JP2020005473A - Evaluation device for solar cell and evaluation method for solar cell - Google Patents

Abstract

To provide an evaluation device which easily evaluates a power generation performance of a solar cell.SOLUTION: An evaluation device for a solar cell according to one embodiment of the present invention comprises an image processing part that evaluates a power generation performance of the solar cell on the basis of at least one image selected from a group formed by a first mage of the solar cell imaged using a transmitted light and a second image of a precursor of the solar cell imaged using the transmitted light. An image processing part performs at least one selected from a group comprised of, for example, (i)an evaluation of the power generation performance of the solar cell by processing the first image with a first learning model, and (ii) an evaluation of the power generation performance of the solar cell by processing the second image with a second learning model.SELECTED DRAWING: Figure 1

Description

  The present disclosure relates to a solar cell evaluation device and a solar cell evaluation method.

In recent years, research and development of perovskite solar cells have been promoted as an example of a coated solar cell. “Coating type solar cell” means a solar cell including a light absorbing layer manufactured using a coating solution. The perovskite solar cell includes a perovskite-type crystal represented by ABX ₃ (A is a monovalent cation, B is a divalent cation, and X is a halogen anion) as a light absorbing material, and a similar structure thereof (hereinafter referred to as a “perovskite compound”). "). The light absorbing layer of the perovskite solar cell is produced using, for example, a coating solution containing a light absorbing material.

In Non-Patent Document 1, (Cs, CH ₃ NH ₃ , HC (NH ₂ ) ₂ ) PbI ₃ (hereinafter, “(Cs, MA, FA) PbI ₃ ” is abbreviated as a light absorbing material of a perovskite solar cell. Solar cells using a perovskite compound represented by the formula:

  Patent Literature 1 discloses an inspection apparatus that generates an image of a thin film by capturing an image of a solar cell panel in which a thin film is stacked on a substrate and analyzing color unevenness of the obtained image. I have.

JP-A-2004-95731

Michael Saliba, 10 others, “Incorporation of rubidium cases into perovskites solar cells improves photovoltaic performance”, Science, U.S.A., Vol. 206-209

  There is a need for a technique for easily evaluating the power generation performance of a solar cell.

  Certain non-limiting exemplary aspects of the present disclosure provide an evaluation device for easily evaluating the power generation performance of a solar cell.

One aspect of the present disclosure is:
A first image of the solar cell imaged using transmitted light, and at least one image selected from a group consisting of a second image of the precursor of the solar cell imaged using transmitted light, Provided is a solar cell evaluation device including an image processing unit for evaluating the power generation performance of a solar cell.

  Comprehensive or specific aspects may be implemented with elements, devices, modules, systems, integrated circuits, methods, or computer programs. Comprehensive or specific aspects may be implemented by any combination of elements, devices, modules, systems, integrated circuits, methods, and computer programs.

  Additional advantages and advantages of the disclosed embodiments will be apparent from the description and drawings. Benefits and / or advantages are provided by the various embodiments or features disclosed in the specification and drawings, and not all are required to achieve one or more of these.

  According to one embodiment of the present disclosure, it is possible to provide an evaluation device that easily evaluates the power generation performance of a solar cell.

FIG. 1 is a schematic configuration diagram illustrating an example of a solar cell evaluation device according to the present disclosure. FIG. 2 is a diagram for explaining a learning process and an output process by the image processing unit of the evaluation device in FIG. FIG. 3 is a flowchart showing output processing by the image processing unit of the evaluation device in FIG. FIG. 4 is a diagram for explaining the arithmetic processing by the arithmetic module of the image processing unit of the evaluation device of FIG. FIG. 5 is a schematic sectional view showing an example of a solar cell to be evaluated by the evaluation device of FIG. FIG. 6 is a schematic cross-sectional view showing an example of a solar cell precursor to be evaluated by the evaluation device of FIG. FIG. 7 is a schematic configuration diagram illustrating another example of the solar cell evaluation device of the present disclosure. FIG. 8 is a graph showing a relationship between a predicted value of the conversion efficiency of the solar cell evaluated in the example and an actually measured value of the conversion efficiency of the solar cell. FIG. 9 is an image of a solar cell in which the predicted value of the conversion efficiency was 13% or more in the example. FIG. 10 is an image of a solar cell in which the predicted value of the conversion efficiency was 5% or less in the example.

<Knowledge underlying the present disclosure>
The findings on which the present disclosure is based are as follows.

  Even when a plurality of coated solar cells are manufactured under the same conditions, the power generation performance of the plurality of coated solar cells may be significantly different from each other. Regarding the large difference between the power generation performances of the plurality of coating type solar cells, for example, the following causes can be considered. In a coating type solar cell, the light absorption layer is formed by a coating method. Depending on the drying conditions in the coating method, how the coating solution applied by the coating method spreads on the substrate, etc., the bias of the crystal of the light absorbing material in the light absorbing layer, or the bias of the film thickness of the light absorbing layer. May occur. In a plurality of coating type solar cells, the bias of the crystal of the light absorbing material in the light absorbing layer or the bias of the film thickness of the light absorbing layer may be different from each other. At this time, it is considered that the power generation performance of the plurality of coating type solar cells is different from each other. In a coating type solar cell, an additive or an impurity phase may segregate in a light absorption layer. The unevenness of the crystal of the light absorbing material in the light absorbing layer, the unevenness of the film thickness of the light absorbing layer, the segregation of the additive or impurity phase in the light absorbing layer, and the like appear as uneven color of the light absorbing layer. Therefore, if the power generation performance of the coating type solar cell can be easily evaluated based on the color unevenness of the light absorbing layer, the technique is useful.

  In a conventional inspection apparatus for a solar cell panel, the illuminator is arranged on the same side of the solar cell panel as the side where the imaging unit is located, as in the apparatus described in Patent Document 1. Therefore, light emitted from the illuminator is reflected on the surface of the solar cell panel and enters the imaging unit. Only the information on the surface state of the solar cell panel is reflected on the image of the solar cell panel captured using the reflected light. That is, it is not possible to obtain information on the inside of the light absorption layer inside the solar cell from the image of the solar cell panel taken using the reflected light. Therefore, it is difficult for the conventional inspection apparatus to evaluate the power generation performance of the solar cell panel with high accuracy.

<Overview of one embodiment according to the present disclosure>
The solar cell evaluation device according to the first aspect of the present disclosure includes:
A first image of the solar cell imaged using transmitted light, and at least one image selected from a group consisting of a second image of the precursor of the solar cell imaged using transmitted light, It has an image processing unit for evaluating the power generation performance of the solar cell.

  According to the first aspect, the image processing unit evaluates the power generation performance of the solar cell with the first image of the solar cell imaged using the transmitted light and the solar cell imaged using the transmitted light. At least one image selected from the group consisting of the second image of the precursor is used. Therefore, according to the image processing unit, the power generation performance of the solar cell can be evaluated based on information inside the solar cell or the precursor of the solar cell. Thereby, the evaluation device of the first aspect can evaluate the power generation performance of the solar cell simply and with high accuracy.

  In the second aspect of the present disclosure, for example, in the solar cell evaluation device according to the first aspect, the image processing unit (i) processes the first image with a first learning model to obtain the solar image. Evaluating the power generation performance of the battery, and (ii) evaluating the power generation performance of the solar cell by processing the second image with a second learning model, at least one selected from the group consisting of: The first learning model shows the relationship between the image of the learning model solar cell imaged using transmitted light and the power generation performance of the learning model solar cell, and the second learning model expresses the transmitted light. The relationship between the image of the precursor of the learning model solar cell captured using the method and the power generation performance of the learning model solar cell may be shown. The solar cell evaluation device of the second aspect can evaluate the power generation performance of the solar cell simply and with high accuracy.

  In a third aspect of the present disclosure, for example, in the solar cell evaluation device according to the second aspect, the image processing unit includes the image of the learning model solar cell and the power generation performance of the learning model solar cell. The first learning model may be generated by learning first learning data associated with. The solar cell evaluation device according to the third aspect can easily and highly accurately evaluate the power generation performance of the solar cell.

  In a fourth aspect of the present disclosure, for example, in the solar cell evaluation device according to the second aspect, the image processing unit includes the image of the precursor of the learning model solar cell and the learning model solar cell. The second learning model may be generated by learning second learning data associated with the power generation performance. The solar cell evaluation device of the fourth aspect can evaluate the power generation performance of the solar cell simply and with high accuracy.

