Annealing temperature control method for optoelectronic semiconductor silicon wafer

Abstract

The present application relates to the field of temperature control technology, and specifically to an annealing temperature control method applied to optoelectronic semiconductor silicon wafers. The method comprises: preprocessing the temperature data of the optoelectronic semiconductor silicon wafer during the annealing process to obtain a temperature preprocessing data set; obtaining a temperature membership function according to the temperature preprocessing data set; and fuzzy controlling the annealing temperature of the optoelectronic semiconductor silicon wafer according to the temperature membership function. The present application utilizes a fuzzy PID control algorithm to overcome the problem of nonlinear fluctuations in the regulation signal, improve the accuracy and real-time performance of the annealing temperature control of the optoelectronic semiconductor silicon wafer, and thus improve the performance and reliability of semiconductor devices.

Description

Technical Field

[0001] The present application belongs to the field of temperature control technology, and specifically relates to an annealing temperature control method applied to optoelectronic semiconductor silicon wafers. Background Art

[0002] Annealing temperature control of optoelectronic semiconductor silicon wafers is a key link in the semiconductor manufacturing process. Annealing refers to the process of heating semiconductor materials to a certain temperature and maintaining it for a period of time, and then slowly cooling it. It mainly promotes the repair of crystal defects, the diffusion of impurities and the recrystallization of crystals by heating and maintaining a certain temperature. The annealing process can eliminate crystal defects, adjust carrier concentration and mobility. Different annealing temperatures and times will affect the microstructure of the material and its optoelectronic properties. This process is crucial to improving the electrical and mechanical properties of semiconductor materials. Therefore, it is necessary to accurately control the temperature during the annealing process in real time to improve the performance and reliability of semiconductor devices.

[0003] In the prior art, the temperature in the annealing process is usually controlled by a PID control algorithm. According to the change in temperature data, the system is precisely controlled by combining the three control functions of proportion, integration and differentiation. However, in the annealing temperature control of optoelectronic semiconductor silicon wafers, because the temperature fluctuation is not a simple linear change, the traditional annealing temperature control method has poor accuracy and real-time performance when facing nonlinear systems, resulting in the inability to guarantee the performance and reliability of optoelectronic semiconductor silicon wafers. Summary of the invention

[0004] In order to solve the above problems, the present application provides an annealing temperature control method for optoelectronic semiconductor silicon wafers, the method comprising:

[0005] Preprocessing the temperature data of the optoelectronic semiconductor silicon wafer during the annealing process to obtain a temperature preprocessing data set;

[0006] According to the temperature preprocessing data set, obtaining a temperature membership function;

[0007] According to the temperature membership function, the annealing temperature of the optoelectronic semiconductor silicon wafer is fuzzy controlled.

[0008] Optionally, obtaining the temperature membership function includes:

[0009] Obtaining a fuzzy set according to the temperature preprocessing data set;

[0010] Clustering the fuzzy sets to obtain multiple clusters;

[0011] According to the plurality of clusters, acquiring characteristic values ​​of data points in the clusters;

[0012] According to the characteristic value, obtaining the abnormality of the data point in the cluster;

[0013] According to the abnormality degree, obtaining the data stability of the cluster;

[0014] According to the data stability, a steady-state output of the fuzzy control system is obtained;

[0015] The temperature membership function is obtained according to the steady-state output.

[0016] Optionally, obtaining the fuzzy set includes:

[0017] Get the difference between any two adjacent temperature data points in the temperature preprocessing data set;

[0018] The fuzzy set is obtained according to the difference between any two adjacent temperature data points, and the fuzzy set represents the temperature change.

[0019] Optionally, obtaining a plurality of clusters includes:

[0020] A plane rectangular coordinate system is constructed with the temperature amplitude of each data point as the horizontal coordinate and the temperature change amount of the data point as the vertical coordinate, wherein the temperature difference between the current data point and the previous data point is the temperature change amount of the current data point;

[0021] The DBSCAN density clustering algorithm is used to cluster the data points in the coordinate system, the clustering radius is set to A, the minimum cluster number is set to B, and multiple clusters of all the data points in the coordinate system are obtained.

[0022] Optionally, obtaining characteristic values ​​of data points in the cluster includes:

[0023] Obtaining the temperature amplitude of the data point in the cluster;

[0024] Obtaining the temperature change of the data points in the cluster;

[0025] Acquire characteristic values ​​of data points in the cluster according to the temperature amplitude and the temperature change.

