A method, system and device for field phase modulation of a micro- or nano-scale liquid crystal device

Abstract

This invention discloses a field phase modulation method, system, and device for micro / nano-scale liquid crystal devices, relating to the field modulation technology field. The field phase modulation method includes loading different electric field distributions onto the liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions; establishing a dataset based on the electric field distribution and phase distribution; training a convolutional neural network using the dataset to obtain an electric field distribution prediction model; inputting the target phase distribution into the electric field distribution prediction model to output a predicted electric field distribution; and loading the predicted electric field distribution onto the liquid crystal device to perform phase distribution modulation. Compared with existing phase modulation methods for liquid crystal devices, this invention introduces the concept of a field, overcoming the edge field effect of the liquid crystal device. The field phase modulation method is more efficient, and the phase distribution obtained through modulation has a higher degree of fit with the target phase distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of field modulation, in particular to a field phase modulation method, system and device of a micro-nano scale liquid crystal device. BACKGROUND

[0002] In recent years, liquid crystal devices have been applied in various fields such as display, AR / VR, optical communication, liquid crystal phased array, and light detection and ranging (LiDAR). Liquid crystal devices are the most ideal phase modulator for realizing complex phase distribution, freeform surface, etc. In order to improve the application of liquid crystal devices in the above fields, it is necessary to improve the performance of liquid crystal devices, such as wider viewing angle, faster response speed, higher resolution and more accurate phase modulation. In order to achieve the above conditions, a feasible way is to increase the overall size of the liquid crystal device, reduce the size of the pixel electrode or introduce a super surface to improve the phase modulation characteristics of the liquid crystal device.

[0003] However, due to the large number of pixel electrodes and the small size of the liquid crystal device, the mutual interference of the electric field between the pixels will occur during the modulation process, that is, the edge field effect. That is, the phase modulation depth on a single pixel electrode is actually determined by the electrode and several pixel electrodes around it. The potential of a single pixel electrode will not only affect the director of the liquid crystal molecules in the region corresponding to the single pixel electrode, but also affect the director of the liquid crystal molecules in the region corresponding to the adjacent pixel electrode. This causes uncertainty in phase modulation.

[0004] Therefore, it is necessary to design a field phase modulation method for a micro-nano scale liquid crystal device to overcome the edge field effect by introducing the concept of field. SUMMARY

[0005] The purpose of the present application is to provide a field phase modulation method, system and device for a micro-nano scale liquid crystal device to solve the problem of uneven phase distribution in the liquid crystal device caused by the existing phase modulation method.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] A field phase modulation method for a micro-nano scale liquid crystal device, the liquid crystal device comprising an upper glass substrate, a common electrode, an upper alignment layer, a liquid crystal layer, a lower alignment layer, a pixel electrode and a lower glass substrate arranged in order from top to bottom; the liquid crystal layer has a plurality of liquid crystal molecules; the method comprises:

[0008] loading different electric field distributions on the liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions; the electric field distribution is the voltage loaded on the pixel electrode, and the phase distribution is the deflection angle of the liquid crystal molecules.

[0009] A dataset is established based on the electric field distribution and the phase distribution;

[0010] A convolutional neural network is trained using the dataset to obtain an electric field distribution prediction model; the input of the electric field distribution prediction model is the phase distribution, and the output is the electric field distribution.

[0011] The target phase distribution is input into the electric field distribution prediction model, and the predicted electric field distribution is output.

[0012] The predicted electric field distribution is applied to the liquid crystal device for phase distribution modulation.

[0013] Optionally, the step of applying different electric field distributions to the liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions specifically includes:

[0014] By applying different electric field distributions to a liquid crystal device, phase depth information of the liquid crystal molecules can be obtained.

[0015] The phase depth information of any liquid crystal molecule is converted into transmittance; the phase depth information includes first phase depth information and second phase depth information.

[0016] The phase distribution is determined based on the transmittance.