  In a fifth aspect of the present disclosure, for example, in the solar cell evaluation device according to any one of the second to fourth aspects, the image processing unit uses a convolutional neural network to generate the first learning model. And at least one learning model selected from the group consisting of the second learning model. The solar cell evaluation device of the fifth aspect can evaluate the power generation performance of a solar cell simply and with high accuracy.

  In the sixth aspect of the present disclosure, for example, the solar cell evaluation device according to any one of the first to fifth aspects further includes an imaging unit that images the solar cell or the precursor of the solar cell. You may have. According to the sixth aspect, the first image or the second image can be acquired by the imaging unit.

  In the seventh aspect of the present disclosure, for example, the solar cell evaluation device according to the sixth aspect, when imaging the solar cell or the precursor of the solar cell by the imaging unit, the solar cell or the The imaging device may further include an illumination unit that is located on a side opposite to the imaging unit with respect to the precursor of the solar cell and irradiates light to the solar cell or the precursor of the solar cell. The solar cell evaluation device according to the seventh aspect can acquire the first image or the second image.

  In an eighth aspect of the present disclosure, for example, the solar cell evaluation device according to any one of the first to seventh aspects includes information on the power generation performance of the solar cell evaluated by the image processing unit. An output unit for outputting may be further provided. The solar cell evaluation device according to the eighth aspect can evaluate the power generation performance of the solar cell simply and with high accuracy.

  In a ninth aspect of the present disclosure, for example, the solar cell evaluation device according to any one of the first to eighth aspects includes a learning model solar cell image captured using transmitted light and the learning model solar cell. First learning data associated with the power generation performance of the model solar cell, and an image of the precursor of the learning model solar cell captured using transmitted light and the power generation performance of the learning model solar cell May be further provided with a storage unit for storing at least one learning data selected from the group consisting of the second learning data associated with. The solar cell evaluation device of the ninth aspect can easily and highly accurately evaluate the power generation performance of the solar cell.

  In a tenth aspect of the present disclosure, for example, in the solar cell evaluation device according to any one of the first to ninth aspects, the solar cell and the precursor of the solar cell include a light containing a perovskite compound. An absorbing layer may be provided. The solar cell evaluation device of the tenth aspect can evaluate the power generation performance of a solar cell simply and with high accuracy.

  In an eleventh aspect of the present disclosure, for example, in the solar cell evaluation device according to any one of the first to tenth aspects, the power generation performance of the solar cell includes conversion efficiency, short-circuit current density, and open-circuit voltage. , A fill factor, a series resistance, a sheet resistance, a hysteresis, a current-voltage curve, and a change with time thereof. The solar cell evaluation device of the eleventh aspect can evaluate the power generation performance of the solar cell simply and with high accuracy.

A method for evaluating a solar cell according to a twelfth aspect of the present disclosure includes:
A first image of the solar cell imaged using transmitted light, and at least one image selected from a group consisting of a second image of the precursor of the solar cell imaged using transmitted light, This includes evaluating the power generation performance of the solar cell.

  According to the twelfth aspect, the power generation performance of the solar cell can be evaluated based on information inside the solar cell or the precursor of the solar cell. According to the evaluation method of the twelfth aspect, the power generation performance of the solar cell can be evaluated simply and with high accuracy.

<Embodiments of the present disclosure>
Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. The following embodiment is an example, and the present disclosure is not limited to the following embodiment.

[Embodiment 1]
(Solar cell evaluation device)
As shown in FIG. 1, the solar cell evaluation device 100 of the present embodiment includes an image processing unit 20. The image processing unit 20 includes at least one image selected from the group consisting of a first image of the solar cell captured using transmitted light and a second image of a precursor of the solar cell captured using transmitted light. And evaluate the power generation performance of the solar cell. The solar cell and the precursor include a light absorbing layer containing a perovskite compound. In detail, the solar cell includes a first electrode, a second electrode facing the first electrode, and a light absorbing layer located between the first electrode and the second electrode and containing a perovskite compound. Means something. Each of the first electrode and the second electrode has a region through which light is transmitted. The term "region through which light is transmitted" means a region through which 10% or more of light is transmitted at any wavelength among light having a wavelength of 200 to 2000 nm. The light absorbing layer has, for example, a light-transmitting property. The term “having translucency” means that 10% or more of light having a wavelength of 200 to 2000 nm is transmitted at any wavelength. The band gap of the perovskite compound contained in the light absorbing layer is, for example, 1.1 eV or more. Therefore, the light absorption layer has a property of transmitting red light, for example. As described later, the solar cell may further include an electron transport layer, a porous layer, and a hole transport layer. Each of the electron transport layer, the porous layer, and the hole transport layer has, for example, a light-transmitting property. The precursor of the solar cell means a material including a first electrode and a light absorption layer that is located on the first electrode and contains a perovskite compound. The precursor includes only the first electrode as an electrode. Each of the first image and the second image may be a still image or a moving image.

  The image processing unit 20 is, for example, an information processing device that executes a learning process described later. Examples of the information processing device include a smart device, a desktop PC (Personal Computer), and a notebook PC. The image processing unit 20 may be a DSP (Digital Signal Processor) including an A / D conversion circuit, an input / output circuit, an arithmetic circuit, a memory, and the like. The image processing unit 20 stores a program for appropriately processing the first image or the second image. The image processing unit 20 may be realized by a server or a cloud system. The image processing unit 20 uses, for example, the relationship between the image of the learning model solar cell captured using transmitted light and the power generation performance of the learning model solar cell from learning data to be described later, or using transmitted light. A learning model is generated by learning the relationship between the captured image of the precursor of the learning model solar cell and the power generation performance of the learning model solar cell. The learning model solar cell means a solar cell for creating learning data used for generating a learning model. By processing the first image or the second image using the learning model, the power generation performance of the solar cell can be predicted. Thereby, the power generation performance of the solar cell can be evaluated. The image processing unit 20 includes, for example, means or functions of a learning model reading module, an image reading module, an image processing module, an image receiving module, an arithmetic module, and an output module.

  The evaluation device 100 may further include the imaging unit 30. The imaging unit 30 is connected to, for example, the image processing unit 20. The imaging unit 30 images a solar cell or a precursor of a solar cell. The imaging unit 30 may have a function of generating a first image or a second image based on a signal obtained by imaging a solar cell or a precursor of a solar cell. The first image or the second image may be generated by the image processing unit 20 based on a signal output from the imaging unit 30.

  As shown in FIG. 1, when imaging the solar cell 10 or the precursor of the solar cell 10 by the imaging unit 30, the solar cell 10 or the precursor of the solar cell 10 is disposed, for example, below the imaging unit 30. FIG. 1 shows an example in which the solar cell 10 is imaged. The imaging unit 30 is arranged at a position where the entire main surface of the solar cell 10 or the precursor of the solar cell 10 can be imaged. The main surface is a surface having the largest area of the solar cell 10 or the precursor of the solar cell 10. The solar cell 10 or the precursor may be arranged such that the central portion of the solar cell 10 or the precursor and the imaging unit 30 are arranged in a direction perpendicular to the main surface of the solar cell 10 or the precursor. The imaging unit 30 may be a camera having sensitivity to the entire visible light range, or may be a camera having sensitivity to only light in a specific wavelength range. The imaging unit 30 may be, for example, a near-ultraviolet camera having sensitivity to light in a wavelength range from 300 nm to 500 nm. The imaging unit 30 may be, for example, a near-infrared camera having sensitivity to light in a wavelength region from 700 nm to 1200 nm.

  The evaluation device 100 may further include a lighting unit 40. The illumination unit 40 is located on the side opposite to the imaging unit 30 with respect to the solar cell 10 or the precursor of the solar cell 10 when the imaging unit 30 images the solar cell 10 or the precursor of the solar cell 10. In FIG. 1, the imaging unit 30 is located above the solar cell 10 or a precursor of the solar cell 10. The lighting unit 40 is located below the solar cell 10 or a precursor of the solar cell 10. The illumination unit 40 irradiates the solar cell 10 or a precursor of the solar cell 10 with light. The light emitted from the illumination unit 40 passes through the solar cell 10 or the precursor of the solar cell 10 and enters the imaging unit 30. Thereby, the imaging unit 30 can image the solar cell 10 or the precursor of the solar cell 10 using the transmitted light. According to the imaging unit 30 and the illumination unit 40, it is possible to acquire the first image or the second image in which the information of the color unevenness of the light absorbing layer is reflected. The power generation performance of the solar cell 10 can be predicted by processing the first image or the second image by the image processing unit 20.