[0026] Optionally, obtaining the abnormality of the data points in the cluster includes:

[0027] Obtaining the mean of the characteristic values ​​of all data points in the cluster;

[0028] The abnormality of the data points in the cluster is obtained according to the eigenvalue and the mean of the eigenvalue.

[0029] Optionally, obtaining the data stability of the cluster includes:

[0030] Obtaining the maximum value of the temperature amplitude in the cluster;

[0031] Obtaining the maximum value of the temperature variation in the cluster;

[0032] Obtaining the mean of the abnormality of the data points in the cluster;

[0033] The data stability of the cluster is obtained according to the maximum value of the temperature amplitude, the maximum value of the temperature change and the mean value of the abnormality.

[0034] Optionally, the obtaining of the steady-state output of the fuzzy control system includes:

[0035] Obtaining a maximum value of the data stability;

[0036] Obtaining a minimum value of the data stability;

[0037] Obtaining the variance of the data stability;

[0038] A steady-state output of a fuzzy control system is obtained according to the maximum value of the data stability, the minimum value of the data stability and the variance of the data stability.

[0039] Optionally, the acquiring the temperature membership function includes:

[0040] Obtaining a temperature error, where the temperature error is the difference between the fuzzy control target temperature and the actual temperature;

[0041] Obtaining the variance of temperature in the temperature preprocessing data set;

[0042] The temperature membership function is obtained according to the steady-state output, the temperature error and the temperature variance.

[0043] Optionally, the fuzzy control of the annealing temperature of the optoelectronic semiconductor silicon wafer according to the temperature membership function includes:

[0044] Inputting the temperature error into the temperature membership function to obtain a fuzzy value, wherein the temperature error is the difference between the fuzzy control target temperature and the actual temperature;

[0045] According to the fuzzy value, a fuzzy rule base is used to perform reasoning to obtain a fuzzy control quantity, wherein the fuzzy rule base includes preset fuzzy reasoning rules;

[0046] Defuzzifying the fuzzy control quantity to obtain a target PID control quantity;

[0047] According to the target PID control amount, the heater output power is adjusted to achieve fuzzy control of the annealing temperature of the optoelectronic semiconductor silicon wafer.

[0048] This application has the following beneficial effects:

[0049] The present application provides an annealing temperature control method for optoelectronic semiconductor silicon wafers, the method comprising: preprocessing the temperature data of the optoelectronic semiconductor silicon wafers during the annealing process to obtain a temperature preprocessing data set; obtaining a temperature membership function according to the temperature preprocessing data set; and fuzzy controlling the annealing temperature of the optoelectronic semiconductor silicon wafer according to the temperature membership function. The present application utilizes a fuzzy PID control algorithm to overcome the problem of nonlinear fluctuations in the regulation signal, improve the accuracy and real-time performance of the annealing temperature control of the optoelectronic semiconductor silicon wafers, and thus improve the performance and reliability of semiconductor devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the implementation scheme of the present application, the drawings required for use in the implementation scheme will be briefly introduced below. It should be understood that the drawings only show certain implementation schemes of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on the drawings without paying creative work.

[0051] Figure 1 The present invention is a flow chart showing a method for controlling annealing temperature of a photoelectric semiconductor silicon wafer according to an exemplary embodiment.

[0052] Figure 2 The figure is a flow chart showing a method for obtaining a temperature membership function according to an exemplary embodiment.

[0053] Figure 3 The present invention is a flowchart of a method for obtaining a fuzzy set according to an exemplary embodiment.

[0054] Figure 4 The figure is a flowchart of a method for obtaining multiple clusters according to an exemplary embodiment.

[0055] Figure 5 The figure is a flow chart showing a method for obtaining characteristic values ​​of data points in a cluster according to an exemplary embodiment.

[0056] Figure 6 The figure is a flow chart showing a method for obtaining the abnormality of data points in a cluster according to an exemplary embodiment.

[0057] Figure 7 The diagram is a schematic diagram of cluster data distribution according to an exemplary embodiment.

[0058] Figure 8 The invention is a flowchart of a method for obtaining data stability of clusters according to an exemplary embodiment.

[0059] Fig. 9 The invention is a flow chart showing a method for obtaining a steady-state output of a fuzzy control system according to an exemplary embodiment.

[0060] Fig.10 The flowchart of another method for obtaining a temperature membership function is shown according to an exemplary embodiment.

