Method and device for monitoring mechanical strength of materials

Abstract

The present application discloses a material mechanical strength monitoring method and device, which belongs to the field of semiconductor technology. The material mechanical strength monitoring method comprises: obtaining the film refractive index corresponding to the material to be tested; inputting the film refractive index into a trained target monitoring model, and obtaining the mechanical strength parameters corresponding to the material to be tested output by the target monitoring model; the mechanical strength parameters include at least one of the hardness and elastic modulus corresponding to the material to be tested. The material mechanical strength monitoring method of the present application realizes non-destructive monitoring, saves a lot of material cost and time cost, can monitor the mechanical properties of the material in real time, and has a small test error, and the accuracy and precision of the calculation results are high.

Description

Technical Field

[0001] The present application belongs to the field of semiconductor technology, and in particular, relates to a method and device for monitoring the mechanical strength of a material. Background Art

[0002] As chip integration continues to increase, problems such as chip RC delay, line crosstalk, and power dissipation are becoming more and more serious. To solve these problems, new Low-k materials must be used to replace traditional dielectric materials. In addition to having a low dielectric constant, Low-k materials should also have mechanical properties such as high strength and high elastic modulus. There is a method of testing the mechanical strength of materials through an indenter in the relevant technology, but this method requires manual testing of the materials individually, and the wafer will be damaged when monitoring the mechanical strength, which requires a lot of material costs and time costs. It is impossible to non-destructively and effectively monitor the mechanical strength of the material in real time, and the manual test has a large error. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems existing in the related art. To this end, the present application proposes a material mechanical strength monitoring method and device, which realizes non-destructive monitoring, saves a lot of material cost and time cost, can monitor the mechanical properties of the material in real time, and has a small test error and high accuracy and precision of the calculation results.

[0004] In a first aspect, the present application provides a method for monitoring the mechanical strength of a material, the method comprising:

[0005] Obtain the film refractive index corresponding to the material to be tested;

[0006] The film refractive index is input into a trained target monitoring model to obtain the mechanical strength parameters corresponding to the material to be tested output by the target monitoring model; the mechanical strength parameters include at least one of the hardness and elastic modulus corresponding to the material to be tested.

[0007] According to the mechanical strength monitoring method of materials provided in the embodiments of the present application, by obtaining the thin film refractive index of the material to be tested and inputting the thin film refractive index into a trained target monitoring model, the mechanical strength parameters output by the target monitoring model can be obtained. There is no need for a dedicated person to perform destructive testing on the material alone, thus achieving non-destructive monitoring and saving a lot of material and time costs. Moreover, the mechanical strength parameters of the material can be measured immediately after each adjustment of the process parameters, and the mechanical properties of the material can be monitored in real time. The test error is small, and the accuracy and precision of the calculation results are high.

[0008] A method for monitoring the mechanical strength of a material according to an embodiment of the present application, wherein the refractive index of the film is input into a trained target monitoring model, and a mechanical strength parameter corresponding to the material to be tested output by the target monitoring model is obtained, comprising:

[0009] The film refractive index is input into the target monitoring model, and the film refractive index is processed by the target algorithm deployed in the target monitoring model to obtain the mechanical strength parameter output by the target monitoring model after processing by the target algorithm; wherein the target algorithm includes the following formula:

[0010] Y=ARI 3 -BRI 2 +CRI-D

[0011] Among them, Y is the mechanical strength parameter, A, B, C and D are parameters obtained by training the target monitoring model, and RI is the refractive index of the film.

[0012] In a method for monitoring the mechanical strength of a material according to an embodiment of the present application, the target monitoring model includes a first sub-model, the target algorithm includes a first algorithm, and the first algorithm is deployed in the first sub-model; the thin film refractive index is input into the target monitoring model, and the thin film refractive index is processed by the target algorithm deployed in the target monitoring model, and the mechanical strength parameter output by the target monitoring model after processing by the target algorithm is obtained, including:

[0013] The film refractive index is input into the first sub-model, and the film refractive index is processed by the first algorithm deployed in the first sub-model to obtain the hardness corresponding to the material to be tested output by the first sub-model after processing by the first algorithm.

[0014] A method for monitoring the mechanical strength of a material according to an embodiment of the present application,

[0015] The target monitoring model includes a second sub-model, the target algorithm includes a second algorithm, and the second algorithm is deployed in the second sub-model; the thin film refractive index is input into the target monitoring model, the thin film refractive index is processed by the target algorithm deployed in the target monitoring model, and the mechanical strength parameter output by the target monitoring model after the processing by the target algorithm is obtained, including:

[0016] The film refractive index is input into the second sub-model, and the film refractive index is processed by the second algorithm deployed in the second sub-model to obtain the elastic modulus corresponding to the material to be tested output by the second sub-model after processing by the second algorithm.