  The illumination unit 40 is not particularly limited as long as it can emit light. The illumination unit 40 is, for example, a semiconductor light emitting element such as a light emitting diode (LED), a super luminescent diode (SLD), and a laser diode (LD). The illumination unit 40 may be, for example, a fluorescent lamp. The lighting unit 40 may include a light emitting surface having an area approximately equal to the area of the main surface of the solar cell 10 or the precursor of the solar cell 10 to be evaluated. The illumination unit 40 may emit white light or emit light of a specific wavelength. The illumination unit 40 may emit light having a wavelength of 300 nm to 500 nm. When the solar cell 10 or a precursor of the solar cell 10 is imaged with light in this wavelength region, information on unevenness in the thickness of the light absorbing layer is remarkably shown in the first image or the second image. The illumination unit 40 may emit light having a wavelength of 700 nm to 1200 nm. When the solar cell 10 or the precursor of the solar cell 10 is imaged with light in this wavelength region, the deviation of the crystal of the light absorbing material in the light absorbing layer, the deviation of the film thickness of the light absorbing layer, or the additive or impurity phase The information of the segregation of appears remarkably in the first image or the second image.

  The evaluation device 100 may further include a storage unit 50. The storage unit 50 is connected to, for example, the image processing unit 20. The storage unit 50 is imaged using the transmitted light and the first learning data in which the image of the learning model solar cell captured using the transmitted light is associated with the power generation performance of the learning model solar cell. At least one learning data selected from the group consisting of the second learning data in which the image of the precursor of the learning model solar cell and the power generation performance of the learning model solar cell are stored. The storage unit 50 may store the learning model generated by the image processing unit 20. The storage unit 50 is realized by, for example, a server or a cloud system. The image processing unit 20 may function as a storage unit.

  The evaluation device 100 may further include the output unit 60. The output unit 60 is connected to, for example, the image processing unit 20. The output unit 60 outputs information on the power generation performance of the solar cell 10 evaluated by the image processing unit 20. The output unit 60 can output the first image or the second image. The output unit 60 includes a monitor (display), a touch panel, a printer, and the like. FIG. 1 shows a monitor as the output unit 60. An information processing device such as a smart device, a desktop PC, or a notebook PC may also serve as the image processing unit 20 and the output unit 60. The power generation performance output by the output unit 60 is selected, for example, from the group consisting of conversion efficiency, short-circuit current density, open-circuit voltage, fill factor, series resistance, sheet resistance, hysteresis, current-voltage curve, and changes with time. At least one. The hysteresis includes a hysteresis between a forward current-voltage curve and a reverse current-voltage curve. The current-voltage curve includes a current-voltage curve when the solar cell 10 is irradiated with light, and a current-voltage curve when the solar cell 10 is not irradiated with light. As an index of the change over time of the conversion efficiency, for example, a maintenance rate of the conversion efficiency may be mentioned.

  The evaluation device 100 may further include an input unit for giving a command to the image processing unit 20. The input unit includes a mouse, a keyboard, a touch pad, a touch panel, and the like.

  The evaluation device 100 may further include a switch 70 for operating the imaging unit 30. The switch 70 is connected to, for example, the image processing unit 20. The switch 70 is connected to the imaging unit 30 via the image processing unit 20. The switch 70 transmits a signal to the imaging unit 30 through the image processing unit 20. The switch 70 may be directly connected to the imaging unit 30. In that case, the switch 70 directly transmits a signal to the imaging unit 30.

(Solar cell evaluation method)
Next, a method for evaluating the solar cell 10 using the evaluation device 100 will be described.

  The evaluation method of the solar cell 10 is, for example, a group consisting of a first image of the solar cell 10 imaged using transmitted light and a second image of a precursor of the solar cell 10 imaged using transmitted light. Evaluating the power generation performance of the solar cell 10 based on at least one selected image. The evaluation method of the solar cell 10 may further include obtaining the first image of the solar cell 10 by imaging the solar cell 10 using the transmitted light. The method may further include obtaining a second image of the precursor by imaging the body.

  In the evaluation method of the solar cell 10, first, the solar cell 10 or a precursor of the solar cell 10 is arranged at a predetermined position. Next, the switch 70 is turned on. Thereby, a signal is transmitted from the switch 70 to the image processing unit 20. When receiving the signal from the switch 70, the image processing unit 20 transmits a signal to the imaging unit 30. Upon receiving the signal from the image processing unit 20, the imaging unit 30 captures an image of the solar cell 10 or a precursor of the solar cell 10. At this time, the illumination unit 40 irradiates the solar cell 10 or the precursor of the solar cell 10 with light. The imaging unit 30 generates a first image or a second image. The imaging unit 30 transmits the first image or the second image to the image processing unit 20. The image processing unit 20 evaluates the power generation performance by predicting the power generation performance of the solar cell 10 from the first image or the second image. The image processing unit 20 transmits the obtained evaluation result to the output unit 60. The output unit 60 outputs the received evaluation result.

  In the evaluation device 100, the image processing unit 20 evaluates the power generation performance of the solar cell by using the first image of the solar cell 10 captured using transmitted light and the first image of the solar cell 10 captured using transmitted light. At least one image selected from the group consisting of the second image of the precursor is used. Therefore, according to the image processing unit 20, the power generation performance of the solar cell 10 can be evaluated based on the information inside the solar cell 10 or the precursor of the solar cell 10. Thereby, the power generation performance of the solar cell 10 can be easily evaluated. According to the evaluation device 100, it is not necessary to operate the solar cell 10 under simulated sunlight in order to obtain the power generation performance of the solar cell 10. According to the evaluation device 100, there is no need to connect the solar cell 10 and a device for applying a bias voltage to the solar cell 10. According to the evaluation device 100, since the power generation performance of the solar cell 10 can be evaluated from the first image or the second image, the evaluation of the solar cell 10 can be performed in a relatively short time. Further, according to the evaluation device 100, the power generation performance of the solar cell 10 can be evaluated based on the second image of the precursor. If the power generation performance evaluated from the second image is not sufficient, it is not necessary to manufacture solar cell 10 from the precursor. Therefore, consumption of materials for manufacturing solar cell 10 can be suppressed.

(Learning processing and output processing by the image processing unit)
Next, an example of a specific method for evaluating the power generation performance of the solar cell 10 by the image processing unit 20 will be described. In this example, in the image processing unit 20, a learning process and an output process are performed.

  FIG. 2 shows an outline of a learning process and an output process by the image processing unit 20. First, the learning process by the image processing unit 20 will be described. The image processing unit 20 receives the first learning data or the second learning data stored in the storage unit 50 from the storage unit 50. As described above, the first learning data is learning data in which an image of the learning model solar cell captured using transmitted light is associated with the power generation performance of the learning model solar cell. The second learning data is learning data in which an image of a precursor of the learning model solar cell captured using transmitted light is associated with the power generation performance of the learning model solar cell. The first learning data or the second learning data includes, for example, a plurality of images and a plurality of power generation performances associated with each of the plurality of images.

  The image in the first learning data or the second learning data may be a still image or a moving image. The image in the first learning data or the second learning data is, for example, an image previously captured by the imaging unit 30 of the evaluation device 100. The power generation performance in the first learning data is obtained, for example, by operating the learning model solar cell displayed on the image under simulated sunlight. The power generation performance in the second learning data is obtained, for example, by operating a learning model solar cell manufactured from the precursor displayed on the image under simulated sunlight. The power generation performance in the first learning data or the second learning data is, for example, at least one numerical data selected from the group consisting of conversion efficiency, short-circuit current density, open-circuit voltage, fill factor, series resistance, and sheet resistance.

  The first learning data or the second learning data may be prototyped, created, and registered by any creator for the purpose of being used for any machine learning. The first learning data or the second learning data may be obtained from a known document or the like. The image in the first learning data or the second learning data includes an application condition of a coating solution containing a light absorbing material when forming the light absorbing layer, a drying condition of a coating film obtained by applying the coating solution, or And the roughness of the substrate may be further associated with each other. The application conditions of the application liquid include an application speed and an application temperature of the application liquid. The drying conditions of the coating film include a drying speed, a drying temperature and a humidity.

