Novel intelligent millimeter wave decoder based on single-layer metasurface

Abstract

The invention provides a novel intelligent millimeter wave decoder based on a single-layer metasurface, which comprises a compact single-layer metasurface array and an input control panel, the single-layer metasurface array is formed by arranging a plurality of square metasurface units, and the metasurface units have high transmission performance and 360-degree transmission phase regulation and control. The structure sequentially comprises a first metal layer, a dielectric substrate layer and a second metal layer from top to bottom, the first metal layer is located on the upper surface of the dielectric substrate, and the second metal layer is located on the lower surface of the dielectric substrate. The input control layer comprises six divided areas, input operation is carried out through selection of input signals, and decoding results of corresponding binary codes 000-111 can be obtained on an output observation face. The invention solves the defect that the traditional decoder cannot meet the requirements of low delay and low power consumption during high-frequency communication, overcomes the limitation of the traditional electronic device on speed, and has the characteristics of compactness, high speed, low delay and low power consumption.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic metamaterial design, and in particular to a novel intelligent millimeter wave decoder based on a single-layer metasurface. Background Art

[0002] With the advancement of the information age, the demand for high-performance computing is increasing. Traditional electronic computer components face a bottleneck, unable to break through the limitations of semiconductors due to the speed of carrier migration in traditional electronic devices. With the development of 5G / 6G communications, artificial intelligence, and high-performance computing, the processing speed requirements for electronic computer components are becoming increasingly higher. In particular, in high-frequency communications and large-scale data transmission scenarios, traditional electronic computer components struggle to meet ultra-low latency requirements. Furthermore, traditional electronic computer components often consume significant power, making them incapable of meeting the high-energy efficiency requirements of the future. Logical computation and logic conversion, as the core of computing and communication systems, play a vital role in information transmission throughout the system. Decoders, as core components in digital systems and communications, perform logic conversion functions during electronic computer computations, enabling reverse decoding of encoded information. Despite continuous technological advancements, today, they still face numerous technical bottlenecks and application challenges. In particular, in high-frequency communications in the millimeter-wave band, ultra-low latency requirements cannot be met, and the associated high energy consumption creates a significant bottleneck in mobile devices and IoT devices, limiting their application in high-speed computing. Therefore, research on intelligent hardware with high speed, low power consumption, and ultra-low latency is crucial for the future development of high-performance computing.

[0003] The development of optical diffraction neural networks and the gradual maturity of millimeter-wave technology have opened up the possibility of a new generation of computing systems based on diffraction neural networks. Electromagnetic metasurfaces, as two-dimensional artificial materials, achieve phase, amplitude, and polarization modulation through subwavelength unit structures. Furthermore, metasurfaces are passive devices that control electromagnetic waves solely through structural design, resulting in extremely low power consumption and suitable for energy-efficient scenarios. In recent years, the integration of metasurfaces with deep learning has achieved innovative results in various fields, such as optical computing and holographic imaging. Therefore, intelligent metasurface systems based on diffraction neural networks hold immense research value.

[0004] Millimeter waves correspond to the electromagnetic wave frequency band of 30-300 GHz, providing abundant frequency resources and supporting 5G / 6G to achieve multi-gigabit transmission rates. Millimeter waves, with their short wavelength and wide bandwidth, are a key technology for reducing system size and miniaturization. Millimeter wave technology has been widely applied in various fields, including millimeter wave antenna design, imaging systems, and radar sensing. Furthermore, in the field of high-frequency communications, millimeter waves offer high speeds, low latency, and strong anti-interference capabilities, making them a promising candidate for the next generation of computing and communication systems.

[0005] In light of the above, and to meet the demand for high-performance computing, this paper designs a novel decoding architecture for decoders in electronic components. Using a microwave diffraction neural network as the computational framework and an electromagnetic metasurface as the core computing device, a novel 3-bit binary codec was designed, implementing a novel decoding mechanism. This intelligent millimeter-wave decoder utilizes only a single-layer metasurface composed of 30×30 cells as the computing device, and performs decoding functions through manipulation of an input control panel. It features compactness, high speed, low power consumption, and low latency, providing a new approach for innovation in next-generation decoding architectures. Summary of the Invention

