Method for optimizing control parameters of oxidation furnace for chip manufacturing and related equipment

Abstract

The invention discloses a control parameter optimization method for an oxidation furnace for chip manufacturing and related equipment, and relates to the technical field of semiconductor production and manufacturing, the control parameter optimization method for the oxidation furnace for chip manufacturing comprises the following steps: acquiring sensor parameters in a wafer oxidation process; the sensor parameters are input into a preset oxidation prediction model, a wafer oxidation prediction result is determined, and the oxidation prediction model is obtained by training a preset to-be-trained model through historical sensor data samples and wafer oxidation result labels; and dynamically adjusting the control parameters of the oxidation furnace according to the wafer oxidation prediction result. According to the invention, by adjusting the control parameters, defects caused by non-uniform oxidation are reduced, and the qualified rate of wafers is improved. The oxidation prediction model can help to avoid defects such as peroxidation or insufficient oxidation and improve the quality of the wafer. Through accurate prediction and dynamic adjustment, the oxidation process of the wafer is effectively controlled, so that the yield of the oxidation process of the wafer is improved.

Description

Technical Field

[0001] This application relates to the technical field of semiconductor manufacturing, and particularly to an optimization method for oxidation furnace control parameters in chip manufacturing and related equipment. Background Art

[0002] In the field of semiconductor manufacturing, the quality and characteristics of the oxide layer have a decisive impact on the performance of the wafer. The oxidation furnace is a key equipment for forming the oxide layer on the wafer surface. How to oxidize the wafer with high quality by the oxidation furnace has become a hot issue in the semiconductor field.

[0003] Traditional oxidation furnaces usually adopt fixed parameter settings, such as temperature, time, oxygen flow rate, etc. The setting of these parameters is often based on experience, lacking adaptability to wafer characteristics and environmental changes, unable to adapt to the slight differences between different batches of wafers and the changes in environmental conditions, and it is difficult to achieve precise quality control, resulting in a low yield of the produced wafers. Summary of the Invention

[0004] The main purpose of this application is to provide an optimization method for oxidation furnace control parameters in chip manufacturing and related equipment, aiming to solve the technical problem of low yield of the produced wafers.

[0005] To achieve the above purpose, this application proposes an optimization method for oxidation furnace control parameters in chip manufacturing, and the method includes:

[0006] Obtain sensor parameters during the wafer oxidation process;

[0007] Input the sensor parameters into a preset oxidation prediction model to determine the wafer oxidation prediction result, where the oxidation prediction model is obtained by training a preset model to be trained with historical sensor data samples and wafer oxidation result labels;

[0008] Dynamically adjust the oxidation furnace control parameters according to the wafer oxidation prediction result.

[0009] In an embodiment, before the step of obtaining sensor parameters during the wafer oxidation process, it includes:

[0010] Obtain historical sensor data samples and wafer oxidation result labels;

[0011] Input the historical sensor data samples into a preset model to be trained;

[0012] Predict the wafer oxidation process through the model to be trained to determine the wafer oxidation prediction result;

[0013] Judge the difference between the wafer oxidation prediction result and the wafer oxidation result label to obtain the prediction loss value;

[0014] If the predicted loss value does not meet the preset loss criterion, return to the step of predicting wafer oxidation through the model to be trained to determine the wafer oxidation prediction result, and stop training until the predicted loss value meets the loss criterion, so as to obtain an oxidation prediction model that meets the loss criterion.

[0015] In one embodiment, the step of dynamically adjusting the oxidation furnace control parameters includes:

[0016] Calculate the corresponding control parameter adjustment deviation according to the deviation between the wafer oxidation prediction result and the preset oxidation target value;

[0017] Adjust the preset control parameter adjustment strategy according to the sensitivity of control parameters in different stages of the wafer oxidation process;

[0018] Adjust the control parameter adjustment strategy according to the change trend of the wafer oxidation prediction result;

[0019] Correct the control parameter adjustment strategy according to the process environment factors monitored in real time;

[0020] Convert the control parameter adjustment deviation into an actual control parameter adjustment value according to the corrected control parameter adjustment strategy;

[0021] Dynamically adjust the oxidation furnace control parameters according to the actual control parameter adjustment value.