[0061] Fig.11 The present invention is a flow chart showing a method for fuzzy controlling the annealing temperature of a photoelectric semiconductor silicon wafer according to a temperature membership function according to an exemplary embodiment. DETAILED DESCRIPTION

[0062] In order to clearly illustrate the technical features of the present solution, the present application is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0063] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0064] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0065] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0066] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0067] It should be noted that the modifications of "one" and "multiple" mentioned in this application are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more". In the description of this application, unless otherwise specified, "multiple" means two or more than two, and other quantifiers are similar; "at least one item", "one or more items" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one item a can represent any number of a; for another example, one or more items of a, b and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple; "and / or" is a kind of association relationship that describes the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural.

[0068] Although the operations or steps are described in a specific order in the drawings in the embodiments of the present application, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or to perform all the operations or steps shown to obtain the desired results. In the embodiments of the present application, these operations or steps can be performed in series; these operations or steps can also be performed in parallel; or some of these operations or steps can be performed.

[0069] At the same time, it is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0070] First, the application scenario of this application is explained. Photoelectric semiconductor silicon wafers are mainly used for crystalline silicon solar cells. In the production process of crystalline silicon solar cells, high-temperature annealing can optimize the lattice structure on the surface of the photoelectric semiconductor silicon wafer through high-temperature treatment, thereby improving the transmittance and conductivity of the crystalline silicon solar cell after ion implantation. Crystalline silicon solar cells that have undergone high-temperature annealing have higher performance and higher photoelectric conversion stability than ordinary solar cells.

[0071] The principle of high temperature annealing process of solar cells is to change the internal structure of the cell through high temperature treatment, so that the surface of solar photovoltaic semiconductor silicon wafer is more conducive to light absorption and increase the light transmittance of the cell. During the annealing process, the surface of the photovoltaic semiconductor silicon wafer absorbs heat, causing the cell structure to change, thus affecting the electrical and mechanical properties of the cell. In the process of silicon wafer annealing, precise temperature control is crucial. This is because the annealing temperature directly affects the crystal structure and electrical characteristics of the silicon wafer, which in turn determines the performance and reliability of the semiconductor device. Accurate temperature control can ensure uniform diffusion of impurities during annealing, thereby optimizing carrier concentration and mobility, and improving the switching speed and overall performance of the device. In addition, proper temperature gradient control can prevent silicon wafer deformation or cracks caused by thermal stress, ensuring the stability and yield of the manufacturing process.

[0072] Before starting the annealing process, the surface of the optoelectronic semiconductor silicon wafer needs to be cleaned first. In order to prevent the surface of the optoelectronic semiconductor silicon wafer from being damaged, a solution that is not too strong should be used during the cleaning process, and the optoelectronic semiconductor silicon wafer should be placed in a light-shielding bag to prevent it from being exposed to light and affecting the performance of the optoelectronic semiconductor silicon wafer. Rapid thermal annealing is commonly used in the annealing process of solar cells. Rapid thermal annealing uses rapid heating technology to heat the optoelectronic semiconductor silicon wafer to a high temperature in a short time and then cool it quickly. The control of time and temperature during annealing is crucial. Depending on the different optoelectronic semiconductor silicon wafers, the choice of temperature and time will also be different. Generally speaking, the commonly used temperature is controlled at 400-600℃, and the time is controlled at about 5-20 minutes. In addition, annealing requires precise control of temperature and time to ensure the performance of solar cells.

[0073] The main purpose of the present invention is to use a fuzzy PID control algorithm to accurately control the temperature of an optoelectronic semiconductor silicon wafer during the annealing process. When the temperature in the annealing process is controlled by the fuzzy PID control algorithm, because the temperature fluctuation is not a simple linear change, the fuzzy PID control algorithm can overcome the problem of nonlinearity of the adjustment signal, and does not require a complex parameter adjustment process, but relies on fuzzy rules, which reduces the difficulty of system debugging. When the temperature is controlled and adjusted by the fuzzy PID control algorithm, it is necessary to determine a suitable temperature membership function to control the adjustment amplitude and temperature change degree of the temperature when adjusting, so that the system will not be abnormal in the temperature adjustment when the temperature changes greatly, thereby affecting the performance and stability of the optoelectronic semiconductor silicon wafer. The present application is described below in conjunction with specific embodiments.

[0074] Figure 1 FIG. 1 is a flow chart showing a method for controlling annealing temperature of a photoelectric semiconductor silicon wafer according to an exemplary embodiment. Figure 1As shown, the embodiment of the present application provides an annealing temperature control method for optoelectronic semiconductor silicon wafers, and the method may include the following steps:

[0075] In step S10, the temperature data of the optoelectronic semiconductor silicon wafer during the annealing process is preprocessed to obtain a temperature preprocessing data set.