  Next, the image processing unit 20 learns the association between the image of the learning model solar cell captured using the transmitted light and the power generation performance of the learning model solar cell using the first learning data, (1) Generates a learning model or learns the relationship between the image of the precursor of the learning model solar cell captured using transmitted light and the power generation performance of the learning model solar cell using the second learning data. Then, a second learning model is generated. The first learning model or the second learning model is stored in the storage unit 50 or the image processing unit 20.

  Next, output processing by the image processing unit 20 will be described. First, the imaging unit 30 images the solar cell 10 or a precursor of the solar cell 10. The imaging unit 30 transmits the obtained first image or second image to the image processing unit 20. The image processing unit 20 inputs the first image received from the imaging unit 30 to the first learning model, or inputs the second image received from the imaging unit 30 to the second learning model. The image processing unit 20 evaluates the power generation performance of the solar cell by (i) processing the first image with the first learning model, and (ii) processing the second image with the second learning model. At least one selected from the group consisting of: evaluating the power generation performance of the solar cell. The first learning model indicates the relationship between the image of the learning model solar cell captured using transmitted light and the power generation performance of the learning model solar cell. The second learning model indicates the relationship between the image of the precursor of the learning model solar cell captured using transmitted light and the power generation performance of the learning model solar cell. The image processing unit 20 transmits the evaluated power generation performance to the output unit 60. The output unit 60 outputs the received information on the power generation performance.

  In the learning process, the image processing unit 20 reads the first learning data or the second learning data from the storage unit 50. The image processing unit 20 learns the association between the image of the learning model solar cell or the image of the precursor of the learning model solar cell and the power generation performance of the learning model solar cell by, for example, deep learning, and generates a learning model. Generate. The deep learning is, for example, a convolutional neural network (CNN). The image processing unit 20 generates at least one learning model selected from a group consisting of a first learning model and a second learning model, for example, using a convolutional neural network. As described later, the first learning model or the second learning model generated by the deep learning includes a plurality of layers having a plurality of nodes. The image processing unit 20 may generate at least one learning model selected from the group consisting of the first learning model and the second learning model by machine learning. The machine learning includes a simple neural network (NN: Neural Network), a random forest (RF) analysis, a support vector machine (SVM), and the like.

  When learning the first learning data or the second learning data by machine learning, the image is treated as one-dimensional data. Therefore, in such machine learning, a highly accurate learning model cannot be generated when an object is shifted from a predetermined position in an image or when the direction of the object is different from the predetermined direction. Sometimes. On the other hand, in deep learning represented by a convolutional neural network, an input image can be treated as two-dimensional data. Therefore, according to the deep learning, even when the object is displaced from a predetermined position in the image, or when the direction of the object is different from the predetermined direction, the object in the image is It can correctly recognize and generate a highly accurate learning model.

  If the first image or the second image is input to the learning model generated by the deep learning, the power generation performance such as the conversion efficiency of the solar cell can be evaluated with high accuracy. According to the learning model generated by the deep learning, by inputting the conversion efficiency to the learning model, an image of the learning model solar cell or an image similar to the image indicating the conversion efficiency is searched or generated from the learning data. You can also.

  Next, the output processing by the image processing unit 20 will be described in detail with reference to the flowchart of FIG. First, in step S1, the learning model reading module of the image processing unit 20 reads the first learning model or the second learning model. The reading of the learning model refers to preparing the image processing unit 20 in a state in which the calculation using the first learning model or the second learning model can be performed. Next, in step S2, the image reading module of the image processing unit 20 reads the first image or the second image.

  Next, in step S3, the image processing module of the image processing unit 20 rotates or trims the first image or the second image. Specifically, the image processing module extracts, from the first image, an outer peripheral edge of the solar cell 10 and a region where light does not pass through the solar cell 10. The image processing module rotates the first image to adjust the position and orientation of the solar cell 10 in the first image. The image processing module extracts, from the first image, only an area where light passes through the solar cell 10 by trimming. Similarly, the image processing module extracts, from the second image, an outer peripheral edge of the precursor and a region where light does not pass through the precursor. The image processing module rotates the second image to adjust the position and orientation of the precursor in the second image. The image processing module extracts, from the second image, only an area where light passes through the precursor by trimming. According to the image processing module, the first image or the second image can be simplified. As a result, the prediction accuracy of the power generation performance of the solar cell 10 can be improved. The operation of rotating or trimming the first image or the second image by the image processing module may be omitted.

  Next, in step S4, the image receiving module of the image processing unit 20 receives the first image or the second image processed by the image processing module as input data. Next, in step S5, the operation module of the image processing unit 20 performs an operation on the received input data using the first learning model or the second learning model.

  With reference to FIG. 4, an example of a calculation process by a calculation module of the image processing unit 20 will be described. FIG. 4 shows a neural network 200. The neural network 200 is, for example, a first learning model or a second learning model. The neural network 200 has an input layer 210, a hidden layer 220, and an output layer 230. When image data is input to the input layer 210 of the neural network 200, the data is transmitted from the input layer 210 to the output layer 230 via the hidden layer 220. In the output layer 230, power generation performance data is output. Each of the input layer 210, the hidden layer 220, and the output layer 230 has a plurality of nodes 240. In FIG. 4, the input layer 210 has four nodes 240a. The hidden layer 220 has five nodes 240b. The output layer 230 has two nodes 240c. Thus, the neural network 200 includes a plurality of layers having a plurality of nodes.

  The neural network 200 further has a plurality of edges 250. The plurality of nodes 240a are respectively connected to the plurality of nodes 240b via the plurality of edges 250a. The plurality of nodes 240b are respectively connected to the plurality of nodes 240c via the plurality of edges 250b.

  In FIG. 4, the neural network 200 has one hidden layer 220. However, the neural network 200 may have a plurality of hidden layers 220. When the neural network 200 is generated by a convolutional neural network, the hidden layer 220 includes a convolutional layer and a pooling layer. The convolutional layer performs a filtering process on a node 240 of a layer located closer to the input layer 210 than the convolutional layer. As a result, a feature map is obtained. The pooling layer generates a new feature map by further reducing the feature map output from the convolution layer. That is, the convolution layer plays a role in extracting local features of the image. The pooling layer is responsible for organizing local features of the image.

  Each of the input layer 210, the hidden layer 220, and the output layer 230 has an activation function. Each of the plurality of edges 250 has a weight. An operation is performed at each of the plurality of nodes 240. Specifically, at a specific node 240, the signal is obtained at the node 240 having a layer connected to the node 240 via the edge 250 and having a layer located closer to the input layer 210 than the layer having the node 240. An operation is performed based on the value. As described above, by performing the calculation in each of the plurality of nodes 240, the power generation performance data is output from the output layer 230 as output data. Details of the operation using the neural network are disclosed in, for example, JP-A-2018-22473, JP-A-2018-26098, and JP-A-2018-26108.

  Next, as shown in FIG. 3, in step S6, the output module of the image processing unit 20 outputs the obtained calculation result as information on the power generation performance of the solar cell 10. The output data is, for example, scalar data indicating one power generation performance or vector data indicating a plurality of power generation performances.

  The above-described module is realized, for example, by the image processing unit 20 reading a predetermined program and executing the program. At this time, the image processing unit 20 may be a computer such as a CPU (Central Processing Unit), an information processing device, and various terminals. The program may be provided in a form recorded on a recording medium such as a magnetic disk such as a flexible disk, a compact disk (CD), an optical disk such as a DVD (Digital Versatile Disc), and a magneto-optical disk. The program recorded on such a recording medium can be read by a computer. Examples of the CD include a CD-ROM. DVDs include DVD-ROMs and DVD-RAMs. The image processing unit 20 reads a program from the recording medium, for example, and transfers the program to an internal memory or an external memory of the image processing unit 20. An internal memory or an external memory stores the program. The image processing unit 20 reads a program from an internal memory or an external memory and executes the program. The program pre-recorded on the above-described recording medium may be provided to the image processing unit 20 via a communication line.

(Solar cells)
Next, a structure and a manufacturing method of the solar cell 10 evaluated by the evaluation device 100 will be described.

  FIG. 5 is a schematic sectional view showing an example of the solar cell 10.