[0006] The purpose of the present invention is to propose a novel intelligent millimeter wave decoder based on a single-layer metasurface.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A novel intelligent millimeter wave decoder based on a single-layer metasurface is composed of a two-layer structure, including a first input layer and a second core computing device. The input layer is made of an absorbing material and includes a divided input signal selection area. The core computing device is a metasurface array formed by arranging square metasurface units. The metasurface array consists of a first metal layer (1), a dielectric substrate (2), and a second metal layer (3). The first metal layer (1) is located on the upper surface of the dielectric substrate (2), and the second metal layer (3) is located on the lower surface of the dielectric substrate (2). The decoder result output area is located on a plane parallel to the metasurface array at a specific distance behind the metasurface array.

[0009] Furthermore, the first input layer can be composed of an absorbing material or a metal plate that can reflect electromagnetic waves. The input layer is located at a distance of h1 from the metasurface array and in front of the metasurface array. The size of the input layer is comparable to that of the metasurface array and can be slightly larger than the metasurface array. The input layer is divided into six areas of equal size, which are arranged in two rows and three columns in the entire input layer. The two areas on the left represent the selection of the first digit of the three-digit binary number, and the selection of the upper area represents the operation number 0, and the selection of the lower area represents the operation number 1; the two middle areas represent the selection of the second digit of the three-digit binary number, and the selection of the upper area represents the operation number 0, and the selection of the lower area represents the operation number 1; the two areas on the right represent the selection of the third digit of the three-digit binary number, and the selection of the upper area represents the operation number 0, and the selection of the lower area represents the operation number 1.

[0010] Furthermore, the operation mode of the input layer signal is set as follows: when the electromagnetic wave input signal passes through a certain operation area in the input layer, it means that the area is selected, which is expressed as the binary number represented by the area; when the electromagnetic wave input signal does not pass through a specific area in the input layer, it means that the area is not selected.

[0011] Furthermore, the second-layer core computing device is composed of a metasurface array, which is composed of an arrangement of square metasurface units; the metasurface unit is composed of a first metal layer (1), a dielectric substrate (2), and a second metal layer (3); the first metal layer (1) of the metasurface unit is located on the upper surface of the dielectric substrate (2), and is composed of a long metal patch (101) located in the center of the dielectric substrate (2) and four short metal patches (102) located on both sides, and the short metal patches (101) are respectively located at the top and bottom of the upper surface of the dielectric substrate (2); the second metal layer (3) of the metasurface unit is located on the lower surface of the dielectric substrate (2), and is composed of two metal patches (301) located in the center of the dielectric substrate (2) and two metal patches (302) located on both sides, and the two metal patches (301) located in the center of the dielectric substrate (2) are respectively located at the top and bottom of the lower surface of the dielectric substrate (2).

[0012] Furthermore, the result output area is located on a plane behind the metasurface array at a distance h2 from the metasurface and parallel to the metasurface array. Four result areas are preset on the plane. The four result areas are close to the center of the plane and are distributed in two rows and two columns; the area in the first row and first column of the four areas represents a decoding result of 0 or 4, the area in the first row and second column represents a decoding result of 1 or 5, the area in the second row and first column represents a decoding result of 2 or 6, and the area in the second row and second column represents a decoding result of 3 or 7.

[0013] Furthermore, the thickness of the metasurface array is 1.5 mm, the dielectric substrate material of the metasurface array is F4B, the dielectric constant of the dielectric substrate is 2.2, and the loss tangent is 0.001.

[0014] Furthermore, the novel intelligent millimeter wave decoder based on the single-layer metasurface operates in the millimeter wave frequency band f=35 GHz, and the polarization is vertical polarization.

[0015] Furthermore, the size of the metasurface array in the novel intelligent decoder based on the single-layer metasurface is 144×144 mm. 2 The longitudinal dimension of the entire decoder system is 75.1 mm. The novel intelligent decoder can complete the decoding function of 3-bit binary numbers 000-111.