[0017] In one embodiment of the present application, the material mechanical strength monitoring method is provided, wherein the target monitoring model is trained based on the following steps:

[0018] Acquire sample refractive indices corresponding to sample materials of the same category as the material to be tested, and sample mechanical strengths corresponding to the sample refractive indices;

[0019] Inputting the sample refractive index and the sample mechanical strength into an initial fitting model, and training the initial fitting model using a target regression algorithm;

[0020] The trained initial fitting model is determined as the target monitoring model.

[0021] A method for monitoring the mechanical strength of a material according to an embodiment of the present application, wherein obtaining a sample refractive index corresponding to a sample material of the same category as the material to be tested, and a sample mechanical strength corresponding to each of the sample refractive indices, comprises:

[0022] Under target conditions, octamethylcyclotetrasiloxane within a first flow range and oxygen within a second flow range are used as reaction source gases, and a plasma enhanced chemical vapor deposition technique is used to prepare the sample material; wherein the target conditions include a target process temperature range, a target pressure range corresponding to the reaction chamber, a target high-frequency power range, a target low-frequency power range, a target plate spacing range, and a target helium flow range;

[0023] A film thickness meter is used to obtain the sample refractive index corresponding to the sample material; an indenter is used to obtain the sample mechanical strength corresponding to the sample material.

[0024] In a second aspect, the present application provides a device for monitoring the mechanical strength of a material, comprising:

[0025] A first processing module is used to obtain the film refractive index corresponding to the material to be tested;

[0026] The second processing module is used to input the film refractive index into a trained target monitoring model to obtain the mechanical strength parameters corresponding to the material to be tested output by the target monitoring model; the mechanical strength parameters include at least one of the hardness and elastic modulus corresponding to the material to be tested.

[0027] According to the material mechanical strength monitoring device provided in the embodiment of the present application, by obtaining the thin film refractive index of the material to be tested and inputting the thin film refractive index into the trained target monitoring model, the mechanical strength parameters output by the target monitoring model can be obtained. There is no need for a dedicated person to perform destructive testing on the material alone, thus achieving non-destructive monitoring and saving a lot of material cost and time cost. Moreover, the mechanical strength parameters of the material can be measured immediately after each adjustment of the process parameters, and the mechanical properties of the material can be monitored in real time. The test error is small, and the accuracy and precision of the calculation results are high.

[0028] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for monitoring the mechanical strength of a material as described in the first aspect above is implemented.

[0029] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the material mechanical strength monitoring method as described in the first aspect above is implemented.

[0030] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the material mechanical strength monitoring method as described in the first aspect above.

[0031] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0032] By obtaining the thin film refractive index of the material to be tested and inputting the thin film refractive index into the trained target monitoring model, the mechanical strength parameters output by the target monitoring model can be obtained. There is no need for a dedicated person to conduct destructive testing on the material alone, thus achieving non-destructive monitoring and saving a lot of material and time costs. Moreover, the mechanical strength parameters of the material can be measured immediately after each adjustment of the process parameters, and the mechanical properties of the material can be monitored in real time. The test error is small, and the accuracy and precision of the calculation results are high.

[0033] Furthermore, by collecting a large amount of sample data and inputting the sample data into the initial fitting model, the initial fitting model is then trained using a target regression algorithm to obtain a target monitoring model. The obtained target monitoring model can effectively characterize the correlation between the refractive index and the mechanical strength, and the trained target monitoring model has high prediction accuracy and precision.

[0034] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0036] Figure 1 It is a schematic diagram of the flow of the mechanical strength monitoring method of the material provided in the embodiment of the present application;

[0037] Figure 2 This is one of the principle schematic diagrams of the mechanical strength monitoring method of the material provided in the embodiment of the present application;

[0038] Figure 3 This is the second principle schematic diagram of the material mechanical strength monitoring method provided in the embodiment of the present application;

[0039] Figure 4 This is the third principle schematic diagram of the material mechanical strength monitoring method provided in the embodiment of the present application;

[0040] Figure 5 This is the fourth principle schematic diagram of the material mechanical strength monitoring method provided in the embodiment of the present application;

[0041] Figure 6 This is the fifth principle schematic diagram of the mechanical strength monitoring method of the material provided in the embodiment of the present application;

[0042] Figure 7 This is the sixth principle schematic diagram of the material mechanical strength monitoring method provided in the embodiment of the present application;

[0043] Figure 8 This is the seventh principle schematic diagram of the material mechanical strength monitoring method provided in the embodiment of the present application;

[0044] Fig. 9 This is the eighth principle schematic diagram of the mechanical strength monitoring method of the material provided in the embodiment of the present application;

[0045] Fig.10 This is the ninth principle schematic diagram of the method for monitoring the mechanical strength of a material provided in an embodiment of the present application;

[0046] Fig.11 This is the tenth principle schematic diagram of the material mechanical strength monitoring method provided in the embodiment of the present application;

[0047] Fig.12 It is a structural schematic diagram of a mechanical strength monitoring device for a material provided in an embodiment of the present application;

[0048] Fig.13 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0050] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0051] In conjunction with the accompanying drawings, the material mechanical strength monitoring method, material mechanical strength monitoring device, electronic device and readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0052] The material mechanical strength monitoring method may be applied to a terminal, and may be specifically executed by hardware or software in the terminal.