  In the solar cell 10, the first electrode 2, the electron transport layer 5, the porous layer 7, the light absorbing layer 3, the hole transport layer 6, and the second electrode 4 are laminated on the substrate 1 in this order. Have been. The second electrode 4 faces the first electrode 2. The light absorbing layer 3 is located between the first electrode 2 and the second electrode 4. The light absorbing layer 3 contains a perovskite compound. Each of the first electrode 2 and the second electrode 4 has a region through which light is transmitted. The solar cell 10 has a region through which light passes. The solar cell 10 may not have the substrate 1, the electron transport layer 5, the porous layer 7, or the hole transport layer 6. The structure of the learning model solar cell shown in the image in the first learning data is, for example, the same as the structure of the solar cell 10.

  The pores in the porous layer 7 are connected from a portion in contact with the light absorption layer 3 to a portion in contact with the electron transport layer 5. Thereby, the material of the light absorbing layer 3 fills the pores of the porous layer 7 and can reach the surface of the electron transport layer 5. Therefore, since the light absorption layer 3 and the electron transport layer 5 are in contact with each other, it is possible to directly exchange electrons.

  Next, the basic operation and effect of the solar cell 10 will be described. When the solar cell 10 is irradiated with light, the light absorbing layer 3 absorbs the light and generates excited electrons and holes. The excited electrons move to the electron transport layer 5. On the other hand, holes generated in the light absorption layer 3 move to the hole transport layer 6. The electron transport layer 5 is connected to the first electrode 2, and the hole transport layer 6 is connected to the second electrode 4. Thereby, the solar cell 10 can extract current from the first electrode 2 as a negative electrode and the second electrode 4 as a positive electrode.

  The effect of increasing the optical path length of light passing through the light absorbing layer 3 is expected due to light scattering caused by the porous layer 7. As the optical path length increases, the amount of electrons and holes generated in the light absorbing layer 3 is expected to increase.

  The solar cell 10 can be manufactured, for example, by the following method. First, the first electrode 2 is formed on the surface of the substrate 1 by a chemical vapor deposition (CVD) method, a sputtering method, or the like. Next, the electron transport layer 5 is formed on the first electrode 2 by a sputtering method or the like. Next, a porous layer 7 is formed on the electron transport layer 5 by a coating method or the like.

  Providing the porous layer 7 on the electron transport layer 5 has an effect that the light absorbing layer 3 can be easily formed. That is, by providing the porous layer 7, the material of the light absorbing layer 3 enters the pores of the porous layer 7, and the porous layer 7 serves as a scaffold for the light absorbing layer 3. Therefore, the material of the light absorption layer 3 is unlikely to be repelled or aggregated on the surface of the porous layer 7. Therefore, the light absorption layer 3 can be formed as a uniform film.

The light absorbing layer 3 can be manufactured, for example, by the following method. Hereinafter, as an example, a method of manufacturing the light absorption layer 3 containing a perovskite compound represented by HC (NH ₂ ) ₂ PbI ₃ (hereinafter, may be abbreviated as “FAPbI ₃ ”) will be described.

First, PbI ₂ and HC (NH ₂ ) ₂ I (FAI) are added to an organic solvent. The organic solvent is at least one solvent selected from alcohols, lactones, alkyl sulfoxides, and amides. The organic solvent is, for example, a mixed solvent of N, N-dimethylformamide (DMF) and dimethylsulfoxide (DMSO).

Next, using a hot plate to heat the solution and PbI ₂ and FAI is added in an organic solvent to a temperature in the range of 40 ° C. or higher 180 ° C. or less, and PbI ₂ and FAI in an organic solvent By dissolving, a coating liquid is obtained. Next, a coating solution is applied on the porous layer 7 by spin coating. Thus, a coating film is formed on the porous layer 7. Next, the substrate 1 is placed on a hot plate and heat treatment is performed. Thereby, the light absorption layer 3 is formed. The heat treatment is performed, for example, at a temperature in the range of 40 ° C. to 100 ° C. for a time in the range of 15 minutes to 1 hour.

  Next, the hole transport layer 6 is formed on the light absorption layer 3. As a method for forming the hole transport layer 6, a coating method or a printing method can be adopted. Examples of the coating method include a doctor blade method, a bar coating method, a spray method, a dip coating method, and a spin coating method. Examples of the printing method include a screen printing method. If necessary, a plurality of materials may be mixed to form the hole transport layer 6, which may be pressurized or fired. When the material of the hole transport layer 6 is an organic low molecular substance or an inorganic semiconductor, the hole transport layer 6 can also be manufactured by a vacuum evaporation method or the like.

  Next, the solar cell 10 can be obtained by forming the second electrode 4 on the hole transport layer 6. Examples of a method for forming the second electrode 4 include a chemical vapor deposition (CVD) method and a sputtering method.

  Hereinafter, each component of the solar cell 10 will be specifically described.

(Substrate 1)
The substrate 1 plays a role of holding each layer of the solar cell 10. The substrate 1 can be formed from a transparent material. As the substrate 1, for example, a glass substrate and a plastic substrate can be used. The plastic substrate may be, for example, a plastic film. When the first electrode 2 has a sufficient strength, each layer can be held by the first electrode 2, so that the substrate 1 is not necessarily provided.

(First electrode 2 and second electrode 4)
Each of the first electrode 2 and the second electrode 4 has conductivity and translucency. Each of the first electrode 2 and the second electrode 4 transmits, for example, light in a visible region to a near-infrared region. Each of the first electrode 2 and the second electrode 4 can be formed using, for example, at least one of a transparent and conductive metal oxide and metal nitride. Examples of such a material include titanium oxide doped with at least one of lithium, magnesium, niobium, and fluorine, gallium oxide doped with at least one of silicon, silicon, and at least one selected from oxygen. Doped gallium nitride, indium-tin composite oxide, antimony, tin oxide doped with at least one of fluorine, boron oxide, aluminum, gallium, zinc oxide doped with at least one of indium, and composites thereof. Can be

  The first electrode 2 or the second electrode 4 can be formed by using a material that is not transparent and providing a pattern through which light is transmitted. Examples of the pattern through which light is transmitted include a linear (stripe), wavy, lattice (mesh), punching metal pattern in which a large number of fine through holes are regularly or irregularly arranged, and These include patterns in which the negative / positive is reversed. When the first electrode 2 or the second electrode 4 has these patterns, light can pass through a portion where no electrode material exists. Non-transparent electrode materials include, for example, platinum, gold, silver, copper, aluminum, rhodium, indium, titanium, iron, nickel, tin, zinc, and alloys containing any of these. A conductive carbon material can also be used.

  When the solar cell 10 does not have the electron transport layer 5, the first electrode 2 is formed of, for example, a material having a property of blocking holes from the light absorption layer 3. In this case, the first electrode 2 does not make ohmic contact with the light absorbing layer 3. The blocking property for holes from the light absorbing layer 3 means a property of passing only electrons generated in the light absorbing layer 3 and not passing holes. The material having such properties is a material having a higher Fermi energy than the energy of the upper end of the valence band of the light absorption layer 3. The above material may be a material having a higher Fermi energy than the Fermi energy of the light absorption layer 3. A specific material is aluminum. When the solar cell 10 has the electron transport layer 5, the first electrode 2 does not need to have a blocking property for holes from the light absorption layer 3.

  When the solar cell 10 does not have the hole transport layer 6, the second electrode 4 is formed of, for example, a material having a property of blocking electrons from the light absorption layer 3. In this case, the second electrode 4 does not make ohmic contact with the light absorbing layer 3. The property of blocking electrons from the light absorbing layer 3 means a property of passing only holes generated in the light absorbing layer 3 and not passing electrons. The material having such properties is a material having lower Fermi energy than the energy of the lower end of the conduction band of the light absorption layer 3. The above-mentioned material may be a material having a lower Fermi energy than the Fermi energy of the light absorption layer 3. Specific materials include platinum, gold, and carbon materials such as graphene. When the solar cell 10 has the hole transport layer 6, the second electrode 4 does not need to have a blocking property for electrons from the light absorption layer 3.

  A material having a blocking property for holes from the light absorbing layer 3 and a material having a blocking property for electrons from the light absorbing layer 3 may not have a light transmitting property. Therefore, when the first electrode 2 or the second electrode 4 is formed using these materials, the first electrode 2 or the second electrode 4 can be formed, for example, by providing a pattern through which light is transmitted. . The above-mentioned pattern is mentioned as a pattern which light transmits.

