Temperature detection method for multi-sensor fusion in reaction chamber of atomic layer deposition equipment

Abstract

The invention provides a temperature detection method for multi-sensor fusion in a reaction chamber of atomic layer deposition equipment, and the method is applied to the technical field of general control and regulation systems, and comprises the steps: obtaining actual measurement values obtained by a plurality of temperature sensors at different positions in the reaction chamber and the number of the sensors; and local Kalman filtering is carried out in combination with a proposed distributed Kalman filtering algorithm, so that the influence caused by noise is reduced, and local temperature values corresponding to the temperature sensors are obtained. A preset optimal linear unbiased estimation algorithm is adopted to carry out fusion processing on the local temperature values, and a fusion temperature value, namely a target temperature value, capable of accurately reflecting the real temperature in the cavity can be obtained. Finally, the temperature value is adopted for temperature control, numerical value loss caused by a single sensor fault in a traditional method can be dealt with, the influence of measurement noise and process noise on temperature measurement precision is reduced, and a guarantee is provided for subsequent reaction chamber temperature control.

Description

Technical Field

[0001] The present application relates to the technical field of general control and regulation systems, and in particular to a temperature detection method of multi-sensor fusion in a reaction chamber of an atomic layer deposition device. Background Art

[0002] ALD (Atomic Layer Deposition) is a common process in the semiconductor field and is used for semiconductor processing. The enclosed space used to achieve atomic layer deposition is the atomic layer deposition reaction chamber, also called the process chamber. ALD atomic layer deposition technology is a thin film deposition technology that specifically injects reactive gases into the reaction chamber to react with substances on the surface of the wafer to change the physical and chemical properties of the wafer.

[0003] During the entire atomic layer deposition process, the stability of the temperature in the reaction chamber is an important cornerstone to ensure the physical and chemical reactions of the material. Usually, when the reaction chamber is performing the atomic layer deposition reaction normally, the operating temperature range is between 260℃ and 360℃. The corresponding working temperature varies depending on the reactants, and the working temperature of some reactants is also outside this working temperature range. Usually a single temperature sensor is used to collect the temperature in the reaction chamber, and the temperature is controlled according to the temperature value collected by the temperature sensor. This temperature control method is easily affected by the external environment.

[0004] How to accurately ensure that the temperature in the reaction chamber is stable at the atomic layer deposition reaction temperature requirement is a technical problem that needs to be solved urgently to ensure the quality of semiconductor products produced by the atomic layer deposition process. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a temperature detection method of multi-sensor fusion in a reaction chamber of an atomic layer deposition device to achieve accurate calculation of the temperature in the reaction chamber, thereby achieving accurate control of the temperature in the reaction chamber.

[0006] In a first aspect, an embodiment of the present application provides a temperature detection method for multi-sensor fusion in a reaction chamber of an atomic layer deposition device, wherein the method is applied to a temperature detection system for multi-sensor fusion in a reaction chamber of an atomic layer deposition device, wherein the temperature detection system at least includes: a plurality of temperature sensors distributed in the atomic layer deposition reaction chamber, each of the temperature sensors being used to monitor the temperature value at a location, and the method includes: Acquire the temperature detection parameters in the reaction chamber, wherein the temperature detection parameters include: t The actual measured value at the moment , the process noise of each temperature sensor , the measurement noise of each of the temperature sensors , the number of the temperature sensors l ; Based on the temperature detection parameters, combined with the preset distributed Kalman filter algorithm, the actual measurement value of each temperature sensor is Performing local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors; Using a preset optimal linear unbiased estimation algorithm, each of the local temperature values ​​is fused to obtain a target temperature value; The temperature in the reaction chamber is controlled based on the target temperature value.

[0007] In combination with the first aspect, in a second possible embodiment, the preset decentralized Kalman filter algorithm satisfies the following formula:

[0008] in, i Indicates the serial number of the temperature sensor. Indicates i Temperature sensor t- 1 moment t A priori estimate of the time, Indicates i Temperature sensor t Always t The posterior estimate of time, Indicates i Temperature sensor t- 1 moment t- The posterior estimate at time 1; Indicates i Temperature sensor t- 1 moment t The prior covariance of time, Indicates i Temperature sensor t Always t The posterior covariance at time , Indicates i Temperature sensor t -1 moment to t The posterior covariance at time -1, For the i The temperature sensor is t The moment Kalman gain, express t a system matrix of the temperature measurement circuit system in the reaction chamber at the time instant, represents the transpose of the system matrix, express t The momenti The measurement matrix of temperature sensors, represents the transpose of the measurement matrix, Indicates i Temperature sensor t The actual measured value at the moment, represents the measurement noise of the temperature sensor The covariance of represents the process noise of the temperature sensor The covariance of In combination with the second possible embodiment of the first aspect, in a third possible embodiment, the preset distributed Kalman filter algorithm is combined to calculate the actual measurement value of each temperature sensor. Perform local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors, including: The temperature sensors are corresponding to The value is determined as the local temperature value corresponding to each temperature sensor.