[0017] Optionally, converting the phase depth information of any liquid crystal molecule into transmittance specifically includes:

[0018] The phase depth information of any liquid crystal molecule is converted into transmittance by orthogonal polarizers disposed on both the surface of the upper glass substrate and the surface of the lower glass substrate.

[0019] Optionally, determining the phase distribution based on the transmittance specifically includes:

[0020] The first phase depth information is determined based on the transmittance;

[0021] Determine the equivalent refractive index of the liquid crystal molecules;

[0022] The second phase depth information is determined based on the equivalent refractive index of the liquid crystal molecules;

[0023] The phase depth information is determined based on the first phase depth information and the second phase depth information;

[0024] Phase distribution is obtained by integrating the phase depth information of all liquid crystal molecules.

[0025] Optionally, the formula for calculating the transmittance is as follows:

[0026]

[0027] Where T is the transmittance, I is the intensity of the test light of the liquid crystal device, and I0 is the intensity of the emitted light of the liquid crystal device.

[0028] The formula for calculating the first phase depth information is as follows:

[0029]

[0030] in, For the first phase depth information, n and k are both constants;

[0031] The formula for calculating the equivalent refractive index of the liquid crystal molecules is as follows:

[0032]

[0033] Where, n eff n is the equivalent refractive index of the liquid crystal molecules. e For unusual refractive index, n o α is the ordinary refractive index, and α is the average deflection angle of the liquid crystal molecules under the electric field distribution.

[0034] The formula for calculating the second phase depth information is as follows:

[0035]

[0036] in, The second phase depth information is given by d, where d is the thickness of the liquid crystal layer and λ is the wavelength of the test light for the liquid crystal device.

[0037] This invention also provides a field phase modulation system for a micro / nano-scale liquid crystal device. The liquid crystal device includes, from top to bottom, an upper glass substrate, a common electrode, an upper alignment layer, a liquid crystal layer, a lower alignment layer, a pixel electrode, and a lower glass substrate; the liquid crystal layer contains a plurality of liquid crystal molecules; the system includes:

[0038] A phase distribution acquisition module is used to apply different electric field distributions to a liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions; the electric field distribution is the voltage applied to the pixel electrode, and the phase distribution is the deflection angle of the liquid crystal molecules;

[0039] A dataset creation module is used to create a dataset based on the electric field distribution and the phase distribution;

[0040] The electric field distribution prediction model acquisition module is used to train a convolutional neural network through the dataset to obtain an electric field distribution prediction model; the input of the electric field distribution prediction model is the phase distribution, and the output is the electric field distribution.

[0041] The predicted electric field distribution acquisition module is used to input the target phase distribution into the electric field distribution prediction model and output the predicted electric field distribution.

[0042] A phase distribution modulation module is used to apply the predicted electric field distribution onto the liquid crystal device for phase distribution modulation.

[0043] Optionally, the phase distribution acquisition module specifically includes:

[0044] The phase depth information acquisition unit is used to apply different electric field distributions onto the liquid crystal device to obtain the phase depth information of the liquid crystal molecules.

[0045] A transmittance conversion unit is used to convert the phase depth information of any liquid crystal molecule into transmittance; the phase depth information includes first phase depth information and second phase depth information.

[0046] A phase distribution determination unit is used to determine the phase distribution based on the transmittance.

[0047] Optionally, the phase distribution determination unit specifically includes:

[0048] The first phase depth information determination subunit is used to determine the first phase depth information based on the transmittance.

[0049] The equivalent refractive index determination subunit is used to determine the equivalent refractive index of liquid crystal molecules;

[0050] The second phase depth information determination subunit is used to determine the second phase depth information based on the equivalent refractive index of the liquid crystal molecules;

[0051] A phase depth information determination subunit is used to determine phase depth information based on the first phase depth information and the second phase depth information;

[0052] The phase distribution determination subunit is used to integrate the phase depth information of all liquid crystal molecules to obtain the phase distribution.

[0053] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the field phase modulation method of the micro-nano scale liquid crystal device.