[0053] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0054] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse and a joystick.

[0055] The mechanical strength monitoring method of a material provided in the embodiments of the present application may be executed by an electronic device or a functional module or functional entity in the electronic device that can implement the mechanical strength monitoring method of the material. The electronic devices mentioned in the embodiments of the present application include but are not limited to mobile phones, tablet computers, computers, cameras, and wearable devices. The mechanical strength monitoring method of a material provided in the embodiments of the present application is described below using an electronic device as an example of an execution subject.

[0056] like Figure 1 As shown, the material mechanical strength monitoring method includes: step 110 and step 120.

[0057] Step 110, obtaining the film refractive index corresponding to the material to be tested;

[0058] In this step, the material to be tested is a semiconductor material. For example, the material to be tested may include a Low-k material. The Low-k material has a relatively low dielectric constant (K value), which is usually lower than the dielectric constant of a traditional dielectric material (such as silicon dioxide, with a K value of approximately 3.9 to 4.5).

[0059] Low-k materials can be used in the field of microelectronics. For example, they can be used in the manufacture of integrated circuits (ICs). Low-k materials can be used as insulating media between metal interconnects to reduce parasitic capacitance, reduce RC delay, reduce line crosstalk and power dissipation, etc.

[0060] Low-k materials include many types, such as fluorine silicate glass (FSG), carbon-doped silicon oxide (such as Black Diamond), and nitrogen-doped silicon carbide (such as BLOK).

[0061] When the process technology enters 130nm, the Cu / Low-k combination becomes the dominant material. For example, at the 90nm~55nm process nodes, BDI can be used as the main Low-k material with a K value of approximately 2.8~3.3.

[0062] The material to be tested in the present application may include a low dielectric constant material of carbon-doped silicon oxide, such as BlackDiamond.

[0063] The film refractive index is used to characterize the ratio of the speed of light waves when propagating in a semiconductor film to the speed of light waves in a vacuum. The film refractive index reflects the refractive ability of the semiconductor film to light.

[0064] In actual implementation, the thin film refractive index of the material to be tested can be obtained by prism coupling method, ellipsometry, spectral reflectometry or interference microscopy.

[0065] Alternatively, the film refractive index of the material to be tested can be measured by a film thickness meter. When the film thickness meter is used to measure the parameters of the material to be tested, the film thickness of the material to be tested can also be measured.

[0066] Step 120, inputting the film refractive index into the trained target monitoring model, and obtaining the mechanical strength parameter corresponding to the material to be tested output by the target monitoring model;

[0067] In this step, the target monitoring model can predict the mechanical strength parameters based on the film refractive index.

[0068] The target monitoring model can be trained using a large number of thin film samples with known refractive index and mechanical strength parameters.

[0069] For example, a large amount of sample materials can be obtained, and the sample refractive index and sample mechanical strength parameters corresponding to the sample materials can be obtained. Then, the sample refractive index corresponding to the sample material is used as a sample, and the sample mechanical strength parameter corresponding to the sample refractive index is used as a sample label to train a target monitoring model.

[0070] Among them, the target monitoring model can be fitted based on samples and sample labels. For example, linear regression, polynomial regression, nonlinear regression, machine learning algorithms (such as decision trees, random forests or support vector machines, etc.) or deep learning models can be used to fit the target monitoring model. The user can select a suitable fitting model, which is not limited in this application.

[0071] The target monitoring model can output mechanical strength parameters corresponding to the material to be tested based on the input refractive index data, wherein the mechanical strength parameters include at least one of hardness and elastic modulus corresponding to the material to be tested.

[0072] Among them, hardness is used to characterize the ability of the surface of the material to be tested to resist local indentation, and can be used to measure the softness and hardness of the material.

[0073] The hardness of the material to be tested will be different if the composition, crystal structure, atomic distance, processing technology and impurity content of the material to be tested are different.

[0074] The elastic modulus is used to characterize the relationship between stress and strain of the material to be tested during the elastic deformation stage. The elastic modulus reflects the ability of the material to resist elastic deformation when subjected to force.

[0075] When the bond strength between atoms, ions or molecules of the material to be measured is different, the elastic modulus of the material to be measured is also different.