[0009] In combination with the second possible embodiment of the first aspect, in a fourth possible embodiment, the preset best linear unbiased estimation algorithm satisfies the following formula:

[0010] in, is the temperature fusion estimate, H is the number of temperature sensors l The stacked matrix, is a stacked matrix of local temperature values ​​corresponding to each of the temperature sensors, is the error covariance matrix of the temperature fusion estimate, The stacking matrix for measuring noise The covariance matrix of the measurement noise is the stacked matrix The measurement error of the local temperature value of each temperature sensor Stacked to get.

[0011] In combination with the fourth possible embodiment of the first aspect, in a fifth possible embodiment, the method of using a preset optimal linear unbiased estimation algorithm to fuse the local temperature values ​​to obtain a target temperature value includes: The temperature fusion estimate obtained by the fusion process , determined as the target temperature value.

[0012] In combination with the first aspect, in a sixth possible embodiment, the process noise , the measurement noise All of them are white noise and follow normal distribution.

[0013] In a second aspect, an embodiment of the present application provides a temperature detection system for multi-sensor fusion in a reaction chamber of an atomic layer deposition device, wherein the temperature detection system comprises: a plurality of temperature sensors and a controller, wherein each of the temperature sensors is distributed at a different position in the atomic layer deposition reaction chamber to monitor the temperature value at the position, and the controller is used to: Acquire the temperature detection parameters in the reaction chamber, wherein the temperature detection parameters include: t The actual measured value at the moment , the process noise of each temperature sensor , the measurement noise of each of the temperature sensors , the number of the temperature sensors l ; Based on the temperature detection parameters, combined with the preset distributed Kalman filter algorithm, the actual measurement value of each temperature sensor is Performing local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors; Using a preset optimal linear unbiased estimation algorithm, each of the local temperature values ​​is fused to obtain a target temperature value; The temperature in the reaction chamber is controlled based on the target temperature value.

[0014] In combination with the second aspect, in a second possible embodiment, the preset decentralized Kalman filter algorithm satisfies the following formula:

[0015] in, i Indicates the serial number of the temperature sensor. Indicates i Temperature sensor t- 1 moment t A priori estimate of the time, Indicates i Temperature sensor t Always t The posterior estimate of time, Indicates i Temperature sensor t- 1 moment t- The posterior estimate at time 1; Indicates i Temperature sensor t- 1 moment t The prior covariance of time, Indicates i Temperature sensor t Alwayst The posterior covariance at time , Indicates i Temperature sensor t -1 moment to t The posterior covariance at time -1, For the i The temperature sensor is t The moment Kalman gain, express t a system matrix of the temperature measurement circuit system in the reaction chamber at the time instant, represents the transpose of the system matrix, express t The moment i The measurement matrix of temperature sensors, represents the transpose of the measurement matrix, Indicates i Temperature sensor t The actual measured value at the moment, represents the measurement noise of the temperature sensor The covariance of represents the process noise of the temperature sensor The covariance of .

[0016] In combination with the second possible embodiment of the second aspect, in a third possible embodiment, the preset distributed Kalman filter algorithm is combined to calculate the actual measurement value of each temperature sensor. Perform local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors, including: The temperature sensors are corresponding to The value is determined as the local temperature value corresponding to each temperature sensor.

[0017] In combination with the second possible embodiment of the second aspect, in a fourth possible embodiment, the preset best linear unbiased estimation algorithm satisfies the following formula:

[0018] in, is the temperature fusion estimate, H is the stacking matrix of the number l of temperature sensors, is a stacked matrix of local temperature values ​​corresponding to each of the temperature sensors, is the error covariance matrix of the temperature fusion estimate, The stacking matrix for measuring noise The covariance matrix of the measurement noise is the stacked matrix The measurement error of the local temperature value of each temperature sensor Stacked to get.

[0019] In combination with the fourth possible embodiment of the second aspect, in a fifth possible embodiment, the method of using a preset optimal linear unbiased estimation algorithm to fuse the local temperature values ​​to obtain a target temperature value includes: The temperature fusion estimate obtained by the fusion process , determined as the target temperature value.

[0020] In combination with the second aspect, in a sixth possible embodiment, the process noise , the measurement noise All of them are white noise and follow normal distribution.