[0054] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the field phase modulation method for the micro / nano-scale liquid crystal device.

[0055] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0056] This invention provides a field phase modulation method, system, and device for micro / nano-scale liquid crystal devices. Through repeated adjustments and iterations with different electric field distributions applied to the liquid crystal device, a dataset is established based on the electric field and phase distributions. A convolutional neural network is then trained using this dataset to obtain an electric field distribution prediction model, achieving overall electric field control of the micro / nano-scale liquid crystal device. The electric field distribution prediction model possesses a mapping relationship between the phase distribution and the electric field distribution of the liquid crystal device. Compared to existing phase modulation methods for liquid crystal devices, this invention introduces the concept of a field, overcoming the edge field effect of the liquid crystal device. The field phase modulation method of this invention is more efficient, and the obtained phase distribution has a higher degree of fit with the target phase distribution. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the edge field effect of the micro / nano-scale liquid crystal device provided by the present invention;

[0059] Figure 2 This is a schematic diagram of the structure of the micro-nano scale liquid crystal device provided in Embodiment 1 of the present invention;

[0060] Figure 3 This is a schematic cross-sectional view of the micro-nano scale liquid crystal device provided in Embodiment 1 of the present invention;

[0061] Figure 4 This is a flowchart illustrating the field phase modulation method for a micro / nano-scale liquid crystal device provided in Embodiment 1 of the present invention.

[0062] Figure 5 This is a schematic diagram of the structure of a convolutional neural network provided in Embodiment 1 of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] In existing technologies, for liquid crystal devices, the conventional phase modulation method involves adjusting the pixel electrodes of the liquid crystal device, i.e., applying different voltages. Each adjustment of the pixel electrodes induces a different phase distribution according to the different electric fields, and then the phase distribution under the electric field distribution is judged to see if it is the target phase distribution. For example... Figure 1 As shown, due to the edge field effect that occurs inside the liquid crystal device during single-electrode driving, the phase distribution obtained after a finite number of tuning iterations cannot perfectly fit the target phase distribution. This means that a sufficient number of tuning iterations and sufficiently high modulation accuracy are required to achieve a better fit of the liquid crystal device's phase distribution, but at the same time, this also means that the modulation difficulty will increase accordingly.

[0065] The purpose of this invention is to provide a field phase modulation method, system, and device for micro / nano-scale liquid crystal devices. By learning the phase distribution driven by the electric field distribution of the entire liquid crystal device, the edge field effect caused by the small size of the liquid crystal device is considered and overcome during the modulation process, thus solving the problem of non-uniform phase distribution inside the liquid crystal device caused by existing phase modulation methods.

[0066] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] Example 1

[0068] The field phase modulation method for micro / nano-scale liquid crystal devices provided by this invention is applicable to liquid crystal devices. For example... Figure 2 As shown, the liquid crystal device includes, from top to bottom, an upper glass substrate, a common electrode, an upper alignment layer, a liquid crystal layer, a lower alignment layer, a pixel electrode, and a lower glass substrate (or silicon-based chip); the liquid crystal layer contains multiple liquid crystal molecules. The liquid crystal device is a two-dimensional liquid crystal device. The liquid crystal device can be a transmissive liquid crystal device, a silicon-based liquid crystal device, or a metasurface liquid crystal device; the common electrode and the pixel electrode should be perpendicular to each other, and both the common electrode and the pixel electrode are ITO driving electrodes.

[0069] like Figure 3The diagram shows a cross-sectional view of a liquid crystal device. Its components include a substrate, typically made of glass, but also transparent materials such as PET; the common electrode material is generally a transparent conductive metal oxide, such as indium tin oxide (ITO), or a transparent conductive organic polymer material, such as polyethylene dioxythiophene (PEDOT), or an inorganic material, such as metal mesh, silver nanowires, graphene, and carbon nanotubes. The upper and lower alignment layers can be aligned by friction or by ultraviolet light exposure. The liquid crystal layer can be made of any liquid crystal material. The pixel electrode material can be aluminum, zinc oxide, or titanium dioxide nanopillars, etc.