[0016] Principle of the present invention:

[0017] The intelligent decoder consists of a three-layer structure. The first layer is used as the input layer to select the signal. The middle layer is the metasurface as the core computing device, which controls the amplitude and phase of the input signal. The third output layer is the electromagnetic wave energy output surface, which is used to observe the encoding results. After the plane electromagnetic wave is selected by the input layer as the input signal of the entire decoding system, the signal undergoes a diffraction process in the first two layers and finally forms energy focus in the output layer. The decoding mechanism of the entire decoder can be perfectly mapped to a diffraction neural network with a three-layer structure. For this three-layer diffraction neural network, the input data is a specific electromagnetic wave, the hidden layer is the designed metasurface, and the output data is the target image. The constructed three-layer diffraction neural network is then trained to obtain the phase parameters of the metasurface and the system parameters of the decoding system. The propagation mode between layers is expressed as follows, where the propagation mode from the input layer to the hidden layer is:

[0018]

[0019] The propagation from the hidden layer to the output layer is:

[0020]

[0021] The weight between the input layer neurons and the hidden layer neurons is w1, and the weight between the hidden layer neurons and the output layer neurons is w2. h and b o The phase bias applied to the hidden layer and the output layer is set to 0 here. and are the electric field intensities reaching the input layer, hidden layer, and output layer, respectively. h1 is the distance from the input control panel to the metasurface in the designed decoding system, and h2 is the distance from the metasurface to the result output surface in the decoding system. Here, h1 and h2 will be used as training parameters of the diffraction neural network to participate in the training of the diffraction neural network. During the propagation process of the three-layer diffraction neural network, the weights w1 and w2 in the neural network are expressed as follows, where the weight between the input layer neurons and the hidden layer neurons is:

[0022]

[0023] The weights of the hidden layer neurons and the output layer neurons are:

[0024]

[0025] Where k is the wave number of the electromagnetic wave, is the position in the input layer (x j ,y j ,z j ) to the point at position (x1,y1,h1) in the hidden layer. is the characteristic coefficient of the hidden layer neuron, corresponding to the complex transmission coefficient of the metasurface unit. The complex transmission coefficient consists of two parts: amplitude α and phase φ. The complex transmission coefficient is expressed as:

[0026]

[0027] The constructed diffraction neural network model is fully trained to obtain the phase parameters used to construct the metasurface array and the system parameters used to construct the decoding system.

[0028] The beneficial effects of the present invention are:

[0029] The present invention proposes an intelligent millimeter-wave decoder composed only of a single-layer metasurface that can realize the decoding function of 3-bit binary code. Compared with traditional electronic decoders, this system breaks through the shortcomings of being unable to meet low latency and low power consumption in high-frequency communications, and overcomes the speed limitations of traditional electronic devices, and can achieve faster calculations. The system only uses a very compact metasurface as a computing device, which is in line with the future trend of miniaturization and high integration. The decoder proposed by the present invention adopts subwavelength modulation, low power consumption and passive design, and has the characteristics of compactness, high speed, low latency and low power consumption, which is in line with the development of high-performance computing in the 5G / 6G communication era. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0031] Figure 1 This is the design of the input mode of the intelligent millimeter wave decoder of the present invention.

[0032] Figure 2 This is a demonstration operation of the input mode of the intelligent millimeter wave decoder of the present invention.

[0033] Figure 3 This is the design of the output mode of the intelligent millimeter wave decoder of the present invention.

[0034] Figure 4 It is the phase distribution of the metasurface array obtained by training the intelligent millimeter wave decoder of the present invention.

[0035] Figure 5 It is a structural diagram of the metasurface unit of the intelligent millimeter wave decoder of the present invention.

[0036] Figure 6 1 is a diagram of the phase and transmission amplitude of the metasurface unit 1 of the intelligent millimeter wave decoder of the present invention.

[0037] Figure 7 1 is a diagram of the phase and transmission amplitude of the metasurface unit 2 of the intelligent millimeter wave decoder of the present invention.

[0038] Figure 8 This is a surface current diagram of the super-surface unit of the intelligent millimeter wave decoder of the present invention.

[0039] Figure 9 This is a structural diagram of the supersurface array constructed by the present invention.

[0040] Figure 10 It is a structural principle diagram of the intelligent millimeter wave decoder of the present invention.

[0041] Figure 11 This is a block diagram of the intelligent millimeter-wave decoder built in CST Microwave Studio 2019 of the present invention.

[0042] Figure 12 This is a diagram of the decoding results of the intelligent millimeter wave decoder of the present invention.