[0076] In this step, the temperature data of the optoelectronic semiconductor silicon wafer during the annealing process is preprocessed to obtain a temperature preprocessing data set. For example, the real-time temperature data of the optoelectronic semiconductor silicon wafer during the annealing process can be sampled at a certain sampling frequency to obtain an annealing temperature time series data sequence, i.e., a temperature preprocessing data set.

[0077] In step S20, a temperature membership function is obtained according to the temperature preprocessing data set.

[0078] In this step, the temperature membership function is obtained according to the temperature preprocessing data set. Exemplarily, a fuzzy set can be obtained according to the temperature preprocessing data set, and then the fuzzy set can be clustered to obtain multiple clusters, and then the characteristic values ​​of the data points in the clusters are obtained according to the multiple clusters, and then the abnormality of the data points in the clusters is obtained according to the characteristic values, and then the data stability of the clusters is obtained according to the abnormality, and then the steady-state output of the fuzzy control system is obtained according to the data stability, and finally the temperature membership function is obtained according to the steady-state output.

[0079] In step S30, the annealing temperature of the optoelectronic semiconductor silicon wafer is fuzzy controlled according to the temperature membership function.

[0080] In this step, the annealing temperature of the optoelectronic semiconductor silicon wafer is fuzzily controlled according to the temperature membership function. Exemplarily, the temperature error can be first input into the temperature membership function to obtain a fuzzy value, wherein the temperature error is the difference between the fuzzy control target temperature and the actual temperature, and then the fuzzy value is used to perform reasoning using a fuzzy rule base to obtain a fuzzy control amount, wherein the fuzzy rule base includes preset fuzzy reasoning rules, and then the fuzzy control amount is defuzzified to obtain a target PID control amount, and finally, according to the target PID control amount, the heater output power is adjusted to achieve fuzzy control of the annealing temperature of the optoelectronic semiconductor silicon wafer.

[0081] In summary, the embodiment of the present application provides an annealing temperature control method for optoelectronic semiconductor silicon wafers, the method comprising: preprocessing the temperature data of the optoelectronic semiconductor silicon wafer during the annealing process to obtain a temperature preprocessing data set; obtaining a temperature membership function according to the temperature preprocessing data set; and fuzzy controlling the annealing temperature of the optoelectronic semiconductor silicon wafer according to the temperature membership function. The embodiment of the present application utilizes a fuzzy PID control algorithm to overcome the problem of nonlinear fluctuations in the adjustment signal, improve the accuracy and real-time performance of the annealing temperature control of the optoelectronic semiconductor silicon wafer, and thus improve the performance and reliability of semiconductor devices.

[0082] Figure 2 FIG. 1 is a flow chart showing a method for obtaining a temperature membership function according to an exemplary embodiment. Figure 2 As shown, the acquisition of the temperature membership function may include the following steps:

[0083] In step S201, a fuzzy set is obtained according to the temperature preprocessing data set.

[0084] In this step, a fuzzy set is obtained according to the temperature preprocessing data set. Exemplarily, the difference between any two adjacent temperature data points in the temperature preprocessing data set can be obtained first, and then a fuzzy set is obtained according to the difference between any two adjacent temperature data points, and the fuzzy set represents the temperature change.

[0085] In step S202, clustering is performed on the fuzzy sets to obtain a plurality of clusters.

[0086] In this step, the fuzzy sets are clustered to obtain multiple clusters. For example, the temperature amplitude of each data point in the temperature preprocessing data set can be used as the horizontal coordinate, and the temperature change of the data point can be used as the vertical coordinate to construct a plane rectangular coordinate system, wherein the temperature difference between the current data point and the previous data point is the temperature change of the current data point, and then the DBSCAN density clustering algorithm is used to cluster the data points in the coordinate system, and the cluster radius is set to A, and the minimum number of clusters is set to B, to obtain multiple clusters of all data points in the coordinate system. For example, A can be set to 3, and B can be set to 4.

[0087] In step S203, characteristic values ​​of data points in the clusters are obtained according to the plurality of clusters.

[0088] In this step, according to the multiple clusters, the characteristic values ​​of the data points in the clusters are obtained. Exemplarily, the temperature amplitude of the data points in the clusters can be obtained first, and then the temperature change of the data points in the clusters can be obtained, and then the characteristic values ​​of the data points in the clusters can be obtained according to the temperature amplitude and the temperature change. The characteristic value represents the distance from the data point to the origin of the plane rectangular coordinate system.

[0089] In step S204, the abnormality of the data points in the cluster is obtained according to the characteristic value.