  The light transmittance of each of the first electrode 2 and the second electrode 4 may be, for example, 50% or more, or may be 80% or more. The wavelength of the light to be transmitted depends on the absorption wavelength of the light absorption layer 3. The thickness of each of the first electrode 2 and the second electrode 4 is, for example, in a range from 1 nm to 1000 nm.

(Electron transport layer 5)
The electron transport layer 5 includes a semiconductor. The electron transport layer 5 may be a semiconductor having a band gap of 3.0 eV or more. By forming the electron transport layer 5 with a semiconductor having a band gap of 3.0 eV or more, visible light and infrared light can be transmitted to the light absorption layer 3. Examples of the semiconductor include an organic or inorganic n-type semiconductor.

Examples of the organic n-type semiconductor include imide compounds, quinone compounds, fullerenes, and fullerene derivatives. As the inorganic n-type semiconductor, for example, an oxide of a metal element, a nitride of a metal element, and a perovskite oxide can be used. Examples of the metal element oxide include Cd, Zn, In, Pb, Mo, W, Sb, Bi, Cu, Hg, Ti, Ag, Mn, Fe, V, Sn, Zr, Sr, Ga, Si and An oxide of Cr can be used. A more specific example is TiO ₂ . Examples of the nitride of the metal element include GaN. Examples of perovskite-type oxides include SrTiO ₃ and CaTiO ₃ .

  The electron transport layer 5 may be formed of a substance having a band gap larger than 6.0 eV. Examples of the substance having a band gap larger than 6.0 eV include alkali metal or alkaline earth metal halides such as lithium fluoride and calcium fluoride, alkali metal oxides such as magnesium oxide, and silicon dioxide. In this case, the thickness of the electron transport layer 5 is, for example, 10 nm or less in order to secure the electron transport property of the electron transport layer 5.

  The electron transport layer 5 may include a plurality of layers made of different materials.

(Porous layer 7)
The porous layer 7 serves as a base when forming the light absorbing layer 3. The porous layer 7 does not hinder the light absorption of the light absorption layer 3 and the electron transfer from the light absorption layer 3 to the electron transport layer 5.

The porous layer 7 includes a porous body. Examples of the porous body include a porous body in which insulating or semiconductor particles are connected. As the insulating particles, for example, particles of aluminum oxide and silicon oxide can be used. As the semiconductor particles, inorganic semiconductor particles can be used. As the inorganic semiconductor, an oxide of a metal element, a perovskite oxide of a metal element, a sulfide of a metal element, and a metal chalcogenide can be used. Examples of oxides of metal elements include Cd, Zn, In, Pb, Mo, W, Sb, Bi, Cu, Hg, Ti, Ag, Mn, Fe, V, Sn, Zr, Sr, Ga, Si, An oxide of Cr may be used. A more specific example is TiO ₂ . Examples of the perovskite oxide of a metal element include SrTiO ₃ and CaTiO ₃ . Examples of sulfides of the metal _{element, CdS, ZnS, In 2 S} 3, SnS, PbS, Mo 2 S, WS 2, Sb 2 S 3, Bi 2 S 3, ZnCdS 2, Cu 2 S and the like. Examples of the metal _{chalcogenides, CdSe, CsSe, In 2 Se} 3, WSe 2, HgS, SnSe, PbSe, include CdTe is.

  The thickness of the porous layer 7 may be from 0.01 μm to 10 μm, or from 0.1 μm to 1 μm. The surface roughness of the porous layer 7 may be large. Specifically, the surface roughness coefficient given by “effective area / projected area” may be 10 or more, or 100 or more. Note that the projection area is an area of a shadow formed behind when an object is illuminated with light from directly in front. The effective area is the actual surface area of the object. The effective area can be calculated from the volume determined from the projected area and the thickness of the object, and the specific surface area and the bulk density of the material constituting the object. The specific surface area is measured, for example, by a nitrogen adsorption method.

(Light absorbing layer 3)
The light absorbing layer 3 contains a perovskite compound. The perovskite compound is represented, for example, by the composition formula ABX ₃ (A is a monovalent cation, B is a divalent cation, and X is a halogen anion). Hereinafter, the perovskite compound represented by the composition formula ABX ₃ may be referred to as “perovskite compound in the present embodiment”. The divalent cation located at the B site of the perovskite compound contained in the learning model solar cell shown in the image in the first learning data is, for example, the B site of the perovskite compound contained in the light absorption layer 3. Is the same as the divalent cation located at The composition of the perovskite compound contained in the learning model solar cell shown in the image of the first learning data may be the same as the composition of the perovskite compound contained in the light absorption layer 3.

The monovalent cation located at the A site is not particularly limited. The monovalent cation is, for example, an organic cation or an alkali metal cation. Examples of the monovalent cation include a methylammonium cation (CH ₃ NH ₃ ⁺ ), a formamidinium cation (NH ₂ CHNH ₂ ⁺ ), a cesium cation (Cs ⁺ ), a rubidium cation (Rb ⁺ ), and a potassium cation (K ⁺ ), A phenethylammonium cation (C ₆ H ₅ CH ₂ CH ₂ NH ₃ ⁺ ) and a guanidinium cation (CH ₆ N ₃ ⁺ ). The monovalent cation is, for example, a methylammonium cation or a formamidinium cation.

The divalent cation located at the B site is not particularly limited. The divalent cation is, for example, Pb ²⁺ , Sn ²⁺ or Ge ²⁺ . The halogen anion located at the X site is, for example, an iodide ion, a bromide ion, a fluoride ion or a chloride ion. A thiocyanate ion ([SCN] ^- ) may be located at the X site. The A site, the B site, and the X site may each be occupied by a plurality of types of ions.

  The light absorbing layer 3 may mainly include the perovskite compound in the present embodiment. Here, “the light absorbing layer 3 mainly contains the perovskite compound in the present embodiment” means that the light absorbing layer 3 contains 90% by mass or more of the perovskite compound in the present embodiment, for example, 95% by mass or more. The light absorbing layer 3 may be made of the perovskite compound in the present embodiment.

  The thickness of the light absorbing layer 3 is, for example, not less than 100 nm and not more than 10 μm although it depends on the magnitude of the light absorption. The thickness of the light absorbing layer 3 may be 100 nm or more and 1000 nm or less.

(Hole transport layer 6)
The hole transport layer 6 is made of an organic material, an inorganic semiconductor, or the like. Examples of typical organic substances used as the hole transport layer 6 include spiro-OMeTAD (2,2 ', 7,7'-tetrakis- (N, N-di-p-methoxyphenylamine) 9,9'-spirobifluorene ), PTAA (poly [bis (4-phenyl) (2,4,6-trimethylphenyl) amine]), P3HT (poly (3-hexylthiophene-2,5-diyl)), PEDOT (poly (3,4-ethylenedioxythiophene) )).

Examples of the inorganic semiconductor include a carbon-based material such as Cu ₂ O, CuGaO ₂ , CuSCN, CuI, CuPC, NiO _x , MoO _x , V ₂ O ₅ , and graphene oxide.

  The hole transport layer 6 may include a plurality of layers made of different materials.

  The thickness of the hole transport layer 6 may be from 1 nm to 1000 nm, from 10 nm to 500 nm, or from 10 nm to 50 nm. Within this range, a sufficient hole transporting property can be exhibited. Since low resistance can be maintained, photovoltaic power generation can be performed with high efficiency. The thickness of the hole transport layer 6 may be so small that the hole transport layer 6 has a light transmitting property.

  The hole transport layer 6 may include a supporting electrolyte and a solvent. The supporting electrolyte and the solvent have an effect of stabilizing the holes in the hole transport layer 6.

Examples of the supporting electrolyte include ammonium salts and alkali metal salts. Examples of the ammonium salt include tetrabutylammonium perchlorate, tetraethylammonium hexafluorophosphate, imidazolium salt, and pyridinium salt. Examples of the alkali metal salt include LiTFSI (Lithium bis (trifluoromethanesulfonyl) imide), LiPF ₆ , LiBF ₄ , lithium perchlorate and potassium boron tetrafluoride.

  The solvent contained in the hole transport layer 6 may have excellent ionic conductivity. Although any of an aqueous solvent and an organic solvent can be used, an organic solvent may be used from the viewpoint of further stabilizing the solute. Specific examples include heterocyclic compound solvents such as tert-butylpyridine, pyridine, and n-methylpyrrolidone.