[0022] In one embodiment, the step of adjusting the preset control parameter adjustment strategy according to the sensitivity of control parameters in different stages of the wafer oxidation process includes:

[0023] Determine the current wafer oxidation stage;

[0024] If the wafer oxidation stage is the heating stage, adjust the preset control parameter adjustment strategy to a temperature deviation, and adopt a large-scale adjustment strategy for rapid correction;

[0025] If the wafer oxidation stage is the oxidation stage, adjust the control parameter adjustment strategy to focus on controlling the temperature and oxygen partial pressure, and accurately adjust the temperature and oxygen partial pressure deviations according to different periods of the oxidation stage;

[0026] If the wafer oxidation stage is the cooling stage, adjust the control parameter adjustment strategy to focus on controlling the temperature cooling rate, and adopt a small-scale adjustment strategy for slow correction.

[0027] In one embodiment, the step of adjusting the control parameter adjustment strategy according to the change trend of the wafer oxidation prediction result includes:

[0028] If the predicted result of the wafer oxidation is that the oxidation rate is too fast, according to the remaining time of the oxidation prediction, adjust the control parameter adjustment strategy to a gradually decreasing adjustment strategy;

[0029] If the predicted result of the wafer oxidation is that the oxidation rate is too slow, according to the remaining time of the oxidation prediction, adjust the control parameter adjustment strategy to a gradually increasing adjustment strategy;

[0030] If the predicted result of the wafer oxidation is that the oxidation process is operating stably, adjust the control parameter adjustment strategy to maintain the existing control parameter adjustment value.

[0031] In one embodiment, the step of correcting the control parameter adjustment strategy according to the process environment factors monitored in real time includes:

[0032] When there is an unstable phenomenon in the gas flow in the furnace, according to the gas flow offset and the stable compensation time, correct the control parameter adjustment strategy to adopt a gradually corrected adjustment strategy for the control parameters;

[0033] When the temperature distribution is uneven, according to the temperature deviation and the oxidation sensitivity of each region, correct the control parameter adjustment strategy to precisely adjust the control parameters;

[0034] When the pressure fluctuates, according to the pressure change and the adjustment strategy of the pressure compensation time, correct the control parameter adjustment strategy to adjust the control parameters in real time.

[0035] In addition, to achieve the above object, the present application also proposes an oxidation furnace control parameter optimization device for chip manufacturing, and the oxidation furnace control parameter optimization device for chip manufacturing includes:

[0036] An acquisition module for acquiring sensor parameters during the wafer oxidation process;

[0037] A prediction module for inputting the sensor parameters into a preset oxidation prediction model to determine the predicted result of the wafer oxidation, wherein the oxidation prediction model is obtained by training a preset model to be trained with historical sensor data samples and wafer oxidation result labels;

[0038] An adjustment module for dynamically adjusting the oxidation furnace control parameters according to the predicted result of the wafer oxidation.

[0039] In addition, to achieve the above object, the present application also proposes an oxidation furnace control parameter optimization device for chip manufacturing, and the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the oxidation furnace control parameter optimization method for chip manufacturing as described above.

[0040] In addition, to achieve the above object, the present application also provides a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for optimizing the control parameters of the oxidation furnace for chip manufacturing as described above are implemented.

[0041] In addition, to achieve the above object, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the method for optimizing the control parameters of the oxidation furnace for chip manufacturing as described above are implemented.

[0042] One or more technical solutions proposed by the present application have at least the following technical effects:

[0043] In related technologies, traditional oxidation furnaces usually adopt fixed parameter settings, such as temperature, time, oxygen flow rate, etc. The setting of these parameters is often based on experience, lacking adaptability to wafer characteristics and environmental changes, unable to adapt to the small differences between different batches of wafers and changes in environmental conditions, and it is difficult to achieve precise quality control, resulting in a low yield of the produced wafers. In contrast, the present application obtains sensor parameters during the wafer oxidation process; inputs the sensor parameters into a preset oxidation prediction model to determine the wafer oxidation prediction result, where the oxidation prediction model is obtained by training a preset model to be trained with historical sensor data samples and wafer oxidation result labels; dynamically adjusts the oxidation furnace control parameters according to the wafer oxidation prediction result. It can be understood that by adjusting the control parameters, the present application can ensure that the oxidation degree of each wafer is consistent, reduce defects caused by uneven oxidation, and improve the qualified rate of wafers. The oxidation prediction model can help avoid defects such as over-oxidation or under-oxidation, and improve the quality of wafers. Optimizing the oxidation process can reduce material waste and production time, and reduce production costs. Through precise prediction and dynamic adjustment, the oxidation process of wafers can be effectively controlled, thereby improving the yield of the wafer oxidation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0045] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0046] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the method for optimizing the control parameters of the oxidation furnace for chip manufacturing according to the present application;