[0090] In this step, the abnormality of the data point in the cluster is obtained according to the eigenvalue. Exemplarily, the mean of the eigenvalues ​​of all the data points in the cluster can be obtained first, and then the abnormality of the data point in the cluster can be obtained according to the eigenvalue and the mean of the eigenvalue.

[0091] In step S205, the data stability of the cluster is obtained according to the abnormality.

[0092] In this step, the data stability of the cluster is obtained according to the abnormality. For example, the maximum value of the temperature amplitude in the cluster can be obtained first, the maximum value of the temperature change in the cluster can be obtained, and then the mean value of the abnormality of the data points in the cluster can be obtained. Finally, the data stability of the cluster can be obtained according to the maximum value of the temperature amplitude, the maximum value of the temperature change and the mean value of the abnormality.

[0093] In step S206, the steady-state output of the fuzzy control system is obtained according to the data stability.

[0094] In this step, the steady-state output of the fuzzy control system is obtained according to the data stability. Exemplarily, the maximum value of the data stability can be obtained first, then the minimum value of the data stability can be obtained, and then the variance of the data stability can be obtained, and finally the steady-state output of the fuzzy control system can be obtained according to the maximum value of the data stability, the minimum value of the data stability and the variance of the data stability.

[0095] In step S207, the temperature membership function is obtained according to the steady-state output.

[0096] In this step, the temperature membership function is obtained according to the steady-state output. Exemplarily, the temperature error can be obtained first, which is the difference between the fuzzy control target temperature and the actual temperature, and then the variance of the temperature in the temperature preprocessing data set is obtained, and then the temperature membership function is obtained according to the steady-state output, the temperature error and the variance of the temperature.

[0097] Figure 3 FIG. 1 is a flow chart showing a method for obtaining a fuzzy set according to an exemplary embodiment. Figure 3 As shown, the obtaining of the fuzzy set may include the following steps:

[0098] In step S2011, the difference between any two adjacent temperature data points in the temperature preprocessing data set is obtained.

[0099] In this step, the temperature difference between any two adjacent temperature data points in the temperature preprocessing data set is obtained.

[0100] In step S2012, the fuzzy set is obtained according to the difference between any two adjacent temperature data points, and the fuzzy set represents the temperature variation.

[0101] In this step, the data set consisting of the difference between any two adjacent temperature data points is taken as a fuzzy set, which represents the temperature change.

[0102] Figure 4 FIG. 1 is a flow chart showing a method for obtaining multiple clusters according to an exemplary embodiment. Figure 4 As shown, the obtaining of multiple clusters may include the following steps:

[0103] In step S2021, a plane rectangular coordinate system is constructed with the temperature amplitude of each data point as the horizontal coordinate and the temperature change of the data point as the vertical coordinate, wherein the temperature difference between the current data point and the previous data point is the temperature change of the current data point.

[0104] In this step, a plane rectangular coordinate system is constructed with the temperature amplitude of each data point in the temperature preprocessing data set as the horizontal coordinate and the temperature change of the data point as the vertical coordinate, wherein the temperature difference between the current data point and the previous data point is the temperature change of the current data point.

[0105] In step S2022, the DBSCAN density clustering algorithm is used to cluster the data points in the coordinate system, the clustering radius is set to A, the minimum cluster number is set to B, and multiple clusters of all data points in the coordinate system are obtained.

[0106] In this step, the DBSCAN density clustering algorithm is used to cluster the data points in the coordinate system, and the clustering radius is set to A and the minimum cluster number is set to B to obtain multiple clusters of all data points in the coordinate system. Exemplarily, the clustering radius A can be set to 3 and the minimum cluster number B can be set to 4.

[0107] Figure 5 FIG. 1 is a flow chart showing a method for obtaining characteristic values ​​of data points in a cluster according to an exemplary embodiment. Figure 5 As shown, the step of obtaining the characteristic values ​​of the data points in the cluster may include the following steps:

[0108] In step S2031, the temperature amplitude of the data point in the cluster is obtained.

[0109] In this step, we obtain the clusters Medium data point Temperature amplitude .

[0110] In step S2032, the temperature variation of the data points in the cluster is obtained.

[0111] In this step, we obtain the clusters Medium data point Temperature change .

[0112] In step S2033, characteristic values ​​of the data points in the cluster are obtained according to the temperature amplitude and the temperature change.

[0113] In this step, according to the temperature amplitude and temperature change , get the clustering clusters Medium data point The eigenvalue of . For example, cluster Medium data point The eigenvalue of It can be obtained by the following formula:

[0114] Formula 1

[0115] in, It represents the distribution distance of the data points. The farther the distance from the origin of the coordinate system, the larger the temperature value or the greater the temperature change. Therefore, the distribution distance is used to represent the characteristic value of each data point. The larger the value, the greater the abnormality of the temperature data.