[0021] In a third aspect, an embodiment of the present application provides an electronic device, wherein the electronic device includes: A processor and a memory for storing programs; Wherein, the program includes instructions, and when the instructions are executed by the processor, the processor executes the temperature detection method of multi-sensor fusion in the reaction chamber of the atomic layer deposition equipment described in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium storing computer instructions, characterized in that the computer instructions are used to enable a computer to execute the temperature detection method of multi-sensor fusion in the reaction chamber of the atomic layer deposition equipment described in the first aspect.

[0023] Beneficial effects of this application: The embodiment of the present application provides a temperature detection method, system and electronic device for multi-sensor fusion in the reaction chamber of an atomic layer deposition device, which is applied to the technical field of general control and regulation systems. The method is used in a temperature detection system for multi-sensor fusion in the reaction chamber of an atomic layer deposition device. The actual measurement values ​​and the number of sensors obtained by multiple temperature sensors at different positions in the reaction chamber are obtained, and local Kalman filtering is performed in combination with the proposed distributed Kalman filtering algorithm to reduce the impact of noise and obtain the local temperature value corresponding to each temperature sensor. The local temperature values ​​are fused using a preset optimal linear unbiased estimation algorithm to obtain a fused temperature value that can accurately reflect the actual temperature in the chamber, that is, the target temperature value. Finally, this temperature value is used for temperature control, which can deal with the missing values ​​caused by the failure of a single sensor in the traditional method, reduce the impact of measurement noise and process noise on the temperature measurement accuracy, and provide protection for subsequent reaction chamber temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 A schematic diagram of a flow chart of a temperature detection method of multi-sensor fusion in a reaction chamber of an atomic layer deposition device provided in an embodiment of the present application is shown; Figure 2 Another schematic flow chart of a temperature detection method using multi-sensor fusion in a reaction chamber of an atomic layer deposition device provided in an embodiment of the present application is shown; Figure 3 A schematic diagram of a system architecture of a temperature detection system with multi-sensor fusion in a reaction chamber of an atomic layer deposition device provided in an embodiment of the present application is shown; Figure 4 Another system architecture schematic diagram of a temperature detection system for multi-sensor fusion in a reaction chamber of an atomic layer deposition device provided in an embodiment of the present application is shown; Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] 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.

[0026] 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.

[0027] The term "including" and its variations used in this document are open inclusions, that is, "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 other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. 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.

[0028] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0029] In the first aspect, the present application provides a temperature detection method of multi-sensor fusion in a reaction chamber of an atomic layer deposition device, and the method is applied to any electronic device with a temperature detection function of multi-sensor fusion in a reaction chamber of an atomic layer deposition device, including but not limited to a personal mobile terminal, a computer or a server, etc. In some possible embodiments, the method is applied to a temperature detection system of multi-sensor fusion in a reaction chamber of an atomic layer deposition device, and the temperature detection system at least includes: a plurality of temperature sensors distributed in the atomic layer deposition reaction chamber, each of the temperature sensors is used to monitor the temperature value of the location, and can be as follows: Figure 1 As shown, the method comprises the following steps: S11, obtaining temperature detection parameters in the reaction chamber, wherein the temperature detection parameters include: t The actual measured value at the moment , the process noise of each temperature sensor , the measurement noise of each of the temperature sensors , the number of the temperature sensors l ; S12, based on the temperature detection parameters, combined with a preset distributed Kalman filter algorithm, performing local Kalman filtering on the actual measurement value of each temperature sensor to obtain a local temperature value corresponding to each temperature sensor; S13, using a preset optimal linear unbiased estimation algorithm to fuse the local temperature values ​​to obtain a target temperature value; S14. Controlling the temperature in the reaction chamber based on the target temperature value.

[0030] The method provided in the present application obtains the actual measurement values ​​and the number of sensors obtained by multiple temperature sensors at different positions in the reaction chamber, and performs local Kalman filtering in combination with the proposed distributed Kalman filtering algorithm to reduce the impact of noise and obtain the local temperature value corresponding to each temperature sensor. The local temperature values ​​are fused using a preset optimal linear unbiased estimation algorithm to obtain a fused temperature value that can accurately reflect the actual temperature in the chamber, that is, the target temperature value. This temperature value is finally used for temperature control, which can cope with the missing values ​​caused by the failure of a single sensor in the traditional method, reduce the impact of measurement noise and process noise on the temperature measurement accuracy, and provide protection for the subsequent reaction chamber temperature control.