[0047] Figure 2 It is a schematic flowchart provided for the second embodiment of the method for optimizing the control parameters of an oxidation furnace for chip manufacturing in this application;

[0048] Figure 3 It is a schematic module structure diagram of the device for optimizing the control parameters of an oxidation furnace for chip manufacturing in an embodiment of this application;

[0049] Figure 4 It is a schematic device structure diagram of the hardware operating environment involved in the method for optimizing the control parameters of an oxidation furnace for chip manufacturing in an embodiment of this application.

[0050] The realization of the purpose, functional features, and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0051] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0052] To better understand the technical solutions of this application, the following will be described in detail in combination with the accompanying drawings of the specification and specific implementation manners.

[0053] The main solution of the embodiment of this application is:

[0054] Obtain the sensor parameters during the wafer oxidation process;

[0055] Input the sensor parameters into a preset oxidation prediction model to determine the wafer oxidation prediction result, where the oxidation prediction model is obtained by training a preset model to be trained with historical sensor data samples and wafer oxidation result labels;

[0056] Dynamically adjust the control parameters of the oxidation furnace according to the wafer oxidation prediction result.

[0057] In this embodiment, this application takes the device for optimizing the control parameters of an oxidation furnace for chip manufacturing as the execution main body. For the convenience of description, it is hereinafter simply referred to as "device" for specific description.

[0058] Since traditional oxidation furnaces usually adopt fixed parameter settings, such as temperature, time, oxygen flow rate, etc., the setting of these parameters is often based on experience, lacking adaptability to wafer characteristics and environmental changes, unable to adapt to the small differences between different batches of wafers and changes in environmental conditions, and it is difficult to achieve precise quality control, resulting in a low yield of the produced wafers.

[0059] This application provides a solution. By adjusting control parameters, it can ensure that the oxidation degree of each wafer is consistent, reduce defects caused by uneven oxidation, and improve the qualification rate of wafers. The oxidation prediction model can help avoid defects such as over-oxidation or under-oxidation, and improve the quality of wafers. Optimizing the oxidation process can reduce material waste and production time, and lower production costs. Through accurate prediction and dynamic adjustment, the oxidation process of wafers can be effectively controlled, thereby improving the yield of the wafer oxidation process.

[0060] Based on this, the embodiment of this application provides an optimization method for the control parameters of an oxidation furnace used in chip manufacturing. Referring to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the optimization method for the control parameters of the oxidation furnace used in chip manufacturing of this application.

[0061] In this embodiment, the optimization method for the control parameters of the oxidation furnace used in chip manufacturing includes steps S10 to S30:

[0062] Step S10, obtain the sensor parameters during the wafer oxidation process;

[0063] It should be noted that the wafer oxidation process refers to the process in semiconductor manufacturing where a silicon wafer is heat-treated in an oxidation furnace to form an oxide film on its surface. This process is crucial for manufacturing integrated circuits and semiconductor devices because it can form insulating layers, protective layers, or serve as the basis for other process steps. Sensor parameters refer to the data of physical quantities and operating conditions that are real-time monitored and recorded by various sensors during the wafer oxidation process. These parameters usually include temperature, pressure, gas flow rate, humidity, oxidation time, etc., and they are key factors for controlling the oxidation process and evaluating the oxidation quality.

[0064] It can be understood that in the field of semiconductor manufacturing, the wafer oxidation process is a basic step for forming key insulating layers and other functional layers in integrated circuits and semiconductor devices. This process involves exposing the silicon wafer to high temperatures and a specific gas environment to promote the occurrence of oxidation reactions. To ensure the quality and uniformity of the oxide layer, it is crucial to precisely control the parameters during the oxidation process. In this application, a method is proposed to optimize the control parameters of the oxidation furnace by obtaining the sensor parameters during the wafer oxidation process. The core of this method lies in real-time monitoring and analyzing the key physical quantities during the oxidation process, such as temperature, pressure, and gas flow rate, which have a direct impact on the formation and characteristics of the oxide layer.