[0116] Figure 6 FIG. 1 is a flow chart showing a method for obtaining the abnormality of a data point in a cluster according to an exemplary embodiment. Figure 6 As shown, the step of obtaining the abnormality of the data points in the cluster may include the following steps:

[0117] In step S2041, the mean of the characteristic values ​​of all data points in the cluster is obtained.

[0118] In this step, we obtain the clusters The mean of the eigenvalues ​​of all data points in .

[0119] In step S2042, the abnormality of the data point in the cluster is obtained according to the eigenvalue and the mean of the eigenvalue.

[0120] In this step, according to the eigenvalue and the mean of the eigenvalues , get the clustering clusters Medium data point Abnormality . For example, cluster Medium data point Abnormality It can be obtained by the following formula:

[0121] Formula 2

[0122] in, For normalization processing.

[0123] Because the eigenvalue of each data point can represent its abnormal size in the overall data, It can represent the abnormality of each data point in different clusters. It should be noted that in Formula 2 The value is not 0. When it is 0, replace it with 0.1.

[0124] Select data points with similar abnormalities as one class, and set the error range to , the data points whose distance is within this error range are regarded as a class of data.

[0125] Figure 7 FIG. 1 is a schematic diagram of cluster data distribution according to an exemplary embodiment. Figure 7 As shown in the figure, during the annealing process, the silicon wafer will first be heated to the preset temperature, and the temperature will rise sharply during this process; when heated to the preset temperature, the temperature is in a fluctuating change, so when the temperature amplitude is large, but the temperature change degree of adjacent data points is small, the data points will be more inclined to the lower right of the coordinate system; on the contrary, when the temperature amplitude is small, but the temperature change degree of adjacent data points is large, the data points will be inclined to the upper left of the coordinate system. Because the temperature fluctuation in the temperature preheating stage is normal, it cannot be used as a regulating factor for temperature control and needs to be removed.

[0126] Figure 8 FIG. 1 is a flow chart showing a method for obtaining data stability of a cluster according to an exemplary embodiment. Figure 8 As shown, the obtaining of the data stability of the cluster may include the following steps:

[0127] In step S2051, the maximum value of the temperature amplitude in the cluster is obtained.

[0128] In this step, we obtain the clusters The maximum value of the medium temperature amplitude .

[0129] In step S2052, the maximum value of the temperature variation in the cluster is obtained.

[0130] In this step, we obtain the clusters The maximum value of the temperature change .

[0131] In step S2053, the mean value of the abnormality of the data points in the cluster is obtained.

[0132] In this step, we obtain the clusters The mean of the abnormality of the data points in .

[0133] In step S2054, the data stability of the cluster is obtained according to the maximum value of the temperature amplitude, the maximum value of the temperature change and the mean value of the abnormality.

[0134] In this step, the maximum value of the temperature amplitude is , the maximum value of temperature change and the mean of the abnormality , get the clustering clusters Data stability . For example, cluster Data stability It can be obtained by the following formula:

[0135] Formula 3

[0136] in, Indicates The maximum value of the temperature amplitude and temperature change in the clusters is not 0.

[0137] Indicates that in the cluster The ratio of the sum of the maximum temperature amplitude and the maximum temperature change to the temperature amplitude or the maximum temperature change. The larger the ratio is, the more likely the cluster is to be located in the upper left corner or lower right corner of the coordinate system of the cluster data distribution diagram, so the data in the cluster is more stable.

[0138] Fig. 9 FIG. 1 is a flow chart showing a method for obtaining a steady-state output of a fuzzy control system according to an exemplary embodiment. Fig. 9 As shown, the acquisition of the steady-state output of the fuzzy control system may include the following steps:

[0139] In step S2061, the maximum value of the data stability is obtained.

[0140] In this step, the maximum value of all data stability is obtained .

[0141] In step S2062, the minimum value of the data stability is obtained.

[0142] In this step, the minimum value of all data stability is obtained .

[0143] In step S2063, the variance of the data stability is obtained.

[0144] In this step, the variance of the stability of all data is obtained .

[0145] In step S2064, a steady-state output of the fuzzy control system is obtained according to the maximum value of the data stability, the minimum value of the data stability and the variance of the data stability.