[0043] Figure 13 This is a diagram showing the energy ratio of the result area in the decoding result of the intelligent millimeter wave decoder of the present invention. DETAILED DESCRIPTION

[0044] The embodiments of the present invention are described in detail below with reference to the accompanying drawings and examples.

[0045] In order to make the objects, features and advantages of the present invention more clearly understood, please refer to the accompanying drawings.

[0046] In the present invention, the operating frequency of the new intelligent millimeter wave decoder based on the single-layer metasurface is 35 GHz.

[0047] The intelligent decoder consists of a three-layer structure. The first layer is used as the input layer to select the signal. The middle layer is the metasurface as the core computing device, which controls the amplitude and phase of the input signal. The third output layer is the electromagnetic wave energy output surface, which is used to observe the decoding results. After the plane electromagnetic wave is selected by the input layer as the input signal of the entire decoding system, the signal undergoes a diffraction process in the first two layers and finally forms energy focus in the output layer. The decoding mechanism of the entire decoder can be perfectly mapped to a diffraction neural network with a three-layer structure. For this three-layer diffraction neural network, the input data is a specific electromagnetic wave, the hidden layer is the designed metasurface, and the output data is the target image. The constructed three-layer diffraction neural network is then trained to obtain the phase parameters of the metasurface and the system parameters of the coding system. The propagation mode between layers is expressed as follows, where the propagation mode from the input layer to the hidden layer is:

[0048]

[0049] The propagation from the hidden layer to the output layer is:

[0050]

[0051] Furthermore, the weight between the input layer neurons and the hidden layer neurons is w1, and the weight between the hidden layer neurons and the output layer neurons is w2. h and b o The phase bias applied to the hidden layer and the output layer is set to 0 here. and are the electric field intensities reaching the input layer, hidden layer, and output layer, respectively. h1 is the distance from the input control panel to the metasurface in the designed decoder, and h2 is the distance from the metasurface to the result output surface in the decoder. Here, h1 and h2 will be used as training parameters of the diffractive neural network to participate in the training of the diffractive neural network. During the propagation process of the three-layer diffractive neural network, the weights w1 and w2 in the neural network are expressed as follows, where the weight between the input layer neurons and the hidden layer neurons is:

[0052]

[0053] The weights of the hidden layer neurons and the output layer neurons are:

[0054]

[0055] Where k is the wave number of the electromagnetic wave, is the position in the input layer (x j ,y j ,z j ) to the point at position (x1,y1,h1) in the hidden layer. is the characteristic coefficient of the hidden layer neuron, corresponding to the complex transmission coefficient of the metasurface unit. The complex transmission coefficient consists of two parts: amplitude α and phase φ. The complex transmission coefficient is expressed as:

[0056]

[0057] Furthermore, the constructed diffraction neural network model is fully trained to obtain the phase parameters used to construct the metasurface array and the system parameters used to construct the decoding system.

[0058] Specifically, the present invention proposes a decoding system that can realize the decoding function of any 3-bit binary number, and divides the input layer into 6 areas, such as Figure 1The two left-hand blocks represent the selection of the first digit of a three-digit binary number, representing the binary digits 0 and 1, respectively. The two middle blocks represent the selection of the second digit, representing the binary digits 0 and 1, respectively. Similarly, the two right-hand blocks represent the selection of the third digit. When the electromagnetic wave signal completely passes through a region in the input layer, that region is selected. Regions that the signal cannot pass through are not selected. The electromagnetic wave signal that passes through a region is represented by a matrix value of 1. Figure 2 The input operation of the designed input mode is demonstrated. Figure 3 As shown, four areas are preset in the output layer, each representing the result of decoding a 3-bit binary number. When a high peak energy focus appears in a certain area in the preset area, the decoding result is the value represented by the area.

[0059] Furthermore, after the input and output patterns are designed, the input data representing the specific input pattern and the output data representing the target result are provided to the constructed three-layer diffraction neural network for full training. After full training, the diffraction neural network tends to converge after 250 iterations. And the following is obtained: Figure 4 The phase parameters used to construct the metasurface and the system parameters h1 and h2 used to construct the decoding system are shown, where h1 = 5 mm, h2 = 70.1 mm.