[0070] like Figure 4 As shown, the field phase modulation method for micro / nano-scale liquid crystal devices provided by this invention specifically includes:

[0071] Step 101: Apply different electric field distributions to the liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions. The electric field distribution is the voltage applied to the pixel electrode. The distribution of the voltage of the pixel electrode is a two-dimensional matrix, so the electric field distribution is a two-dimensional matrix A, where A[i][j] represents the voltage applied to the pixel electrode in the i-th row and m-th column; the phase distribution is the phase delay distribution of the entire liquid crystal layer of the liquid crystal device. The electric field formed by the voltage applied to each pixel electrode induces the deflection of liquid crystal molecules in the liquid crystal layer, forming a phase difference, which causes a non-uniform phase distribution inside the liquid crystal device. That is, the phase distribution is the deflection angle of the liquid crystal molecules, and the phase distribution is also a two-dimensional matrix B, where B[i][j] represents the phase depth information of the sampling point in the i-th row and j-th column.

[0072] Furthermore, step 101 specifically includes:

[0073] Step 1011: Apply different electric field distributions to the liquid crystal device to obtain the phase depth information of the liquid crystal molecules.

[0074] Step 1012: Convert the phase depth information of any liquid crystal molecule into transmittance; the phase depth information includes first phase depth information and second phase depth information.

[0075] Since the phase distribution of a liquid crystal device cannot be directly quantified, orthogonal polarizers are used on both the surface of the upper and lower glass substrates to convert the phase depth information of any liquid crystal molecule into transmittance. Test light passes through the liquid crystal device to generate an image of the illuminance distribution. A CCD camera then captures the emitted light from the liquid crystal device to obtain a grayscale signal, which can be converted into transmittance. The formula for calculating transmittance is as follows:

[0076]

[0077] Where T is the transmittance, I is the intensity of the test light of the liquid crystal device, and I0 is the intensity of the emitted light of the liquid crystal device.

[0078] Step 1013: Determine the phase distribution based on transmittance.

[0079] Furthermore, step 1013 specifically includes:

[0080] Step 10131: Determine the first phase depth information based on the transmittance. The formula for calculating the first phase depth information is as follows:

[0081]

[0082] in, This represents the first phase depth information, where n and k are both constants.

[0083] Step 10132: Determine the equivalent refractive index of the liquid crystal molecules. The formula for calculating the equivalent refractive index of liquid crystal molecules is as follows:

[0084]

[0085] Where, n eff n is the equivalent refractive index of the liquid crystal molecules. e For unusual refractive index, n o Let α be the ordinary refractive index, and α be the average deflection angle of the liquid crystal molecules under the electric field distribution.

[0086] Step 10133: Determine the second phase depth information based on the equivalent refractive index of the liquid crystal molecules. The formula for calculating the second phase depth information is as follows:

[0087]

[0088] in, The second phase depth information is given by d, where d is the thickness of the liquid crystal layer and λ is the wavelength of the test light for the liquid crystal device.

[0089] Step 10134: Determine the phase depth information based on the first phase depth information and the second phase depth information. The range of the phase depth information is obtained using the first phase depth information, and the precise value of the phase depth information is obtained using the second phase depth information.

[0090] Step 10135: Integrate the phase depth information of all liquid crystal molecules to obtain the phase distribution.

[0091] Step 102: Establish a dataset based on the electric field distribution and phase distribution. Since different pixel electrode voltage combinations are applied to the liquid crystal device during each debugging step in Step 101, each electric field distribution will drive different deflection angles of liquid crystal molecules in different regions, thus forming different phase distributions. Therefore, the established dataset includes the electric field distribution and its corresponding phase distribution.