[0076] The mechanical strength parameters of the material to be tested prepared under different process parameters are also different. When the process parameters are adjusted, steps 110 and 120 can be used to monitor the newly prepared material to be tested.

[0077] The inventors have tested and found that when the film refractive index of the material to be tested is 1.4458, the hardness of the material to be tested measured by the indenter is 2.98 Gpa, and the measured elastic modulus is 20.41 Gpa. The hardness of the material to be tested calculated by the target monitoring model provided in this application is 3.01 Gpa, and the calculated elastic modulus is 20.38 Gpa. The calculated values ​​of the target monitoring model are basically consistent with the measured values. The test error of the target monitoring model provided in this application is small, and the accuracy and precision of the calculation results are high.

[0078] According to the mechanical strength monitoring method of materials provided in the embodiments of the present application, by obtaining the thin film refractive index of the material to be tested and inputting the thin film refractive index into a trained target monitoring model, the mechanical strength parameters output by the target monitoring model can be obtained. There is no need for a dedicated person to perform destructive testing on the material alone, thus achieving non-destructive monitoring and saving a lot of material and time costs. Moreover, the mechanical strength parameters of the material can be measured immediately after each adjustment of the process parameters, and the mechanical properties of the material can be monitored in real time. The test error is small, and the accuracy and precision of the calculation results are high.

[0079] In some embodiments, step 120 may include:

[0080] The film refractive index is input into the target monitoring model, and the film refractive index is processed by the target algorithm deployed in the target monitoring model to obtain the mechanical strength parameters output by the target monitoring model after being processed by the target algorithm.

[0081] In this embodiment, the target monitoring model may be a mathematical or computational model, or may be a model based on machine learning, or may be a model based on physical principles.

[0082] A target algorithm may be deployed in the target monitoring model, and the target algorithm may process the film refractive index input to the target monitoring model to obtain corresponding mechanical strength parameters.

[0083] The target algorithm includes the following formula:

[0084] Y=ARI 3 -BRI 2 +CRI-D

[0085] Among them, Y is the mechanical strength parameter, A, B, C and D are the parameters obtained by training the target monitoring model, and RI is the refractive index of the film.

[0086] A, B, C and D may be different when different mechanical strength parameters need to be monitored.

[0087] In some embodiments, the film refractive index is input into the target monitoring model, and the film refractive index is processed by the target algorithm deployed in the target monitoring model to obtain the mechanical strength parameter output by the target monitoring model after the target algorithm is processed, including:

[0088] The film refractive index is input into the first sub-model, and the film refractive index is processed by the first algorithm deployed in the first sub-model to obtain the hardness corresponding to the material to be tested output by the first sub-model after being processed by the first algorithm.

[0089] In this embodiment, the target monitoring model may include a first sub-model, the target algorithm may include a first algorithm, and the first algorithm may be deployed in the first sub-model.

[0090] The first algorithm can process the film refractive index input to the first sub-model to obtain a corresponding hardness parameter.

[0091] Among them, the first sub-model can be trained based on the sample refractive index corresponding to the acquired sample material and the sample hardness corresponding to the sample material. For example, based on the sample refractive index and the sample hardness, a relationship curve between the sample refractive index and the sample hardness can be fitted, that is, the first algorithm can be fitted.

[0092] In some embodiments, the first algorithm may include the following formula:

[0093] H=1e+07RI 3 -6e+07RI 2 +8e+07RI-4e+07

[0094] =1·10 7 ·RI 3 -6.10 7 ·RI 2 +8·10 7 ·RI-4·10 7

[0095] Where H is the hardness, e is the scientific notation symbol, and RI is the film refractive index.

[0096] In this embodiment, the first algorithm can be used to characterize the mathematical relationship between the refractive index and hardness of the film, such as Figure 2 The fitting curve corresponding to the first algorithm obtained by fitting is illustrated.

[0097] A series of thin film sample data with known refractive index and hardness can be collected for training or verifying the first sub-model. For example, a multivariate equation approximate fitting can be used based on the sample refractive index and sample hardness to obtain the first algorithm.

[0098] In some embodiments, the film refractive index is input into the target monitoring model, and the film refractive index is processed by the target algorithm deployed in the target monitoring model to obtain the mechanical strength parameter output by the target monitoring model after the target algorithm is processed, including:

[0099] The film refractive index is input into the second sub-model, and the film refractive index is processed by the second algorithm deployed in the second sub-model to obtain the elastic modulus corresponding to the material to be tested output by the second sub-model after processing by the second algorithm.

[0100] In this embodiment, the target monitoring model may include a second sub-model, the target algorithm may include a second algorithm, and the second algorithm may be deployed in the second sub-model.