[0146] In this step, according to the maximum value of data stability , minimum value of data stability and the variance of data stability , get the steady-state output of the fuzzy control system For example, the steady-state output of the fuzzy control system is It can be obtained by the following formula:

[0147] Formula 4

[0148] in, It represents the difference in data stability among different clusters. The greater the difference, the greater the performance difference of data in different clusters, so the more unstable the steady-state output. The variance of data stability represents the volatility of data changes. The greater the volatility, the more likely it is that there will be anomalies. Therefore, a greater degree of regulation is required during regulation.

[0149] Fig.10 FIG. 1 is a flow chart showing another method for obtaining a temperature membership function according to an exemplary embodiment. Fig.10 As shown, the obtaining of the temperature membership function may include the following steps:

[0150] In step S2071, a temperature error is obtained, where the temperature error is the difference between the fuzzy control target temperature and the actual temperature.

[0151] In this step, the temperature error is obtained , temperature error is the difference between the fuzzy control target temperature and the actual temperature. For example, if the target temperature is 510°C and the actual temperature is 490°C, the temperature error is is 510°C -490°C = 20°C.

[0152] In step S2072, the variance of the temperature in the temperature preprocessing data set is obtained.

[0153] In this step, the variance of the temperature in the temperature preprocessing dataset is obtained .

[0154] In step S2073, the temperature membership function is obtained according to the steady-state output, the temperature error and the temperature variance.

[0155] In this step, according to the steady-state output , Temperature error and the variance of temperature , get the temperature membership function . Exemplarily, the temperature membership function It can be obtained by the following formula:

[0156] Formula 5

[0157] The temperature membership function is used to limit the difference between normal and abnormal temperature changes during the temperature control process, so that the temperature control will not be out of control due to the large difference between abnormal and normal temperatures. This improves the accuracy and stability of temperature control.

[0158] Fig.11 FIG. 1 is a flow chart showing a method for fuzzy controlling the annealing temperature of a photoelectric semiconductor silicon wafer according to a temperature membership function according to an exemplary embodiment. Fig.11 As shown, the fuzzy control of the annealing temperature of the optoelectronic semiconductor silicon wafer according to the temperature membership function may include the following steps:

[0159] In step S301, the temperature error is input into the temperature membership function to obtain a fuzzy value, wherein the temperature error is the difference between the fuzzy control target temperature and the actual temperature.

[0160] In this step, the temperature error Input temperature membership function , get the fuzzy value , where the temperature error is the difference between the fuzzy control target temperature and the actual temperature.

[0161] In step S302, a fuzzy control quantity is obtained by performing reasoning based on the fuzzy value using a fuzzy rule base, wherein the fuzzy rule base includes preset fuzzy reasoning rules.

[0162] In this step, according to the fuzzy value , use the fuzzy rule base to reason and obtain the fuzzy control quantity , wherein the fuzzy rule base includes preset fuzzy inference rules. Exemplarily, the fuzzy inference rules may include "if the temperature error is positive and large, then the control amount should be increased"; "if the temperature error is negative and small, then the control amount should be reduced".

[0163] In step S303, the fuzzy control amount is defuzzified to obtain a target PID control amount.

[0164] In this step, the fuzzy control quantity Defuzzification is performed to obtain the target PID control quantity. For example, the fuzzy control quantity The defuzzification process may select the maximum value of the membership function in the fuzzy output as the result of the defuzzification. For example, the target PID control amount may be to increase the heater output power by 10%.

[0165] In step S304, the heater output power is adjusted according to the target PID control amount to implement fuzzy control of the annealing temperature of the optoelectronic semiconductor silicon wafer.

[0166] In this step, the heater output power is adjusted according to the target PID control quantity to realize the fuzzy control of the annealing temperature of the optoelectronic semiconductor silicon wafer.

[0167] In summary, the embodiment of the present application provides an annealing temperature control method for optoelectronic semiconductor silicon wafers, the method comprising: preprocessing the temperature data of the optoelectronic semiconductor silicon wafer during the annealing process to obtain a temperature preprocessing data set; obtaining a temperature membership function according to the temperature preprocessing data set; and fuzzy controlling the annealing temperature of the optoelectronic semiconductor silicon wafer according to the temperature membership function. The embodiment of the present application utilizes a fuzzy PID control algorithm to overcome the problem of nonlinear fluctuations in the adjustment signal, improve the accuracy and real-time performance of the annealing temperature control of the optoelectronic semiconductor silicon wafer, and thus improve the performance and reliability of semiconductor devices.

[0168] In an exemplary embodiment, the present application also provides a computer-readable storage medium having computer program instructions stored thereon, and when the program instructions are executed by a processor, the steps of the annealing temperature control method for optoelectronic semiconductor silicon wafers provided in the present application are implemented.