[0060] Furthermore, the key to the physical realization of the optical decoding system lies in the design of the metasurface unit. The phase parameter data obtained through the diffraction neural network training requires that the phase of the designed metasurface unit needs to accurately cover 0-2π. In order to further make the metasurface more compact, the present invention sets the metasurface unit period p = 4.8mm. Since the metasurface unit period is too small, it is difficult to achieve a phase coverage of 0-2π by only controlling a single unit. The present invention achieves 360° phase coverage of the metasurface unit by combining and controlling the metasurface units.

[0061] Specifically, the designed metasurface unit is as follows Figure 5 As shown, the upper and lower surfaces of the metasurface are both copper metal patches with a thickness of 0.035mm. They are placed on a dielectric substrate with a side length of 4.8mm and a thickness of 1.5mm. The dielectric substrate has a dielectric constant of 2.2 and a loss tangent of 0.001. The entire metasurface unit is composed of two groups of metal patches. The first group of metal patches is located in the center of the metasurface unit. The length of the front metal patch is L1, and the length of the back metal patch is L1 / 2. The second group of metal patches is a set of bilaterally symmetrical metal patches. The length of the front metal patch is L2 / 2, and the length of the back metal patch is L2. Figure 6As shown in the figure, after optimizing the parameters of the metasurface unit, it was determined that when L2 = 1.45 mm, by adjusting L1 within the range of 0-4.8 mm, the unit phase can accurately cover all phases within the 360° phase range except the range of -20°–105°. Figure 7 As shown, when L1 = 4.8mm, which reaches the unit period limit, by adjusting L2 within the range of 0-1.65mm, the unit phase can cover the phase range of -20°–105°. Therefore, by adjusting L1 and L2 separately, the unit phase can fully cover the 360° phase range.

[0062] Specifically, Figure 8 The surface current distribution of the unit metal patch when L1 and L2 are respectively regulated for the unit. When L2 is determined, only by regulating L1, the surface current of the first metal layer (1) located on the upper surface of the metasurface and the surface current of the second metal layer (3) located on the lower surface of the metasurface are opposite, forming a current loop on the side of the unit and inducing a magnetic current perpendicular to the current. The interaction between the magnetic current and the surface current excites electromagnetic resonance, achieving the phase coverage of the first part and a high transmission amplitude. When L1 is determined, only by regulating L2, the surface current of the first metal layer (1) on the upper surface of the metasurface unit and the surface current of the second metal layer (3) located on the lower surface are opposite, and the induced magnetic current interacts with the current to excite electromagnetic resonance, achieving the phase coverage of the second part and a high transmission amplitude. Therefore, by separately regulating the metal patches of different lengths of the metasurface unit, different electromagnetic resonances are excited, achieving 360° full-range phase coverage and a high transmission amplitude close to 1.

[0063] Furthermore, the electromagnetic simulation software CST Microwave Studio 2019 was used to arrange the array, and the phase parameters obtained by diffraction neural network training were used to construct the following Figure 9 The metasurface array shown consists of 30×30 units and measures 144 mm. 2 The array thickness is 1.5mm, which has a very compact structure. The structural principle diagram of the new intelligent millimeter wave decoder designed by the present invention is as follows: Figure 10 As shown in the figure, the input operation control panel is made of absorbing material or metal plate, and is placed close to the decoding device metasurface, with a distance of only 5mm. The output surface is 70.1mm away from the metasurface, and the input signal is a plane electromagnetic wave with y polarization direction. According to the designed decoder framework diagram, the intelligent decoder was simulated in the electromagnetic simulation software CST Microwave Studio 2019. Figure 11 The construction shown in the figure was simulated and verified. The decoding results and energy proportion of the result area obtained by simulation are as follows: Figure 12 and Figure 13As shown, a total of 8 groups of binary codes within the 3-bit binary code 000-111 are used as input, and high-peak energy focusing is achieved in the corresponding result area of ​​the output surface. The energy intensity of the focus area is much greater than that of the rest of the area, forming a strong energy contrast, making the results clear and easy to identify.