[0031] By selecting the embodiments of the present application, the influence of test noise and process noise on the temperature measurement accuracy can be reduced, and the accuracy of temperature measurement can be improved to achieve precise control of the temperature in the reaction chamber, thereby achieving precise guarantee of temperature stability in the reaction chamber and further guaranteeing the quality of semiconductor products produced by the atomic layer deposition process.

[0032] The above steps S11 to S14 will be described in detail below with reference to specific examples: In the embodiment of the present application, the temperature sensor can be any type of temperature acquisition device. As a preferred embodiment, the temperature sensor can be a thermocouple, wherein the installation position of each temperature sensor is located at a different position of the atomic layer deposition reaction chamber. As a preferred embodiment, the installation position of each temperature sensor is evenly distributed in the reaction chamber. Among them, the specific model, size, color, and installation position of the temperature sensor can be flexibly set according to the actual process production requirements, and this application does not make strict restrictions.

[0033] Among them, the temperature detection system of multi-sensor fusion in the reaction chamber of the atomic layer deposition equipment can be understood as an electrical system or circuit system for temperature measurement and control. In this application, the variables involved in the entire electrical system or circuit system that affect temperature control are collectively referred to as temperature detection parameters. In addition to the actual measurement values ​​collected by each temperature sensor, the temperature detection parameters may also include: the process noise of each temperature sensor, the measurement noise of each temperature sensor, and the number of temperature sensors. This article defines each temperature detection parameter as: The temperature sensors monitor t The actual measured value at the moment , process noise of each temperature sensor , the measurement noise of each temperature sensor , the number of temperature sensors l The actual measured value is the temperature value output by the temperature sensor, which includes the measurement noise and process noise of the temperature sensor.

[0034] Among them, measurement noise refers to the random fluctuations or uncertainties in the measurement results caused by the characteristics of the sensor itself, the measurement environment, the measurement circuit and other factors when the temperature sensor measures the temperature. It is the noise directly superimposed on the measurement signal during the measurement process, which will cause the measured value to deviate from the true value. Process noise refers to the fluctuation or change of the true value of the temperature itself caused by various uncertain factors within the system in the actual physical process. It reflects the inherent uncertainty of the measured temperature in the actual physical process and has nothing to do with the measurement system, but is related to the dynamic process of temperature change.

[0035] When executing step S11, as an implementation method, the actual measurement value can be obtained by directly obtaining the measurement output result of the temperature sensor. The number of temperature sensors can be obtained by obtaining the number of temperature sensors set by the program through the host computer. As an implementation method, the process noise and measurement noise need to be obtained in advance by obtaining the specific model of each temperature sensor, and querying the product manual or model introduction corresponding to the model, and determining the measurement variance value corresponding to each temperature sensor according to the product manual or model introduction, and then determining the process noise of the entire system and the measurement noise of each temperature sensor according to the measurement variance value.

[0036] Specifically, the measurement noise of each temperature sensor can be calculated by multiple experimental measurements and modeling based on the measurement results. The temperature change model of the system can be established through the physical principles and thermodynamic processes of the measured system, and then the temperature data can be analyzed through the clustering algorithm in machine learning to identify the temperature fluctuations caused by the process noise, and its characteristics can be analyzed to obtain the process noise of the system. The specific method of obtaining process noise and measurement noise is not the focus of this article and will not be repeated here. By selecting an embodiment of the present application, the model of the temperature sensor can be obtained in advance, and the variance of the temperature sensor can be determined, and then the process noise and measurement noise can be determined based on the characteristics of the temperature sensor, so as to accurately reduce the noise of the actual measurement values ​​of each temperature sensor, thereby improving the accuracy of temperature measurement.

[0037] Combination Figure 2 It can be understood that in the embodiment of the present application, the atomic layer deposition reaction chamber can be regarded as a black box, in which the actual temperature value It can be sensed by external people or equipment, usually measured by a temperature sensor, and the measured value is output , but as mentioned above, the measured value The measurement noise, process noise and other interference factors are included in the There is an error between them. Based on this, the entire reaction chamber circuit system can be regarded as a black box. The internal state of the black box is mathematically modeled to obtain the true temperature value. and Actual measured value Specifically, as an implementation method, the real temperature value Compared with the actual measured value The mapping relationship between them satisfies the following formula 1: Formula 1 in, i Representative i A temperature sensor, l is the number of temperature sensors, Arepresent t The system matrix of the reaction chamber temperature measurement circuit system at time instant, Indicates i The measurement matrix of temperature sensors, for t The actual temperature value in the reaction chamber at time t can also be understood as an estimated temperature value, which is a value that estimates the actual temperature through statistical algorithm simulation. for t The actual temperature value in the reaction chamber at time +1 is also an estimated temperature value, which can be obtained by t The temperature value at the moment can be calculated by combining the process noise. For the i The actual measured value actually output by the temperature sensor. Based on this, according to the mapping relationship of the above formula 1, when the actual measured value is known After that, the actual temperature value in the reaction chamber can be inferred. .