[0065] Step S20, input the sensor parameters into a preset oxidation prediction model to determine the wafer oxidation prediction result, where the oxidation prediction model is obtained by training a preset model to be trained with historical sensor data samples and wafer oxidation result labels;

[0066] It should be noted that the sensor parameters refer to the data of physical quantities and operating conditions that are monitored and recorded in real time by various sensors during the wafer oxidation process, including but not limited to key parameters such as temperature, pressure, gas flow rate, humidity, and oxidation time. The oxidation prediction model is a model constructed using mathematical and statistical methods for predicting the results of the wafer oxidation process. This model can predict the characteristics of the oxide layer, such as thickness, uniformity, and quality, based on the input sensor parameters. The historical sensor data samples refer to the sensor parameter data collected in the past during the wafer oxidation process, which are used to train the oxidation prediction model so that it can learn the relationship between the oxidation process and the oxidation results. The wafer oxidation result labels refer to the records of the wafer oxidation results corresponding to the historical sensor data samples, and these results are usually determined by quality inspection steps and used as the "true" results in model training to evaluate the accuracy of model prediction. The preset model to be trained refers to the model framework set before the start of training. This model has not been trained and needs to learn historical data to optimize its parameters to improve the accuracy of predicting the wafer oxidation results.

[0067] It can be understood that in the field of semiconductor manufacturing, wafer oxidation is a key step that directly affects the performance and reliability of devices. To ensure the quality of the oxide layer, it is necessary to precisely control multiple parameters during the oxidation process. Traditional control methods often rely on fixed parameter settings and lack adaptability to different wafer characteristics and environmental changes, resulting in unstable oxide layer quality. The method described in this application determines the prediction results of wafer oxidation by inputting the sensor parameters collected in real time into a preset oxidation prediction model. The core of this method lies in using historical sensor data samples and wafer oxidation result labels to train a model to be trained so that it can predict future oxidation results. In this way, the oxidation prediction model can learn the relationship between oxidation parameters and oxide layer quality and make predictions based on these relationships. The training of the oxidation prediction model is an iterative process in which the model continuously adjusts its internal parameters until the difference between the prediction result and the actual result (i.e., the prediction loss value) meets the preset loss criterion. Once the model training is completed, it can be used for real-time prediction to provide the prediction results of wafer oxidation based on the input sensor parameters.

[0068] The data-driven method provides a new way to optimize the oxidation process because it can dynamically adjust control parameters according to real-time data to achieve a higher-quality oxide layer. This method not only improves the uniformity and quality of the oxide layer but also reduces the dependence on physical measurements, reduces production costs, and enhances the controllability and predictability of the semiconductor manufacturing process. In this way, this application helps to promote the progress of semiconductor manufacturing technology and meet the growing demand for high-performance integrated circuits.

[0069] Step S30: Dynamically adjust the control parameters of the oxidation furnace according to the predicted wafer oxidation results.

[0070] It should be noted that the predicted wafer oxidation results refer to the predicted data of the characteristics and quality of the expected wafer oxide layer analyzed by the oxidation prediction model based on real-time sensor parameters. These results usually include key indicators such as the thickness, uniformity, and defect rate of the oxide layer, which are used to evaluate and optimize the oxidation process. The oxidation furnace is a device used in the semiconductor manufacturing process to oxidize wafers under high-temperature conditions. This device can generate an oxide layer in a strictly controlled environment and is an essential part of semiconductor device manufacturing. The control parameters refer to the parameters used to precisely control the performance of the oxidation furnace and the oxidation process during the operation of the oxidation furnace. These parameters include, but are not limited to, temperature, pressure, gas flow rate, oxidation time, etc., which directly affect the formation and characteristics of the oxide layer. Dynamic adjustment means making real-time and automatic adjustments to the control parameters of a system or process based on real-time feedback or predicted results. In the context of this application, dynamic adjustment means automatically optimizing the control parameters of the oxidation furnace according to changes in the predicted wafer oxidation results to achieve more precise process control.

[0071] It can be understood that in the field of semiconductor manufacturing, precise control of the wafer oxidation process is crucial for ensuring the performance and reliability of the final product. The quality and characteristics of the oxide layer, such as thickness and uniformity, directly affect the electrical performance and stability of the device. Therefore, real-time monitoring and optimization of the oxidation process are the keys to improving semiconductor manufacturing efficiency and quality. The method described in this application involves dynamically adjusting the control parameters of the oxidation furnace according to the predicted wafer oxidation results. This method uses an advanced prediction model, combined with real-time sensor data, to predict the results of the oxidation process and adjusts the operating parameters of the oxidation furnace accordingly. This dynamic adjustment strategy enables the oxidation process to adapt to real-time changing conditions, such as changes in wafer characteristics and environmental fluctuations, thus achieving a higher-precision and more consistent oxide layer.