[0092] Step 103: Train a convolutional neural network using the dataset to obtain an electric field distribution prediction model; the input of the electric field distribution prediction model is the phase distribution, and the output is the electric field distribution. For example... Figure 5 As shown, the convolutional neural network consists of an input layer, multiple convolutional layers, multiple pooling layers, multiple normalization layers, a fully connected layer, and an output layer. The phase distribution of the liquid crystal device is input through the input layer, then convolutional operations are performed through the convolutional layers, and finally the electric field distribution of the liquid crystal device is output through the output layer. The Adam optimizer is used when training the convolutional neural network. The electric field distribution prediction model reflects the mapping relationship between the electric field distribution and the phase distribution of the liquid crystal device. Corresponding to the liquid crystal device, the convolutional layer is a convolutional layer. The pooling layer can be either an average pooling layer or a max pooling layer.

[0093] The process of training a convolutional neural network using a dataset includes determining the accuracy and loss value of the electric field distribution prediction model.

[0094] The formula for calculating accuracy is as follows:

[0095]

[0096] Where A is an m*n two-dimensional matrix of electric field distribution, and B is an m*n two-dimensional matrix of electric field distribution output during the training process. ij Let B be the voltage value applied to the pixel electrode in the i-th row and j-th column of the electric field distribution. ij This represents the voltage value applied to the pixel electrode in the i-th row and j-th column of the electric field distribution output during the training process.

[0097] The formula for calculating the loss value is as follows:

[0098]

[0099] Where m is the number of rows in the m*n two-dimensional matrix, n is the number of columns in the m*n two-dimensional matrix, m×n is the size of the m*n two-dimensional matrix, and A ij Let B be the voltage value applied to the pixel electrode in the i-th row and j-th column of the electric field distribution. ij This represents the voltage value applied to the pixel electrode in the i-th row and j-th column of the electric field distribution output during the training process.

[0100] The learning performance and training effect of the constructed electric field distribution prediction model can be improved by adjusting the hyperparameters, including activation function, batch size, convolution kernel size and learning rate.

[0101] Step 104: Input the target phase distribution into the electric field distribution prediction model and output the predicted electric field distribution. The learned electric field distribution prediction model achieves the prediction task of regressing the electric field distribution required to load the liquid crystal device for the target phase. The phase distribution of the liquid crystal layer driven by this electric field distribution is the target phase distribution. Furthermore, the electric field distribution prediction model is used to achieve electric field modulation based on the electric field distribution.

[0102] Step 105: Apply the predicted electric field distribution to the liquid crystal device for phase distribution modulation.

[0103] This invention accumulates a dataset through multiple iterations of debugging. Each iteration applies different voltage combinations to the pixel electrodes of the liquid crystal device, creating different electric field distributions within the device. These different electric field distributions induce varying deflection angles in different regions of the liquid crystal molecules, resulting in different phase delay distributions. After accumulating a sufficient dataset, a convolutional neural network is trained. This trained electric field distribution prediction model then predicts the electric field distribution required by the device under the target phase distribution. The predicted electric field distribution is then applied to the liquid crystal device, inducing the deflection of liquid crystal molecules in the liquid crystal layer to form a new phase distribution. This new phase distribution is either the target phase distribution or a highly similar distribution.

[0104] Example 2

[0105] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a field phase modulation system for micro-nano scale liquid crystal devices is provided below.

[0106] The system includes:

[0107] The phase distribution acquisition module is used to apply different electric field distributions to the liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions; the electric field distribution is the voltage applied to the pixel electrode, and the phase distribution is the deflection angle of the liquid crystal molecules.

[0108] The dataset creation module is used to create datasets based on electric field distribution and phase distribution.

[0109] The electric field distribution prediction model acquisition module is used to train a convolutional neural network through a dataset to obtain an electric field distribution prediction model. The input of the electric field distribution prediction model is the phase distribution, and the output is the electric field distribution.

[0110] The electric field distribution prediction module is used to input the target phase distribution into the electric field distribution prediction model and output the predicted electric field distribution.

[0111] The phase distribution modulation module is used to apply the predicted electric field distribution onto the liquid crystal device for phase distribution modulation.