[0101] The second algorithm can process the film refractive index input to the second sub-model to obtain the corresponding elastic modulus.

[0102] Among them, the second sub-model can be trained based on the sample refractive index corresponding to the acquired sample material and the sample elastic modulus corresponding to the sample material. For example, based on the sample refractive index and the sample elastic modulus, a relationship curve between the sample refractive index and the sample elastic modulus can be fitted, that is, the second algorithm can be fitted.

[0103] In some embodiments, the second algorithm may include the following formula:

[0104] E=4e+08RI 3 -2e+09RI 2 +3e+09RI-1e+09

[0105] =4·10 8 ·RI 3 -2.10 9 ·RI 2 +3·10 9 ·RI-1·10 9

[0106] Wherein, E is the elastic modulus, e is the scientific notation symbol, and RI is the film refractive index.

[0107] In this embodiment, the second algorithm can be used to characterize the mathematical relationship between the film refractive index and the elastic modulus, such as Figure 3 The fitting curve corresponding to the second algorithm obtained by fitting is illustrated.

[0108] A series of thin film sample data with known refractive index and elastic modulus can be collected for training or verifying the second sub-model. For example, a multivariate equation approximate fitting can be used based on the sample refractive index and sample elastic modulus to obtain the second algorithm.

[0109] In some embodiments, the target monitoring model can be trained based on the following steps:

[0110] Obtaining sample refractive indices corresponding to sample materials of the same category as the material to be tested, and sample mechanical strengths corresponding to the sample refractive indices;

[0111] The sample refractive index and the sample mechanical strength are input into the initial fitting model, and the initial fitting model is trained by using a target regression algorithm;

[0112] The trained initial fitting model is determined as the target monitoring model.

[0113] In this step, the sample material may be of the same type as the material to be tested. For example, sample materials prepared under a variety of different process parameters may be collected. When the process parameters are different, the corresponding refractive index of the sample material is also different.

[0114] In the case where the material to be tested is a BD material, the BD material may be doped or modified, and the doped or modified material may be used as a sample material.

[0115] The refractive index of each sample material can be measured using an optical instrument, and the mechanical strength of each sample material can be measured using a mechanical testing method (such as a tensile test or a compression test, etc.). The mechanical strength is the ability of a material to resist damage from external forces.

[0116] The measured sample refractive index and sample mechanical strength can be sorted to form a data set, in which each sample includes two characteristics: the refractive index and the mechanical strength corresponding to the refractive index.

[0117] Check whether there are outliers or missing values ​​in the data set and handle them accordingly. Outliers may be caused by measurement errors or data recording errors and need to be eliminated or corrected; missing values ​​need to be filled or deleted according to the specific situation.

[0118] A suitable initial fitting model may be selected according to specific requirements and characteristics of the data, wherein the initial fitting model may be a regression model such as linear regression, polynomial regression or support vector machine.

[0119] In cases where the relationship between the data in a data set is nonlinear, it can be approximated by increasing the power, for example, polynomial regression can be determined as the initial fitting model.

[0120] The sorted sample refractive index and sample mechanical strength data are input into the initial fitting model, and then the initial fitting model is trained using a target regression algorithm (such as least squares method or gradient descent method). During the training process, the algorithm will continuously adjust the parameters of the model to minimize the error between the model's predicted results and the actual measurement results.

[0121] The performance of the trained model can then be evaluated using evaluation methods such as cross-validation or hold-out method. Evaluation indicators can be mean squared error (MSE), root mean square error (RMSE) or mean absolute error (MAE).

[0122] If the trained model shows good performance in the evaluation phase, the model can be determined as the target monitoring model, which is capable of predicting the mechanical strength of new materials based on their refractive index data.

[0123] If the model performance is poor, you can try to optimize the model by using a more complex model structure, increasing the amount of data, or adjusting model parameters, etc., which is not limited in this application.

[0124] According to the mechanical strength monitoring method of materials provided in the embodiments of the present application, a large amount of sample data is collected and input into an initial fitting model, and then a target regression algorithm is used to train the initial fitting model to obtain a target monitoring model. The obtained target monitoring model can effectively characterize the correlation between the refractive index and the mechanical strength, and the prediction accuracy and precision of the trained target monitoring model are high.

[0125] In some embodiments, obtaining the sample refractive index corresponding to the sample material of the same category as the material to be tested, and the sample mechanical strength corresponding to each sample refractive index may include:

[0126] Under target conditions, octamethylcyclotetrasiloxane within a first flow rate range and oxygen within a second flow rate range are used as reaction source gases, and a plasma enhanced chemical vapor deposition technology is used to prepare a sample material;

[0127] A film thickness meter is used to obtain the sample refractive index corresponding to the sample material; an indenter is used to obtain the sample mechanical strength corresponding to the sample material.