[0038] Based on Formula 1, in the embodiment of the present application, the process noise , the measurement noise All are white noise and obey the normal distribution. The measurement noise of each temperature sensor is different due to factors such as manufacturing process, working principle, environmental adaptability, electronic component characteristics, use conditions and calibration accuracy, but all meet the statistical characteristics of Gaussian white noise. , the average value of the measurement noise , and each temperature sensor is independent of each other, it can be considered , and are independent random variables. In this way, the system average value of the temperature measurement circuit system of the entire reaction chamber can be determined as:

[0039] Based on the system average matrix, it can be seen that The covariance of , system process noise The covariance of is: , the measurement noise of each temperature sensor The covariance of is: ,in, is the Kronecker sign function, satisfying .

[0040] Since in the embodiment of the present application, multiple temperature sensors are used to measure the temperature in the reaction chamber, and each temperature sensor is independent of each other, the actual temperature measurement value obtained by each of them cannot represent the actual temperature state in the reaction chamber, and the actual temperature measurement value obtained by each of them has errors caused by noise. Based on this, in the embodiment of the present application, when executing step S12, a local Kalman filter is performed on each temperature sensor to remove potential errors in the output results of the temperature sensor, and the local temperature value corresponding to each temperature sensor is obtained. Compared with the actual measurement value, the local temperature value is more accurate and more helpful to accurately reflect the actual temperature in the reaction chamber.

[0041] Specifically, in some possible embodiments, the preset decentralized Kalman filter algorithm satisfies the following formula 2: Formula 2 in, Indicates i Temperature sensor t- 1 moment t A priori estimates of moments; Indicates i Temperature sensor t Always t The posterior estimate of time, Indicates i Temperature sensor t- 1 moment t- The posterior estimate at time 1; Indicates i Temperature sensor t- 1 moment t Prior covariance of moments; Indicates i Temperature sensor t Always t The posterior covariance at time , Indicates i Temperature sensor t -1 moment to t -1 posterior covariance; For the i The temperature sensor is t The Kalman gain at the moment can be calculated based on the existing Kalman gain calculation formula.

[0042] A express ta temperature measurement circuit system matrix in the reaction chamber at the time instant, wherein each element in the matrix is ​​the current state of each node in the entire temperature measurement circuit, which may be a normalized state value; represents the transpose of the system matrix; express t The moment i The measurement matrix of a temperature sensor is the matrix constructed by the actual measurement values; represents the transpose of the measurement matrix; Indicates i Temperature sensor t The actual measured value at the moment; represents the measurement noise of the temperature sensor The covariance of represents the process noise of the temperature sensor The covariance of .

[0043] Based on this, as an implementation method, when executing step S12, the temperature sensors corresponding to the above formula 2 can be The value is determined as the local temperature value corresponding to each temperature sensor, so as to filter the process noise and measurement noise of each temperature sensor and obtain an accurate local temperature value.

[0044] If the temperature sensor is arranged on the inner wall of the reaction chamber, and the number of the temperature sensors is 3, and the specific type of the temperature sensor is a thermocouple, the overall concept of the temperature detection method of multi-sensor fusion in the reaction chamber of the atomic layer deposition device provided in the embodiment of the present application can be as follows: Figure 3 As shown, the thermocouples T1, T2 and T3 are arranged at different positions on the inner wall of the reaction chamber to form a local temperature sensor node, and then each thermocouple measures the actual temperature measurement value of its own position. , , , and input it into the controller.

[0045] In the controller, each thermocouple and the connected circuit are regarded as a thermocouple acquisition module, and each thermocouple corresponds to thermocouple acquisition module 1, thermocouple acquisition module 2, and thermocouple acquisition module 3. Then, by executing the above formula 2 in step S12, the temperature value output by each thermocouple acquisition module is subjected to local distributed Kalman filtering. In order to facilitate distinction, the local distributed Kalman filtering is performed on the three thermocouple acquisition modules, namely local distributed Kalman filtering 1, local distributed Kalman filtering 2, and local distributed Kalman filtering 3, and then the local temperature values ​​corresponding to each temperature sensor are obtained. , , Then, the fusion processing module in the controller executes step S13 to fuse each local temperature value and finally calculates a temperature fusion estimation value , the temperature fusion estimate The target temperature value is determined to be output, so that the controller executes step S14 to control the temperature in the reaction chamber based on the target temperature value.