[0112] Furthermore, the phase distribution acquisition module specifically includes:

[0113] The phase depth information acquisition unit is used to apply different electric field distributions to the liquid crystal device to obtain the phase depth information of the liquid crystal molecules.

[0114] The transmittance conversion unit is used to convert the phase depth information of any liquid crystal molecule into transmittance; the phase depth information includes first phase depth information and second phase depth information.

[0115] Phase distribution determination unit, used to determine the phase distribution based on transmittance.

[0116] Furthermore, the phase distribution determination unit specifically includes:

[0117] The first phase depth information determination sub-unit is used to determine the first phase depth information based on the transmittance.

[0118] The equivalent refractive index determination subunit is used to determine the equivalent refractive index of liquid crystal molecules.

[0119] The second phase depth information determination subunit is used to determine the second phase depth information based on the equivalent refractive index of the liquid crystal molecules.

[0120] The phase depth information determination subunit is used to determine phase depth information based on the first phase depth information and the second phase depth information.

[0121] The phase distribution determination subunit is used to integrate the phase depth information of all liquid crystal molecules to obtain the phase distribution.

[0122] Example 3

[0123] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the field phase modulation method of the micro-nano scale liquid crystal device of Embodiment 1.

[0124] The aforementioned electronic device may be a server.

[0125] Example 4

[0126] Embodiment 4 of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the field phase modulation method for micro-nano scale liquid crystal devices of Embodiment 1.

[0127] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0128] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the methods, systems, and devices of the present invention and their core ideas. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A field phase modulation method for a micro / nano-scale liquid crystal device, the liquid crystal device comprising, from top to bottom, an upper glass substrate, a common electrode, an upper alignment layer, a liquid crystal layer, a lower alignment layer, a pixel electrode, and a lower glass substrate; wherein the liquid crystal layer contains a plurality of liquid crystal molecules; characterized in that, The method includes: Different electric field distributions are applied to the liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions; the electric field distribution is the voltage applied to the pixel electrode, and the phase distribution is the deflection angle of the liquid crystal molecules; A dataset is established based on the electric field distribution and the phase distribution; A convolutional neural network is trained using the dataset to obtain an electric field distribution prediction model; the input of the electric field distribution prediction model is the phase distribution, and the output is the electric field distribution. The target phase distribution is input into the electric field distribution prediction model, and the predicted electric field distribution is output. The predicted electric field distribution is applied to the liquid crystal device for phase distribution modulation.

2. The field phase modulation method for micro / nano-scale liquid crystal devices according to claim 1, characterized in that, The step of applying different electric field distributions to the liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions specifically includes: By applying different electric field distributions to a liquid crystal device, phase depth information of the liquid crystal molecules can be obtained. The phase depth information of any liquid crystal molecule is converted into transmittance; the phase depth information includes first phase depth information and second phase depth information. The phase distribution is determined based on the transmittance.

3. The field phase modulation method for micro / nano-scale liquid crystal devices according to claim 2, characterized in that, The process of converting the phase depth information of any liquid crystal molecule into transmittance specifically includes: The phase depth information of any liquid crystal molecule is converted into transmittance by orthogonal polarizers disposed on both the surface of the upper glass substrate and the surface of the lower glass substrate.

4. The field phase modulation method for micro / nano-scale liquid crystal devices according to claim 2, characterized in that, Determining the phase distribution based on the transmittance specifically includes: The first phase depth information is determined based on the transmittance; the formula for calculating the transmittance is as follows: in, T For transmittance, I To test the light intensity for liquid crystal devices. I 0 represents the intensity of the light emitted from the liquid crystal device; The formula for calculating the first phase depth information is as follows: in, For the first phase depth information, n and k are both constants; The equivalent refractive index of the liquid crystal molecules is determined; the formula for calculating the equivalent refractive index of the liquid crystal molecules is as follows: in, The equivalent refractive index of the liquid crystal molecules is given. It is an unusual refractive index. For ordinary light refractive index, The average deflection angle of the liquid crystal molecules under the electric field distribution; The second phase depth information is determined based on the equivalent refractive index of the liquid crystal molecules; the formula for calculating the second phase depth information is as follows: in, This refers to the second phase depth information. d The thickness of the liquid crystal layer, To test the wavelength of light for liquid crystal devices; Phase depth information is determined based on the first phase depth information and the second phase depth information; the range of phase depth information is obtained through the first phase depth information, and the precise value of phase depth information is obtained through the second phase depth information; Phase distribution is obtained by integrating the phase depth information of all liquid crystal molecules.