[0128] In this embodiment, the target conditions may include a target process temperature range, a target pressure range corresponding to the reaction chamber, a target high frequency power range, a target low frequency power range, a target plate spacing range, and a target helium flow range.

[0129] The target process temperature range can be: 400±5℃, which can be used to ensure the smooth progress of chemical reactions and uniform deposition of thin films. When other process parameters remain unchanged and the process temperature increases, the refractive index of the prepared sample material increases, such as Figure 4 The correlation curve between the process temperature and the refractive index of the sample material is illustrated.

[0130] The target pressure range can be: 10±2Torr, which can be used to control the deposition rate and uniformity of the film. At a lower pressure, the collision between molecules is reduced, which is conducive to the directional growth of the film and the reduction of defects. When other process parameters remain unchanged and the pressure increases, the refractive index of the prepared sample material increases, such as Figure 5 The dependence curve between pressure and the refractive index of the sample material is illustrated.

[0131] High-frequency and low-frequency power can provide the energy required for depositing thin films. High-frequency power can be used to excite octamethylcyclotetrasiloxane or promote chemical reactions, and low-frequency power can be used to adjust the deposition rate and morphology of the thin film.

[0132] The target high-frequency power range can be: 400±5W. When other process parameters remain unchanged and the high-frequency power increases, the refractive index of the prepared sample material decreases, such as Figure 6 The dependence curve between the high frequency power and the refractive index of the sample material is illustrated.

[0133] The target low-frequency range can be: 100±5W. When other process parameters remain unchanged and the low-frequency power increases, the refractive index of the prepared sample material decreases, such as Figure 7 The dependence curve between the high frequency power and the refractive index of the sample material is illustrated.

[0134] The target plate spacing range can be: 300±5Mils. The plate spacing will affect the electric field distribution and the deposition position of the film. By adjusting the plate spacing, the thickness uniformity and deposition rate of the film can be controlled. When other process parameters remain unchanged and the plate spacing increases, the refractive index of the prepared sample material decreases, such as Figure 8 The dependence curve between the plate spacing and the refractive index of the sample material is shown.

[0135] The target helium flow rate range can be: 1000±50Sccm. Helium can be used to adjust the reaction atmosphere, promote the oxidation of thin films or as a carrier gas. When other process parameters remain unchanged and the helium flow rate increases, the refractive index of the prepared sample material decreases, such as Fig. 9 The correlation curve between the helium flow rate and the refractive index of the sample material is illustrated.

[0136] The first flow rate range may be: 1600±50 Sccm. When other process parameters remain unchanged, the larger the flow rate of octamethylcyclotetrasiloxane, the smaller the refractive index of the prepared sample material. Fig.10 The correlation curve between the flow rate of octamethylcyclotetrasiloxane and the refractive index of the sample material is illustrated.

[0137] The second flow rate range may be: 150±5 Sccm. When other process parameters remain unchanged, the larger the oxygen flow rate, the smaller the refractive index of the prepared sample material. Fig.11 The correlation curve between oxygen flow rate and the refractive index of the sample material is illustrated.

[0138] PECVD (Plasma Enhanced Chemical Vapor Deposition) is a thin film preparation technology that forms a thin film by decomposing chemical gas substances in the gas phase under plasma excitation and depositing them on the substrate surface.

[0139] Octamethylcyclotetrasiloxane (OMCTS) is used as the source gas. OMCTS decomposes under plasma excitation to generate free radicals and reactants. These reactants undergo chemical reactions on the substrate surface to form the main components of BD Film (sample material or material to be tested).

[0140] Oxygen (O2) can act as an oxidant in the reaction, promoting the chemical reaction between the free radicals generated by the decomposition of OMCTS and forming a more stable film structure.

[0141] The chemical reaction equation of the sample material prepared by plasma enhanced chemical vapor deposition technology using octamethylcyclotetrasiloxane and oxygen as reaction source gases is:

[0142] [(CH3)-Si-O]4+O2→SiOCH+by-products↑

[0143] Wherein, [(CH3)-Si-O]4 is octamethylcyclotetrasiloxane, O2 is oxygen, SiOCH is a compound containing silicon, oxygen and carbon (sample material), and by-products are by-products of chemical reactions.

[0144] After the sample material is prepared, the sample material can be placed in the test sample holder of the film thickness meter, the test wavelength range and step length can be set, and the test program can be started to allow the instrument to measure. For example, the wavelength point with the highest reflectivity can be found in the test results, and its reflectivity value can be recorded. Then, the refractive index value of the material can be calculated based on the Fresnel formula and the optical constant formula of the material.

[0145] Place the sample material on the sample stage of the indenter, set the test parameters of the indenter, such as indentation depth and loading rate, start the test program, and let the indenter perform indentation test on the sample material.