[0046] When executing step S14, the working power of the heating module in the reaction chamber is adjusted according to the temperature difference between the target temperature value and the temperature value specified in the production process, so as to adjust the temperature in the reaction chamber.

[0047] In step S13, the preset optimal linear unbiased estimation algorithm satisfies the following formula 3: Formula 3 in, is the temperature fusion estimate, The ultimate goal of the fusion process is to provide the temperature fusion estimate of the temperature measurement system of the entire reaction chamber, that is, the actual temperature value in the reaction chamber. H is the number of temperature sensors l The stacked matrix, is a stacked matrix of local temperature values ​​corresponding to each of the temperature sensors, is the error covariance matrix of the temperature fusion estimate, The stacking matrix for measuring noise The covariance matrix of the measurement noise is the stacked matrix The measurement error of the local temperature value of each temperature sensor Stacked to get.

[0048] Stacking is a common calculation method in linear algebra calculation, and this application will not go into details. l The stacking matrix H , the stacked matrix of local temperature values ​​corresponding to each temperature sensor , the stacked matrix of measurement noise It is defined in advance by the following formula 4: Formula 4 in, col () is a stacking calculation symbol, which means that the data in the brackets are stacked, which can be horizontal stacking or vertical stacking. Based on this, the fusion processing module can determine the mapping relationship between the stacking matrices through the following formula 5: Formula 5 Further, according to the best linear unbiased criterion, combined with the formula from The temperature fusion estimate shown in the above formula 3 can be obtained by fusion And the error covariance matrix of the temperature fusion estimate .

[0049] Among them, the error covariance matrix By calculating the variables H and , where, according to formula 4, we can deduce Satisfies the following formula 6, that is, It can be calculated by the following formula 6: Formula 6 Among them, each element in the matrix is the cross-covariance, which can be understood as the mutual interference between the temperature sensors. By calculating the cross-covariance, the mutual interference between the temperature sensors can be eliminated. Where i is the serial number of one temperature sensor, j is the serial number of another temperature sensor, when i=j, , which is the first i Temperature sensor t Always t The posterior covariance at time . If i≠j, then the cross-covariance The following constraints are satisfied: Formula 7 in, , I It is the unit matrix. Among the elements in the unit matrix, only the elements on the diagonal are 1, and the rest are 0.

[0050] In this way, through the above method, in the presence of internal and external interference, a high temperature and accurate temperature measurement result can still be maintained, effectively avoiding the situation where the actual measurement value cannot accurately reflect the real temperature value, optimizing the reliability of temperature measurement, so that the target temperature value finally output can truly reflect the actual temperature value in the reaction chamber. In addition, the method of using multiple temperature sensors for temperature measurement realizes redundant design. Even if one of the temperature sensors fails, the temperature estimation value and covariance of the failed temperature sensor will be directly reset to zero, and will not affect the normal progress of the overall temperature fusion. The entire temperature measurement system can still rely on the data of the remaining temperature sensors to continue the fusion processing, thereby obtaining an accurate temperature value, ensuring that the temperature measurement of the process is not interrupted, especially in the atomic layer deposition reaction process, which can effectively avoid the problems of production suspension and increased scrap rate caused by temperature sensor failure, ensure the accuracy of temperature control and reduce economic losses.

[0051] In a second aspect, the present application provides a temperature detection system of multi-sensor fusion in a reaction chamber of an atomic layer deposition device, wherein, Figure 4 As shown, the system 40 includes: a controller 41 and a temperature acquisition module 42. The temperature acquisition module 42 can be a virtual module composed of temperature sensors 421, 422, 42n, etc., distributed in different positions in the atomic layer deposition reaction chamber. That is, the system includes a plurality of temperature sensors and a controller, each of which is distributed in different positions in the atomic layer deposition reaction chamber to monitor the temperature value at the position, and the controller is used to: Acquire the temperature detection parameters in the reaction chamber, wherein the temperature detection parameters include: t The actual measured value at the moment , the process noise of each temperature sensor , the measurement noise of each of the temperature sensors , the number of the temperature sensors l ; Based on the temperature detection parameters, combined with the preset distributed Kalman filter algorithm, the actual measurement value of each temperature sensor is Performing local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors; Using a preset optimal linear unbiased estimation algorithm, each of the local temperature values ​​is fused to obtain a target temperature value; The temperature in the reaction chamber is controlled based on the target temperature value.