5. A field phase modulation system for a micro / nano-scale liquid crystal device, the liquid crystal device comprising, from top to bottom, an upper glass substrate, a common electrode, an upper alignment layer, a liquid crystal layer, a lower alignment layer, a pixel electrode, and a lower glass substrate; The liquid crystal layer contains a plurality of liquid crystal molecules; characterized in that... The system includes: A phase distribution acquisition module is used to apply different electric field distributions to a liquid crystal device to obtain the phase distribution of the liquid crystal device under different electric field distributions; the electric field distribution is the voltage applied to the pixel electrode, and the phase distribution is the deflection angle of the liquid crystal molecules; A dataset creation module is used to create a dataset based on the electric field distribution and the phase distribution; The electric field distribution prediction model acquisition module is used to train a convolutional neural network through the dataset to obtain an electric field distribution prediction model; the input of the electric field distribution prediction model is the phase distribution, and the output is the electric field distribution. The predicted electric field distribution acquisition module is used to input the target phase distribution into the electric field distribution prediction model and output the predicted electric field distribution. A phase distribution modulation module is used to apply the predicted electric field distribution onto the liquid crystal device for phase distribution modulation.

6. The field phase modulation system for a micro / nano-scale liquid crystal device according to claim 5, characterized in that, The phase distribution acquisition module specifically includes: The phase depth information acquisition unit is used to apply different electric field distributions onto the liquid crystal device to obtain the phase depth information of the liquid crystal molecules. A transmittance conversion unit is used to convert the phase depth information of any liquid crystal molecule into transmittance; the phase depth information includes first phase depth information and second phase depth information. A phase distribution determination unit is used to determine the phase distribution based on the transmittance.

7. The field phase modulation system for a micro / nano-scale liquid crystal device according to claim 6, characterized in that, The phase distribution determination unit specifically includes: The first phase depth information determination subunit is used to determine the first phase depth information based on the transmittance; the formula for calculating the transmittance is as follows: in, T For transmittance, I To test the light intensity for liquid crystal devices. I 0 represents the intensity of the light emitted from the liquid crystal device; The formula for calculating the first phase depth information is as follows: in, For the first phase depth information, n and k are both constants; An equivalent refractive index determination subunit is used to determine the equivalent refractive index of liquid crystal molecules; the formula for calculating the equivalent refractive index of the liquid crystal molecules is as follows: in, The equivalent refractive index of the liquid crystal molecules is given. It is an unusual refractive index. For ordinary light refractive index, The average deflection angle of the liquid crystal molecules under the electric field distribution; The second phase depth information determination subunit is used to determine the second phase depth information based on the equivalent refractive index of the liquid crystal molecules; the calculation formula for the second phase depth information is as follows: in, This refers to the second phase depth information. d The thickness of the liquid crystal layer, To test the wavelength of light for liquid crystal devices; A phase depth information determination subunit is used to determine phase depth information based on the first phase depth information and the second phase depth information; obtain the range of phase depth information through the first phase depth information, and obtain the precise value of phase depth information through the second phase depth information. The phase distribution determination subunit is used to integrate the phase depth information of all liquid crystal molecules to obtain the phase distribution.

8. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the field phase modulation method of the micro-nano scale liquid crystal device according to any one of claims 1-4.

9. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the field phase modulation method for the micro-nano scale liquid crystal device as described in any one of claims 1-4.

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