[0146] The force and penetration depth curve during the test can be recorded, and the mechanical strength of the sample material (such as hardness and elastic modulus, etc.) can be calculated based on the curve.

[0147] The refractive index value as well as the mechanical strength value of each sample can be recorded to analyze the correlation between refractive index and mechanical strength.

[0148] The material mechanical strength monitoring device provided in the present application is described below. The material mechanical strength monitoring device described below and the material mechanical strength monitoring method described above can be referenced to each other.

[0149] The material mechanical strength monitoring method provided in the embodiment of the present application can be performed by a material mechanical strength monitoring device. In the embodiment of the present application, the material mechanical strength monitoring method performed by the material mechanical strength monitoring device is used as an example to illustrate the material mechanical strength monitoring device provided in the embodiment of the present application.

[0150] An embodiment of the present application also provides a device for monitoring the mechanical strength of a material.

[0151] like Fig.12 As shown, the material mechanical strength monitoring device includes: a first processing module 1210 and a second processing module 1220.

[0152] The first processing module 1210 is used to obtain the film refractive index corresponding to the material to be tested;

[0153] The second processing module 1220 is used to input the film refractive index into the trained target monitoring model to obtain the mechanical strength parameters corresponding to the material to be tested output by the target monitoring model; the mechanical strength parameters include at least one of the hardness and elastic modulus corresponding to the material to be tested.

[0154] According to the material mechanical strength monitoring device provided in the embodiment of the present application, by obtaining the thin film refractive index of the material to be tested and inputting the thin film refractive index into the trained target monitoring model, the mechanical strength parameters output by the target monitoring model can be obtained. There is no need for a dedicated person to perform destructive testing on the material alone, thus achieving non-destructive monitoring and saving a lot of material cost and time cost. Moreover, the mechanical strength parameters of the material can be measured immediately after each adjustment of the process parameters, and the mechanical properties of the material can be monitored in real time. The test error is small, and the accuracy and precision of the calculation results are high.

[0155] In some embodiments, the second processing module 1220 may also be used to:

[0156] The film refractive index is input into the target monitoring model, and the film refractive index is processed by the target algorithm deployed in the target monitoring model to obtain the mechanical strength parameters output by the target monitoring model after the target algorithm is processed; wherein the target algorithm includes the following formula:

[0157] Y=ARI 3 -BRI 2 +CRI-D

[0158] Among them, Y is the mechanical strength parameter, A, B, C and D are the parameters obtained by training the target monitoring model, and RI is the refractive index of the film.

[0159] In some embodiments, the target monitoring model includes a first sub-model, the target algorithm includes a first algorithm, and the first algorithm is deployed in the first sub-model; the second processing module 1220 can also be used to:

[0160] The film refractive index is input into the first sub-model, and the film refractive index is processed by the first algorithm deployed in the first sub-model to obtain the hardness corresponding to the material to be tested output by the first sub-model after being processed by the first algorithm.

[0161] In some embodiments, the target monitoring model includes a second sub-model, the target algorithm includes a second algorithm, and the second algorithm is deployed in the second sub-model; the second processing module 1220 can also be used to:

[0162] The film refractive index is input into the second sub-model, and the film refractive index is processed by the second algorithm deployed in the second sub-model to obtain the elastic modulus corresponding to the material to be tested output by the second sub-model after processing by the second algorithm.

[0163] In some embodiments, the material mechanical strength monitoring device may further include a third processing module for training and obtaining a target monitoring model based on the following steps:

[0164] Obtaining sample refractive indices corresponding to sample materials of the same category as the material to be tested, and sample mechanical strengths corresponding to the sample refractive indices;

[0165] The sample refractive index and the sample mechanical strength are input into the initial fitting model, and the initial fitting model is trained by using a target regression algorithm;

[0166] The trained initial fitting model is determined as the target monitoring model.

[0167] In some embodiments, the third processing module may also be used to:

[0168] Under target conditions, octamethylcyclotetrasiloxane within a first flow range and oxygen within a second flow range are used as reaction source gases, and a plasma enhanced chemical vapor deposition technique is used to prepare a sample material; wherein the target conditions include a target process temperature range, a target pressure range corresponding to the reaction chamber, a target high-frequency power range, a target plate spacing range, a target low-frequency power range, and a target helium flow range;

[0169] A film thickness meter is used to obtain the sample refractive index corresponding to the sample material; an indenter is used to obtain the sample mechanical strength corresponding to the sample material.

[0170] The mechanical strength monitoring device of the material in the embodiment of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices other than the terminal. Exemplary, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a palmtop computer, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) equipment, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobilepersonal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., and the embodiment of the present application is not specifically limited.