  The ionic liquid may be used alone or as a mixture with other solvents. Ionic liquids are desirable because of their low volatility and high flame retardancy.

  Examples of the ionic liquid include imidazolium-based ionic liquids such as 1-ethyl-3-methylimidazolium tetracyanoborate, pyridine-based, alicyclic amine-based, aliphatic amine-based, and azoniumamine-based ionic liquids. it can.

(Precursor of solar cell)
Next, the structure of the precursor of the solar cell 10 evaluated by the evaluation device 100 will be described.

  FIG. 6 is a schematic sectional view illustrating an example of the precursor 15. The precursor 15 has the same structure as the solar cell 10 except that the precursor 15 does not include the hole transport layer 6 and the second electrode 4. The precursor 15 includes only the first electrode 2 as an electrode. In the precursor 15, the light absorption layer 3 is located on the first electrode 2. The precursor 15 is obtained at the stage when the light absorbing layer 3 is manufactured in the method for manufacturing the solar cell 10. The precursor 15 may not have the substrate 1, the electron transport layer 5, or the porous layer 7. The structure of the precursor of the learning model solar cell shown in the image in the second learning data is, for example, the same as the structure of the precursor 15.

  The evaluation device 100 can evaluate the power generation performance of the solar cell 10 using the second image of the precursor 15. That is, it is not necessary to produce the hole transport layer 6 and the second electrode 4 in order to evaluate the power generation performance of the solar cell 10. When the power generation performance evaluated by the evaluation device 100 is not sufficient, it is not necessary to manufacture the solar cell 10 from the precursor 15. The solar cell 10 is manufactured by removing the precursor 15 evaluated as having insufficient power generation performance from the production line and manufacturing the solar cell 10 only from the precursor 15 evaluated as having sufficient power generation performance. Consumption of materials for the purpose can be suppressed.

[Embodiment 2]
FIG. 7 is a schematic configuration diagram illustrating another example of the evaluation device of the present embodiment. The structure of the evaluation device 150 of the second embodiment is the same as the structure of the evaluation device 100 of the first embodiment, except that the evaluation device 150 further includes a transport unit 90 and includes a photoelectric switch 71 instead of the switch 70. Therefore, components common to the evaluation device 100 of the first embodiment and the evaluation device 150 of the present embodiment are denoted by the same reference numerals, and description thereof may be omitted.

  The evaluation device 150 may be arranged in a production line of the solar cell 10 as shown in FIG. 7, for example. In the example shown in FIG. 7, a coating device 80 for forming the light absorption layer 3 of the solar cell 10 and layers such as the hole transport layer 6 formed on the light absorption layer 3 are formed on the production line. The evaluation device 150 is disposed between the evaluation device 150 and the device.

  The coating device 80 applies a coating liquid for forming the light absorbing layer 3 onto the laminate 16. The stacked body 16 includes the first electrode 2. In FIG. 7, the laminate 16 has a structure in which the substrate 1, the first electrode 2, the electron transport layer 5, and the porous layer 7 are laminated in this order. The light absorption layer 3 is formed by heat-treating the coating film produced by the coating device 80. Thereby, the precursor 15 is obtained. The coating device 80 may include a heating device for heat-treating the coating film.

  The transport unit 90 transports the precursor 15 produced by the coating device 80 to a predetermined position. At a predetermined position, the precursor 15 is imaged by the imaging unit 30. The transport unit 90 is, for example, a roller conveyor. When the transport unit 90 is a roller conveyor, the transport unit 90 includes a plurality of rollers. The plurality of rollers are arranged in the transport direction Y. Each of the plurality of rollers contacts the precursor 15. The precursor 15 is transported in the transport direction Y by each of the plurality of rollers rotating at a predetermined rotational speed in a predetermined direction.

  In the evaluation device 150, the photoelectric switch 71 detects whether or not the precursor 15 to be imaged is located at a predetermined position. When the photoelectric switch 71 detects the precursor 15, the photoelectric switch 71 transmits a signal for notifying that the precursor 15 is located at a predetermined position to the image processing unit 20. When receiving the signal from the photoelectric switch 71, the image processing unit 20 transmits a signal to the imaging unit 30. When receiving the signal from the image processing unit 20, the imaging unit 30 captures an image of the precursor 15. Thereby, a second image is obtained. By processing the second image by the method described above, the power generation performance of the solar cell 10 formed from the precursor 15 can be evaluated.

  Hereinafter, the present disclosure will be specifically described with reference to examples. However, the present disclosure is not limited at all by the following examples.

(Production of solar cell)
A perovskite solar cell having the same structure as the perovskite solar cell 10 shown in FIG. 5 was manufactured.

  The perovskite solar cell in the example was manufactured as follows.

First, a conductive substrate having a conductive layer functioning as the first electrode 2 on its surface was prepared. As the conductive substrate, a 1 mm-thick conductive glass substrate (manufactured by Nippon Sheet Glass) on which an indium-doped SnO ₂ layer functioning as the first electrode 2 was formed was used. In the conductive substrate, an indium-doped SnO ₂ layer was disposed as a first electrode 2 on a glass substrate. The surface resistance of the first electrode 2 is 10Ω / sq. Met.

  Next, a titanium oxide layer having a thickness of about 10 nm was formed as the electron transport layer 5 on the first electrode 2 by a sputtering method.

  Next, high-purity titanium oxide powder having an average primary particle diameter of 30 nm was dispersed in ethyl cellulose to prepare a titanium oxide paste for screen printing.

  A titanium oxide paste was applied on the electron transport layer 5 and dried. Further, by baking in air at 500 ° C. for 30 minutes, a porous layer 7 which is a porous titanium oxide layer having a thickness of 0.2 μm was formed.

  Next, a solution in which 10 mg of lithium bis (trifluoromethanesulfonyl) imide (LiTFSI) was dissolved in 1 mL of acetonitrile (hereinafter, referred to as “Li-containing solution”) was prepared.

  The Li-containing solution was applied to the electron transport layer 5 and the porous layer 7 by dropping the Li-containing solution onto the porous layer 7 and performing spin coating. Furthermore, by baking in air at a temperature of 500 ° C. for 30 minutes, each of the electron transport layer 5 and the porous layer 7 contained Li. Thereby, the electron transport layer 5 and the porous layer 7 containing Li were obtained.

Next, PbI ₂ , PbBr ₂ , and HC (NH ₂ ) ₂ I (FAI) were added to a solution obtained by mixing N, N-dimethylformamide (DMF) and dimethyl sulfoxide (DMSO) at a volume ratio of 4: 1. , CH ₃ NH ₃ Br (MABr), CsI and RbI were dissolved to prepare a coating solution for forming the light absorbing layer 3. The concentration of PbI _{2 in} the coating solution was 0.92 mol / L. The concentration of PbBr _{2 in} the coating solution was 0.17 mol / L. The concentration of FAI in the coating solution was 0.83 mol / L. The concentration of MABr in the coating solution was 0.17 mol / L. The concentration of CsI in the coating solution was 0.05 mol / L. The concentration of RbI in the coating solution was 0.05 mol / L. This coating solution was spin-coated on the porous layer 7 to obtain a coating film. Next, the substrate 1 on which the coating film was laminated was placed on a hot plate, and heat treatment was performed at 100 ° C. to form the light absorption layer 3. The light absorbing layer 3 mainly contained a perovskite compound having a composition formula (FAPbI ₃ ) _0.83 (MAPbBr ₃ ) _0.17 .

  Next, to a toluene solution containing PTAA at a concentration of 10 mg / mL, 5 μL of tert-butylpyridine (tBP) and 3 μL of acetonitrile solution containing LiTFSI at a concentration of 1.8 mol / L were added to form a hole transport layer. A coating liquid for forming No. 6 was prepared. This coating solution was spin-coated on the light absorption layer 3 to form the hole transport layer 6.

  Next, gold was deposited on the hole transport layer 6 so as to have a thickness of 80 nm, whereby the second electrode 4 was formed. Thus, a solar cell was obtained.