[0052] In combination with the second aspect, in a second possible embodiment, the preset decentralized Kalman filter algorithm satisfies the following formula:

[0053] in, i Indicates the serial number of the temperature sensor. Indicates i Temperature sensor t- 1 moment t A priori estimate of the time, Indicates i Temperature sensor t Always t The posterior estimate of time, Indicates i Temperature sensor t- 1 moment t- The posterior estimate at time 1; Indicates i Temperature sensor t-1 moment t The prior covariance of time, Indicates i Temperature sensor t Always t The posterior covariance at time , Indicates i Temperature sensor t -1 moment to t The posterior covariance at time -1, For the i The temperature sensor is t The moment Kalman gain, express t a system matrix of the temperature measurement circuit system in the reaction chamber at the time instant, represents the transpose of the system matrix, express t The moment i The measurement matrix of temperature sensors, represents the transpose of the measurement matrix, Indicates i Temperature sensor t The actual measured value at the moment, represents the measurement noise of the temperature sensor The covariance of represents the process noise of the temperature sensor The covariance of .

[0054] In combination with the second possible embodiment of the second aspect, in a third possible embodiment, the preset distributed Kalman filter algorithm is combined to calculate the actual measurement value of each temperature sensor. Perform local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors, including: The temperature sensors are corresponding to The value is determined as the local temperature value corresponding to each temperature sensor.

[0055] In combination with the second possible embodiment of the second aspect, in a fourth possible embodiment, the preset best linear unbiased estimation algorithm satisfies the following formula:

[0056] in, is the temperature fusion estimate, H is the stacking matrix of the number l of temperature sensors, is a stacked matrix of local temperature values ​​corresponding to each of the temperature sensors, is the error covariance matrix of the temperature fusion estimate, The stacking matrix for measuring noise The covariance matrix of the measurement noise is the stacked matrix The measurement error of the local temperature value of each temperature sensor Stacked to get.

[0057] In combination with the fourth possible embodiment of the second aspect, in a fifth possible embodiment, the method of using a preset optimal linear unbiased estimation algorithm to fuse the local temperature values ​​to obtain a target temperature value includes: The temperature fusion estimate obtained by the fusion process , determined as the target temperature value.

[0058] In combination with the second aspect, in a sixth possible embodiment, the process noise , the measurement noise All of them are white noise and follow normal distribution.

[0059] The names of the messages or information exchanged between multiple systems in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0060] In a third aspect, the exemplary embodiments of the present application further provide an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to enable the electronic device to perform a method according to an embodiment of the present application when executed by the at least one processor.

[0061] The exemplary embodiment of the present application further provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present application.

[0062] The exemplary embodiments of the present application further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor of a computer, it is used to enable the computer to execute the method according to the embodiment of the present application.

[0063] refer to Figure 5, the structural block diagram of the electronic device 500 that can be used as the server or client of the present application will now be described, which is an example of hardware devices that can be applied to various aspects of the present application. The electronic device is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0064] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM 502 or a computer program loaded from a storage unit 508 into a RAM 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O interface 505 is also connected to the bus 504.

[0065] A plurality of components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 may be any type of device capable of inputting information to the electronic device 500, and the input unit 506 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 507 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 may include, but is not limited to, a disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0066] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the temperature detection method of multi-sensor fusion in the reaction chamber of the aforementioned atomic layer deposition device may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. In some embodiments, the computing unit 501 may be configured to perform the temperature detection method of multi-sensor fusion in the reaction chamber of the aforementioned atomic layer deposition device by any other appropriate means (e.g., by means of firmware).

[0067] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0068] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0069] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0070] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0071] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0072] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

Claims

1. A temperature detection method for multi-sensor fusion in a reaction chamber of an atomic layer deposition device, characterized in that: The method is applied to a temperature detection system of multi-sensor fusion in a reaction chamber of an atomic layer deposition device, wherein the temperature detection system at least comprises: a plurality of temperature sensors distributed in the reaction chamber, each of the temperature sensors being used to monitor the temperature value at a location, and the method comprises: Acquire the temperature detection parameters in the reaction chamber, wherein the temperature detection parameters include: t The actual measured value at the moment , the process noise of each temperature sensor , the measurement noise of each of the temperature sensors , the number of the temperature sensors l ; Based on the temperature detection parameters, combined with the preset distributed Kalman filter algorithm, the actual measurement value of each temperature sensor is Performing local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors; Using a preset optimal linear unbiased estimation algorithm, each of the local temperature values ​​is fused to obtain a target temperature value; The temperature in the reaction chamber is controlled based on the target temperature value.