[0072] In a feasible implementation manner, step S30 may include:

[0073] Calculate the corresponding control parameter adjustment deviation according to the deviation between the predicted wafer oxidation results and the preset oxidation target value;

[0074] Adjust the preset control parameter adjustment strategy according to the sensitivity of the control parameters at different stages during the wafer oxidation process;

[0075] Adjust the control parameter adjustment strategy according to the changing trend of the predicted wafer oxidation results;

[0076] Correct the control parameter adjustment strategy according to the process environment factors monitored in real time;

[0077] According to the corrected control parameter adjustment strategy, convert the control parameter adjustment deviation into an actual control parameter adjustment value;

[0078] Dynamically adjust the oxidation furnace control parameters according to the actual control parameter adjustment value.

[0079] It should be noted that the wafer oxidation prediction result refers to the prediction data of the characteristics and quality of the expected wafer oxide layer analyzed by the oxidation prediction model based on real-time sensor parameters. These results usually include key indicators such as the thickness, uniformity, and defect rate of the oxide layer, which are used to evaluate and optimize the oxidation process. The preset oxidation target value refers to the ideal value of the oxide layer characteristics preset according to the process requirements and product quality standards during the wafer oxidation process. These target values are used to evaluate the accuracy and consistency of the oxidation process and as a benchmark for adjusting control parameters. The control parameter adjustment deviation refers to the amount that needs to be adjusted for the oxidation furnace control parameters calculated based on the deviation between the wafer oxidation prediction result and the preset oxidation target value. This deviation is the basis for controlling parameter adjustment. The control parameter adjustment strategy refers to a series of rules and methods for guiding how to adjust the oxidation furnace control parameters according to the deviation between the wafer oxidation prediction result and the preset target value. The strategy may include the adjustment amplitude, direction, and timing. The different stages of the wafer oxidation process refer to different time periods during the wafer oxidation process, and each stage may have different sensitivities to specific control parameters, such as the heating stage, oxidation stage, and cooling stage. The process environment factors refer to various environmental conditions that may affect the oxidation quality and efficiency during the wafer oxidation process, such as temperature fluctuations, pressure changes, gas purity, and humidity. The control parameter adjustment value refers to the specific value calculated based on the control parameter adjustment deviation and the adjustment strategy for actually adjusting the oxidation furnace control parameters.

[0080] It can be understood that in semiconductor manufacturing, wafer oxidation is a key step, and its quality directly affects the performance and reliability of the device. Traditional oxidation process control methods often rely on fixed parameter settings and cannot adapt to different wafer characteristics and environmental changes, resulting in unstable oxide layer quality. The method proposed in this application optimizes the oxidation process through the following steps:

[0081] Calculate the control parameter adjustment deviation: By comparing the wafer oxidation prediction result with the preset oxidation target value, calculate the control parameter deviation that needs to be adjusted. This step is the basis for subsequent adjustment to ensure the accuracy of the adjustment direction and amplitude.

[0082] Adjust the control parameter adjustment strategy: According to the sensitivity of different stages of the wafer oxidation process to control parameters, adjust the preset control parameter adjustment strategy. This step takes into account the dynamic characteristics of the oxidation process to ensure effective control during key stages.

[0083] Adjust the strategy according to the changing trend: According to the changing trend of the wafer oxidation prediction results, further adjust the control parameter adjustment strategy. This step enables the control strategy to adapt to the real-time changes in the oxidation process, improving the response speed and adjustment accuracy.

[0084] Modify the strategy according to process environment factors: Modify the control parameter adjustment strategy based on the real-time monitored process environment factors. This step ensures that the control strategy remains effective when the environmental conditions change.

[0085] Convert the adjustment deviation into an actual value: According to the modified control parameter adjustment strategy, convert the control parameter adjustment deviation into an actual control parameter adjustment value. This step is the key to realizing the dynamic adjustment of control parameters and ensures the implementability of the adjustment value.

[0086] Dynamically adjust the control parameters: Dynamically adjust the oxidation furnace control parameters according to the actual control parameter adjustment value. This step realizes the real-time optimization of the oxidation process and improves the quality and consistency of the oxide layer.