  On the basis of the above method, a total of 400 solar cells were produced. In each of the manufacturing methods of 400 solar cells, the concentration of LiTFSI in the Li-containing solution was appropriately changed in the range of 0 mg / mL to 30 mg / mL. In each of the manufacturing methods of the 400 solar cells, the composition of the perovskite compound contained in the light absorption layer 3 was appropriately changed. Specifically, the molar ratio between FA and MA in the perovskite compound was appropriately changed in the range of 75:25 to 100: 0. The molar ratio of I to Br in the perovskite compound was appropriately changed in the range of 83:17 to 100: 0. In each of the manufacturing methods of the 400 solar cells, the concentration of RbI in the coating solution for forming the light absorbing layer 3 was appropriately changed in the range of 0 to 0.1 mol / L. In each of the manufacturing methods of 400 solar cells, the amount of tBP added to the toluene solution containing PTAA was appropriately changed in the range of 0 to 6 μL. In each of the manufacturing methods of 400 solar cells, the addition amount of the acetonitrile solution containing LiTFSI to the toluene solution containing PTAA was appropriately changed in the range of 0 to 5 μL.

(Measurement of power generation performance)
Next, the conversion efficiency was measured for each of the 400 solar cells obtained. The conversion efficiency was measured under simulated sunlight using a solar simulator set at an output of 100 mW / cm ² .

(Image of solar cell)
Next, each of the 400 solar cells was imaged using transmitted light. Specifically, a solar cell was arranged on a lighting device made of surface-emitting LEDs such that the second electrode 4 was on the upper surface, and the solar cell was imaged from above. Imaging was performed with a visible light camera. Thereby, an image was obtained for each of the 400 solar cells.

(Image processing)
Next, the following processing was performed on each of the obtained 400 images. First, the outer edge of the solar cell in the image was detected. The image was rotated to adjust the position and orientation of the solar cell in the image. Next, a region where light did not pass through the solar cell was removed, and only a region where light passed through the solar cell was extracted. In this way, the processing was performed in advance for each of the 400 images. Through the above operation, 400 pieces of data in which the image of the solar cell is associated with the power generation performance of the solar cell are obtained.

(Evaluation of conversion efficiency)
Next, a learning model was generated and the conversion efficiency was evaluated using the obtained 400 data. The generation of the learning model and the evaluation of the conversion efficiency were performed by quadrant cross-validation using a convolutional neural network. Specifically, the generation of the learning model and the evaluation of the conversion efficiency were performed by the following method. First, 400 data were divided into four to create a first data group, a second data group, a third data group, and a fourth data group. Each of the first to fourth data groups was a group of 100 data. Next, three data groups were selected from the first to fourth data groups. The 300 data included in the selected three data groups were learned by a convolutional neural network to generate a learning model. Next, using this learning model, images of 100 data included in the data group not used for generating the learning model were processed. Thereby, the predicted value of the conversion efficiency of the solar cell was calculated for each of the processed 100 data images. The generation of the learning model and the calculation of the predicted value of the conversion efficiency of the solar cell were repeated four times in total. At this time, in each of the four operations, the combination of the three data groups used for generating the learning model was different from each other. As a result, a predicted value of the conversion efficiency of the solar cell was calculated for each of the 400 data.

  FIG. 8 shows the relationship between the predicted value of the conversion efficiency of the solar cell obtained by the above operation and the actually measured value of the conversion efficiency of the solar cell. In the graph of FIG. 8, the horizontal axis indicates the measured value of the conversion efficiency of the solar cell. The vertical axis indicates the predicted value of the conversion efficiency of the solar cell. The Root Mean Squared Error (RMSE) calculated based on this result was 2.6%. From this result, it can be seen that the conversion efficiency of the solar cell could be predicted with high accuracy. As can be seen from FIG. 8, the accuracy of the prediction of the conversion efficiency is particularly high when the measured value of the conversion efficiency is 10% or more. On the other hand, when the measured value of the conversion efficiency is 5% or less, the predicted value of the conversion efficiency may be significantly different from the measured value. This is considered to be due to the fact that when generating the learning model, the number of data of the solar cells whose conversion efficiency measured value was 5% or less was insufficient. That is, if a learning model is generated using a sufficient number of data, it is expected that the conversion efficiency of the solar cell can be predicted with higher accuracy.

  FIG. 9 shows images of a plurality of solar cells in which the predicted value of the conversion efficiency was 13% or more. Further, FIG. 10 shows images of a plurality of solar cells in which the predicted value of the conversion efficiency was 5% or less. The image of the solar cell shown in FIG. 9 is actually orange. Among the images of the solar cell shown in FIG. 10, the upper left and center images are black, and the remaining images are white. As can be seen from FIG. 9, when the color of the light absorbing layer is orange and the color unevenness of the light absorbing layer is suppressed, the conversion efficiency of the solar cell tends to be high. On the other hand, as can be seen from FIG. 10, when the color of the light absorbing layer is black or white, or when the light absorbing layer has uneven color, the conversion efficiency of the solar cell tends to be low. As described above, the power generation performance of the solar cell can be evaluated from information such as color unevenness of the light absorbing layer of the solar cell. Furthermore, according to the image of the solar cell taken using transmitted light, information inside the light absorbing layer can be obtained, so that the power generation performance can be evaluated with high accuracy.

  The technology disclosed in this specification is useful as a solar cell evaluation device.

DESCRIPTION OF SYMBOLS 1 Substrate 2 1st electrode 3 Light absorption layer 4 2nd electrode 5 Electron transport layer 6 Hole transport layer 7 Porous layer 10 Solar cell 15 Precursor 20 Image processing part 30 Imaging part 40 Illumination part 50 Storage part 60 Output part 100 , 150 evaluation device

Claims (12)

  A first image of the solar cell imaged using transmitted light, and at least one image selected from a group consisting of a second image of the precursor of the solar cell imaged using transmitted light, An evaluation device for a solar cell, comprising an image processing unit for evaluating the power generation performance of the solar cell. The image processing unit evaluates the power generation performance of the solar cell by (i) processing the first image with a first learning model, and (ii) processing the second image with a second learning model. By performing the process, the power generation performance of the solar cell is evaluated, at least one selected from the group consisting of:
The first learning model indicates a relationship between an image of the learning model solar cell captured using transmitted light and the power generation performance of the learning model solar cell,
The sun according to claim 1, wherein the second learning model indicates a relationship between an image of a precursor of the learning model solar cell captured using transmitted light and the power generation performance of the learning model solar cell. Battery evaluation device.   The image processing unit generates the first learning model by learning first learning data in which the image of the learning model solar cell is associated with the power generation performance of the learning model solar cell. The solar cell evaluation device according to claim 2.   The image processing unit learns the second learning data in which the image of the precursor of the learning model solar cell is associated with the power generation performance of the learning model solar cell, so that the second learning is performed. The solar cell evaluation device according to claim 2, which generates a model.   5. The image processing unit according to claim 2, wherein the image processing unit generates at least one learning model selected from the group consisting of the first learning model and the second learning model using a convolutional neural network. The solar cell evaluation device according to the above.   The solar cell evaluation device according to any one of claims 1 to 5, further comprising an imaging unit configured to image the solar cell or the precursor of the solar cell.   When imaging the solar cell or the precursor of the solar cell by the imaging unit, with respect to the solar cell or the precursor of the solar cell, the imaging unit is located on the opposite side to the imaging unit, and the sun The solar cell evaluation device according to claim 6, further comprising an illumination unit that irradiates light to the battery or the precursor of the solar cell.   The solar cell evaluation device according to claim 1, further comprising an output unit configured to output information on the power generation performance of the solar cell evaluated by the image processing unit.   First learning data in which the image of the learning model solar cell captured using transmitted light is associated with the power generation performance of the learning model solar cell, and the learning model captured using transmitted light. A storage unit for storing at least one learning data selected from the group consisting of second learning data in which an image of a precursor of a solar cell and the power generation performance of the learning model solar cell are associated with each other. Item 10. The solar cell evaluation device according to any one of Items 1 to 8.   The solar cell evaluation device according to any one of claims 1 to 9, wherein the solar cell and the precursor of the solar cell include a light absorption layer containing a perovskite compound.   The power generation performance of the solar cell is at least one selected from the group consisting of conversion efficiency, short-circuit current density, open-circuit voltage, fill factor, series resistance, sheet resistance, hysteresis, current-voltage curve, and changes with time. The solar cell evaluation device according to any one of claims 1 to 10.   A first image of the solar cell captured using transmitted light, and at least one image selected from a group consisting of a second image of the precursor of the solar cell captured using transmitted light, A method for evaluating a solar cell, including evaluating the power generation performance of the solar cell.