[0064] Finally, it should be noted that the above embodiments are only intended to illustrate the technical solutions of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations may be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A novel intelligent millimeter wave decoder based on a single-layer metasurface, the decoder consisting of a two-layer structure, including a first input layer and a second core computing device, the input layer being made of an absorbing material and including a divided input signal selection area, the core computing device being a metasurface array formed by arranging square metasurface units; the metasurface array consisting of a first metal layer (1), a dielectric substrate (2), and a second metal layer (3), the first metal layer (1) being located on the upper surface of the dielectric substrate (2), and the second metal layer (3) being located on the lower surface of the dielectric substrate (2); and the decoder result output area being located on a plane parallel to the metasurface array at a specific distance behind the metasurface array.

2. According to claim 1, the first input layer can be composed of an absorbing material or a metal plate that can reflect electromagnetic waves. The input layer is located at a distance of h1 from the metasurface array and in front of the metasurface array. The size of the input layer is comparable to that of the metasurface array and can be slightly larger than the metasurface array; the input layer is divided into six areas of equal size, and is arranged in two rows and three columns in the entire input layer; the two areas on the left represent the selection of the first digit of a three-digit binary number, and the selection in the upper area represents the operation digit 0, and the selection in the lower area represents the operation digit 1; the two middle areas represent the selection of the second digit of the three-digit binary number, and the selection in the upper area represents the operation digit 0, and the selection in the lower area represents the operation digit 1; the two areas on the right represent the selection of the third digit of the three-digit binary number, and the selection in the upper area represents the operation digit 0, and the selection in the lower area represents the operation digit 1.

3. According to claim 2, the operation mode of the input layer signal is set as follows: when the electromagnetic wave input signal passes through a certain operation area in the input layer, it represents that the area is selected, which is expressed as the binary number represented by the area; when the electromagnetic wave input signal does not pass through a specific area in the input layer, it represents that the area is not selected.

4. According to claim 1, the second-layer core computing device is composed of a metasurface array, and the metasurface array is composed of an arrangement of square metasurface units; the metasurface unit is composed of a first metal layer (1), a dielectric substrate (2), and a second metal layer (3); the first metal layer (1) of the metasurface unit is located on the upper surface of the dielectric substrate (2), and is composed of a long metal patch (101) located in the center of the dielectric substrate and four short metal patches (102) located on both sides, and the short metal patches (102) are respectively located at the top and bottom of the upper surface of the dielectric substrate (2); the second metal layer (3) of the metasurface unit is located on the lower surface of the dielectric substrate (2), and is composed of two metal patches (301) located in the center of the dielectric substrate (2) and two metal patches (302) located on both sides, and the two metal patches (301) located in the center of the dielectric substrate (2) are respectively located at the top and bottom of the lower surface of the dielectric substrate (2).

5. According to claim 4, the length of the long metal patch (101) located at the center of the dielectric substrate (2) in the first metal layer (1) of the metasurface unit is L1, and the length of the two metal patches (301) located at the center of the dielectric substrate (2) in the second metal layer (3) is L1 / 2; the length of the four short metal patches (102) located on both sides of the dielectric substrate (2) in the first metal layer (1) of the metasurface unit is L2, and the length of the two metal patches (302) located on both sides of the dielectric substrate (2) in the second metal layer (3) is 2L2; the metasurface unit can achieve 360° transmission phase coverage by regulating L1 and L2.

6. According to claim 1, the result output area is located on a plane behind the metasurface array at a distance h2 from the metasurface and parallel to the metasurface array. Four result areas are preset on the plane. The four result areas are close to the center of the plane and are distributed in two rows and two columns. The area in the first row and first column of the four areas represents a decoding result of 0 or 4, the area in the first row and second column represents a decoding result of 1 or 5, the area in the second row and first column represents a decoding result of 2 or 6, and the area in the second row and second column represents a decoding result of 3 or 7.

7. According to claim 4, the thickness of the metasurface array is 1.5 mm, the dielectric substrate material of the metasurface array is F4B, the dielectric constant of the dielectric substrate is 2.2, and the loss tangent is 0.

001.

8. According to claim 1, the novel intelligent millimeter wave decoder based on a single-layer metasurface operates in the millimeter wave frequency band f = 35 GHz, and the polarization is vertical polarization.

9. According to claim 1, the size of the metasurface array in the novel intelligent millimeter wave decoder based on a single-layer metasurface is 144×144 mm 2 The longitudinal dimension of the entire decoder system is 75.1 mm. The novel intelligent decoder can complete the decoding function of 3-bit binary numbers 000-111.

Images