[0087] In summary, the method described in this application realizes the intelligent and dynamic adjustment of the oxidation furnace control parameters by comprehensively considering the prediction results, target deviation, process stage, changing trend, and environmental factors, providing an innovative solution for the semiconductor manufacturing field to improve the quality and efficiency of the wafer oxidation process.

[0088] In a feasible implementation manner, the step of adjusting the preset control parameter adjustment strategy according to the sensitivity of the control parameters in different stages of the wafer oxidation process includes:

[0089] Determine the current wafer oxidation stage;

[0090] If the wafer oxidation stage is the heating stage, adjust the preset control parameter adjustment strategy to the temperature deviation, and adopt a large-amplitude adjustment strategy for rapid correction;

[0091] If the wafer oxidation stage is the oxidation stage, adjust the control parameter adjustment strategy to focus on controlling the temperature and oxygen partial pressure, and precisely adjust the temperature and oxygen partial pressure deviations according to different periods of the oxidation stage;

[0092] If the wafer oxidation stage is the cooling stage, adjust the control parameter adjustment strategy to focus on controlling the temperature cooling rate, and adopt a small-amplitude adjustment strategy for slow correction.

[0093] It should be noted that the wafer oxidation stage refers to different periods in the wafer oxidation process, including the heating stage, the oxidation stage, and the cooling stage. Each stage has different requirements for the adjustment strategy of control parameters. The heating stage is the initial stage of the wafer oxidation process, and its main purpose is to heat the wafer to the temperature required for oxidation. The oxidation stage is the core stage of the wafer oxidation process, during which the surface of the wafer reacts with the oxidant to form an oxide layer. The cooling stage is the final stage of the wafer oxidation process, which slowly cools the wafer from the oxidation temperature to room temperature. The control parameter adjustment strategy refers to the strategy of adjusting the control parameters (such as temperature, pressure, etc.) of the oxidation furnace according to the different wafer oxidation stages. The temperature deviation refers to the difference between the actual temperature and the preset temperature target value, which is used to guide the rapid temperature adjustment in the heating stage. The oxygen partial pressure refers to the partial pressure of oxygen during the oxidation process, which is one of the key parameters that need to be precisely controlled in the oxidation stage. The temperature cooling rate refers to the speed at which the wafer cools during the cooling stage, and it needs to be precisely controlled to avoid thermal stress and oxide layer quality problems.

[0094] It can be understood that the present application describes a method for dynamically adjusting control parameters according to different stages of the wafer oxidation process. This method precisely identifies the current oxidation stage and adjusts the control parameters according to the characteristics of each stage to achieve better oxide layer quality and process control.

[0095] In the heating stage, since the wafer needs to be quickly heated to the set oxidation temperature, a large-scale temperature adjustment strategy is adopted to achieve rapid correction. This strategy helps to reduce the time delay during the heating process and ensure the timely start of the oxidation process.

[0096] The oxidation stage is a crucial period for forming the oxide layer, and the control of temperature and oxygen partial pressure is particularly critical. The method of the present application precisely adjusts the temperature and oxygen partial pressure deviations and makes fine adjustments according to the characteristics of different periods to optimize the growth rate and quality of the oxide layer.

[0097] In the cooling stage, the temperature of the wafer needs to be slowly reduced to avoid thermal stress. The method of the present application adopts a small-scale adjustment strategy to precisely control the cooling rate to protect the structural integrity of the oxide layer and prevent device damage caused by too rapid temperature changes.

[0098] Generally speaking, the present application achieves precise control of the wafer oxidation process by dynamically adjusting control parameters according to the characteristics and requirements of different oxidation stages. This method not only improves the quality and uniformity of the oxide layer but also enhances the flexibility and adaptability of the production process, which is of great significance for improving the performance and reliability of semiconductor devices.

[0099] In a feasible implementation manner, the step of adjusting the control parameter adjustment strategy according to the change trend of the wafer oxidation prediction result includes:

[0100] When the predicted result of wafer oxidation indicates that the oxidation rate is too fast, according to the remaining time of the expected oxidation, adjust the control parameter adjustment strategy to adopt a gradually decreasing adjustment strategy;

[0101] When the predicted result of wafer oxidation indicates that the oxidation rate is too slow, according to the remaining time of the expected oxidation, adjust the control parameter adjustment strategy to adopt a gradually increasing adjustment strategy;

[0102] When the predicted result of wafer oxidation indicates stable operation during the oxidation process, adjust the control parameter adjustment strategy to maintain the existing control parameter adjustment value.