2. The method according to claim 1, characterized in that The preset decentralized Kalman filter algorithm satisfies the following formula: in, i Indicates the serial number of the temperature sensor. Indicates i Temperature sensor t- 1 moment t A priori estimate of the time, Indicates i Temperature sensor t Always t The posterior estimate of time, Indicates i Temperature sensor t- 1 moment t- The posterior estimate at time 1; Indicates i Temperature sensor t- 1 moment t The prior covariance of time, Indicates i Temperature sensor t Always t The posterior covariance at time , Indicates i Temperature sensor t -1 moment to t The posterior covariance at time -1, For the i A temperature sensor in t The moment Kalman gain, express t a system matrix of the temperature measurement circuit system in the reaction chamber at the time instant, represents the transpose of the system matrix, express t The moment i The measurement matrix of temperature sensors, represents the transpose of the measurement matrix, Indicates i Temperature sensor t The actual measured value at the moment, represents the measurement noise of the temperature sensor The covariance of represents the process noise of the temperature sensor The covariance of .

3. The method according to claim 2, characterized in that The preset distributed Kalman filter algorithm is combined with the actual measurement value of each temperature sensor. Perform local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors, including: The temperature sensors are corresponding to The value is determined as the local temperature value corresponding to each temperature sensor.

4. The method according to claim 2, characterized in that: The preset optimal linear unbiased estimation algorithm satisfies the following formula: in, is the temperature fusion estimate, H is the number of temperature sensors l The stacked matrix, is a stacked matrix of local temperature values ​​corresponding to each of the temperature sensors, is the error covariance matrix of the temperature fusion estimate, The stacking matrix for measuring noise The covariance matrix of the measurement noise is the stacked matrix The measurement error of the local temperature value of each temperature sensor Stacked to get.

5. The method according to claim 4, characterized in that The method adopts a preset optimal linear unbiased estimation algorithm to fuse the local temperature values ​​to obtain a target temperature value; including: The temperature fusion estimate obtained by the fusion process , determined as the target temperature value.

6. The method according to claim 1, characterized in that The process noise , the measurement noise All of them are white noise and follow normal distribution.

7. A temperature detection system for multi-sensor fusion in a reaction chamber of an atomic layer deposition device, characterized in that: The temperature detection system comprises: a plurality of temperature sensors and a controller, wherein each of the temperature sensors is distributed at a different position in the atomic layer deposition reaction chamber for monitoring the temperature value at the position, and the controller is used to: Acquire the temperature detection parameters in the reaction chamber, wherein the temperature detection parameters include: t The actual measured value at the moment , the process noise of each temperature sensor , the measurement noise of each of the temperature sensors , the number of the temperature sensors l ; Based on the temperature detection parameters, combined with the preset distributed Kalman filter algorithm, the actual measurement value of each temperature sensor is Performing local Kalman filtering to obtain local temperature values ​​corresponding to each of the temperature sensors; Using a preset optimal linear unbiased estimation algorithm, each of the local temperature values ​​is fused to obtain a target temperature value; The temperature in the reaction chamber is controlled based on the target temperature value.

8. The system according to claim 7, characterized in that The preset decentralized Kalman filter algorithm satisfies the following formula: in, i Indicates the serial number of the temperature sensor. Indicates i Temperature sensor t- 1 moment t A priori estimate of the time, Indicates i Temperature sensor t Always t The posterior estimate of time, Indicates i Temperature sensor t- 1 moment t- The posterior estimate at time 1; Indicates i Temperature sensor t- 1 moment t The prior covariance of time, Indicates i Temperature sensor t Always t The posterior covariance at time , Indicates i Temperature sensor t -1 moment to t The posterior covariance at time -1, For the i The temperature sensor is t The moment Kalman gain, express t a system matrix of the temperature measurement circuit system in the reaction chamber at the time instant, represents the transpose of the system matrix, express t The moment i The measurement matrix of temperature sensors, represents the transpose of the measurement matrix, Indicates i Temperature sensor t The actual measured value at the moment, represents the measurement noise of the temperature sensor The covariance of represents the process noise of the temperature sensor The covariance of The temperature sensors are corresponding to The value is determined as the local temperature value corresponding to each temperature sensor; The preset optimal linear unbiased estimation algorithm satisfies the following formula: in, is the temperature fusion estimate, H is the number of temperature sensors l The stacked matrix, is a stacked matrix of local temperature values ​​corresponding to each of the temperature sensors, is the error covariance matrix of the temperature fusion estimate, The stacking matrix for measuring noise The covariance matrix of the measurement noise is the stacked matrix The measurement error of the local temperature value of each temperature sensor Stack to get; The temperature fusion estimate obtained by the fusion process , determined as the target temperature value; The process noise , the measurement noise All of them are white noise and follow normal distribution.

9. An electronic device, characterized in that: The electronic device comprises: A processor and a memory for storing programs; The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to make a computer execute the method according to any one of claims 1-6.

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