[0171] The mechanical strength monitoring device of the material in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0172] The mechanical strength monitoring device of the material provided in the embodiment of the present application can achieve Figures 1 to 11 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0173] In some embodiments, Fig.13 As shown, an embodiment of the present application also provides an electronic device 1300, including a processor 1301, a memory 1302, and a computer program stored in the memory 1302 and executable on the processor 1301. When the program is executed by the processor 1301, each process of the above-mentioned material mechanical strength monitoring method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0174] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0175] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the various processes of the above-mentioned material mechanical strength monitoring method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0176] On the other hand, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to perform the various processes of the above-mentioned material mechanical strength monitoring method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0177] On the other hand, an embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned material mechanical strength monitoring method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0178] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0179] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0180] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring the mechanical strength of a material, characterized in that: include: Obtain the film refractive index corresponding to the material to be tested; The film refractive index is input into a trained target monitoring model to obtain the mechanical strength parameters corresponding to the material to be tested output by the target monitoring model; the mechanical strength parameters include at least one of the hardness and elastic modulus corresponding to the material to be tested.

2. The method for monitoring the mechanical strength of a material according to claim 1, characterized in that: The step of inputting the film refractive index into a trained target monitoring model to obtain a mechanical strength parameter corresponding to the material to be tested output by the target monitoring model includes: The film refractive index is input into the target monitoring model, and the film refractive index is processed by the target algorithm deployed in the target monitoring model to obtain the mechanical strength parameter output by the target monitoring model after processing by the target algorithm; wherein the target algorithm includes the following formula: Y=ARI 3 -BRI 2 +CRI-D Wherein, Y is the mechanical strength parameter, A, C, C and D are parameters obtained by training the target monitoring model, and RI is the refractive index of the film.

3. The method for monitoring the mechanical strength of a material according to claim 2, characterized in that: The target monitoring model includes a first sub-model, the target algorithm includes a first algorithm, and the first algorithm is deployed in the first sub-model; the thin film refractive index is input into the target monitoring model, the thin film refractive index is processed by the target algorithm deployed in the target monitoring model, and the mechanical strength parameter output by the target monitoring model after the processing by the target algorithm is obtained, including: The film refractive index is input into the first sub-model, and the film refractive index is processed by the first algorithm deployed in the first sub-model to obtain the hardness corresponding to the material to be tested output by the first sub-model after processing by the first algorithm.

4. The method for monitoring the mechanical strength of a material according to claim 2, characterized in that: The target monitoring model includes a second sub-model, the target algorithm includes a second algorithm, and the second algorithm is deployed in the second sub-model; the thin film refractive index is input into the target monitoring model, the thin film refractive index is processed by the target algorithm deployed in the target monitoring model, and the mechanical strength parameter output by the target monitoring model after the processing by the target algorithm is obtained, including: The film refractive index is input into the second sub-model, and the film refractive index is processed by the second algorithm deployed in the second sub-model to obtain the elastic modulus corresponding to the material to be tested output by the second sub-model after processing by the second algorithm.

5. The method for monitoring the mechanical strength of a material according to any one of claims 1 to 4, characterized in that: The target monitoring model is trained based on the following steps: Acquire sample refractive indices corresponding to sample materials of the same category as the material to be tested, and sample mechanical strengths corresponding to the sample refractive indices; Inputting the sample refractive index and the sample mechanical strength into an initial fitting model, and training the initial fitting model using a target regression algorithm; The trained initial fitting model is determined as the target monitoring model.

6. The method for monitoring the mechanical strength of a material according to claim 5, characterized in that: The obtaining of sample refractive indices corresponding to sample materials of the same category as the material to be tested, and sample mechanical strengths corresponding to the sample refractive indices, includes: Under target conditions, octamethylcyclotetrasiloxane within a first flow range and oxygen within a second flow range are used as reaction source gases, and a plasma enhanced chemical vapor deposition technique is used to prepare the sample material; wherein the target conditions include a target process temperature range, a target pressure range corresponding to the reaction chamber, a target high-frequency power range, a target low-frequency power range, a target plate spacing range, and a target helium flow range; A film thickness meter is used to obtain the sample refractive index corresponding to the sample material; an indenter is used to obtain the sample mechanical strength corresponding to the sample material.

7. A device for monitoring the mechanical strength of a material, characterized in that: include: A first processing module is used to obtain the film refractive index corresponding to the material to be tested; The second processing module is used to input the film refractive index into a trained target monitoring model to obtain the mechanical strength parameters corresponding to the material to be tested output by the target monitoring model; the mechanical strength parameters include at least one of the hardness and elastic modulus corresponding to the material to be tested.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for monitoring the mechanical strength of the material as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for monitoring the mechanical strength of a material as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for monitoring the mechanical strength of a material as described in any one of claims 1 to 6 is implemented.

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