation layer including the light modulation structure; or performing embossing transfer on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure; or obtaining a light modulation layer including a light modulation structure by externally dynamically modulating the one or more layers of the preset material; or performing zone printing on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure; or performing partitioned growth on the one or more layers of the preset material to obtain a light modulation layer including a light modulation structure; Or quantum dots are transferred to the one or more layers of the preset materials to obtain a light modulation layer including a light modulation structure.

Images