[0169] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable electronic device, and the computer program has a code portion for executing the above-mentioned annealing temperature control method applied to optoelectronic semiconductor silicon wafers when executed by the programmable electronic device.

[0170] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for controlling the annealing temperature of a photoelectric semiconductor silicon wafer, characterized in that: The method comprises: Preprocessing the temperature data of the optoelectronic semiconductor silicon wafer during the annealing process to obtain a temperature preprocessing data set; According to the temperature preprocessing data set, obtaining a temperature membership function; According to the temperature membership function, fuzzy control is performed on the annealing temperature of the optoelectronic semiconductor silicon wafer; The step of obtaining the temperature membership function comprises: Obtaining a fuzzy set according to the temperature preprocessing data set; Clustering the fuzzy sets to obtain multiple clusters; According to the plurality of clusters, acquiring characteristic values ​​of data points in the clusters; According to the characteristic value, obtaining the abnormality of the data point in the cluster; According to the abnormality degree, obtaining the data stability of the cluster; According to the data stability, a steady-state output of the fuzzy control system is obtained; According to the steady-state output, obtaining the temperature membership function; The obtaining of characteristic values ​​of data points in the cluster includes: Obtaining the temperature amplitude of the data point in the cluster; Obtaining the temperature change of the data points in the cluster; Acquire characteristic values ​​of data points in the cluster according to the temperature amplitude and the temperature change; The fuzzy control of the annealing temperature of the optoelectronic semiconductor silicon wafer according to the temperature membership function comprises: Inputting the temperature error into the temperature membership function to obtain a fuzzy value, wherein the temperature error is the difference between the fuzzy control target temperature and the actual temperature; According to the fuzzy value, a fuzzy rule base is used to perform reasoning to obtain a fuzzy control quantity, wherein the fuzzy rule base includes preset fuzzy reasoning rules; Defuzzifying the fuzzy control quantity to obtain a target PID control quantity; According to the target PID control amount, the heater output power is adjusted to achieve fuzzy control of the annealing temperature of the optoelectronic semiconductor silicon wafer; The obtaining of the temperature membership function comprises: Obtaining a temperature error, where the temperature error is the difference between the fuzzy control target temperature and the actual temperature; Obtaining the variance of temperature in the temperature preprocessing data set; The temperature membership function is obtained according to the steady-state output, the temperature error and the temperature variance; wherein the temperature membership function is obtained by a formula including: ;in, is the steady-state output; is the temperature error; is the variance of the temperature.

2. The annealing temperature control method for optoelectronic semiconductor silicon wafers according to claim 1, characterized in that: The obtaining of the fuzzy set comprises: Get the difference between any two adjacent temperature data points in the temperature preprocessing data set; The fuzzy set is obtained according to the difference between any two adjacent temperature data points, and the fuzzy set represents the temperature change.

3. The annealing temperature control method for optoelectronic semiconductor silicon wafer according to claim 1, characterized in that: The obtaining of multiple clusters comprises: A plane rectangular coordinate system is constructed with the temperature amplitude of each data point as the horizontal coordinate and the temperature change amount of the data point as the vertical coordinate, wherein the temperature difference between the current data point and the previous data point is the temperature change amount of the current data point; The DBSCAN density clustering algorithm is used to cluster the data points in the coordinate system, the clustering radius is set to A, the minimum cluster number is set to B, and multiple clusters of all the data points in the coordinate system are obtained.

4. The annealing temperature control method for optoelectronic semiconductor silicon wafers according to claim 1, characterized in that: The obtaining of the abnormality of the data points in the cluster comprises: Obtaining the mean of the characteristic values ​​of all data points in the cluster; The abnormality of the data points in the cluster is obtained according to the eigenvalue and the mean of the eigenvalue.

5. The annealing temperature control method for optoelectronic semiconductor silicon wafers according to claim 1, characterized in that: The obtaining of the data stability of the cluster includes: Obtaining the maximum value of the temperature amplitude in the cluster; Obtaining the maximum value of the temperature variation in the cluster; Obtaining the mean of the abnormality of the data points in the cluster; The data stability of the cluster is obtained according to the maximum value of the temperature amplitude, the maximum value of the temperature change and the mean value of the abnormality.

6. The annealing temperature control method for optoelectronic semiconductor silicon wafers according to claim 1, characterized in that: The step of obtaining the steady-state output of the fuzzy control system comprises: Obtaining a maximum value of the data stability; Obtaining a minimum value of the data stability; Obtaining the variance of the data stability; A steady-state output of a fuzzy control system is obtained according to the maximum value of the data stability, the minimum value of the data stability and the variance of the data stability.

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