[0103] It should be noted that the predicted result of wafer oxidation refers to the predicted data of the characteristics and quality of the expected wafer oxide layer obtained by the oxidation prediction model based on real-time sensor parameters, including key indicators such as the oxidation rate. The oxidation rate refers to the speed at which the oxide layer is formed on the wafer surface and is an important parameter during the wafer oxidation process, affecting the thickness and uniformity of the oxide layer. The control parameter adjustment strategy refers to a series of rules and methods for guiding how to adjust the oxidation furnace control parameters according to the predicted result of wafer oxidation. The gradually decreasing adjustment strategy is a control parameter adjustment method. When the predicted result shows that the oxidation rate is too fast, the relevant control parameters (such as temperature or gas flow rate) are gradually decreased to slow down the oxidation rate. The gradually increasing adjustment strategy is a control parameter adjustment method. When the predicted result shows that the oxidation rate is too slow, the relevant control parameters are gradually increased to speed up the oxidation rate. The remaining time of the expected oxidation refers to the remaining time expected to complete the oxidation process based on the current oxidation progress and the planned total oxidation time. Stable operation during the oxidation process means that the wafer oxidation process proceeds normally within the expected parameter range, and the oxidation rate and oxide layer quality meet the preset standards.

[0104] It can be understood that this application describes a method for dynamically adjusting control parameters based on the predicted result of wafer oxidation, aiming to optimize the oxidation process of the oxidation furnace. This method monitors the oxidation rate in real time and predicts its trend, and intelligently adjusts the control parameters to maintain the stability and efficiency of the oxidation process.

[0105] When the predicted result shows that the oxidation rate is too fast, this method adjusts the control parameters through a gradually decreasing adjustment strategy to slow down the oxidation rate and prevent the oxide layer from being too thick or having quality problems. This strategy helps to avoid the degradation of device performance caused by too fast oxidation rate.

[0106] On the contrary, when the predicted result shows that the oxidation rate is too slow, this method adjusts the control parameters through a gradually increasing adjustment strategy to speed up the oxidation rate and ensure that the oxidation process proceeds as planned. This strategy helps to improve production efficiency and reduce production delays caused by too slow oxidation rate.

[0107] When the prediction result shows that the oxidation process is operating stably, the method adjusts the control parameter adjustment strategy to maintain the existing control parameter adjustment value to maintain the continuity and stability of the oxidation process. This strategy helps to reduce unnecessary adjustments and improve the efficiency and reliability of the oxidation process.

[0108] Generally speaking, this application realizes the precise control of the oxidation process of the oxidation furnace by intelligently adjusting the control parameters. This method not only improves the quality and uniformity of the oxide layer, but also enhances the flexibility and adaptability of the production process, which is of great significance for improving the performance and reliability of semiconductor devices. Through this dynamic adjustment strategy, it is possible to better cope with the uncertainties in the production process and improve the overall efficiency and quality of semiconductor manufacturing.

[0109] In a feasible implementation manner, the step of modifying the control parameter adjustment strategy according to the real-time monitored process environment factors includes:

[0110] When there is an unstable phenomenon in the gas flow in the furnace, according to the gas flow offset and the stable compensation time, modify the control parameter adjustment strategy to control the parameters by using a step-by-step correction adjustment strategy;

[0111] When the temperature distribution is uneven, according to the temperature deviation and the oxidation sensitivity of each region, modify the control parameter adjustment strategy to precisely adjust the control parameters;

[0112] When there are fluctuations in pressure, according to the pressure change and the adjustment strategy of the pressure compensation time, modify the control parameter adjustment strategy to adjust the control parameters in real time.

[0113] It should be noted that the phenomenon of unstable gas flow in the furnace refers to the abnormal or deviated situation of the gas flow inside the oxidation furnace from the predetermined pattern, which may affect the uniformity and efficiency of the oxidation process. The gas flow offset refers to the degree of deviation between the actual gas flow and the predetermined gas flow path, and is used to evaluate the stability of the gas flow in the furnace. The stable compensation time refers to the time required to restore the gas flow stability, and is used to adjust the control parameters to compensate for the gas flow offset. The adjustment strategy of gradual correction is a method for adjusting control parameters. When the gas flow is unstable, the gas flow is optimized by gradual adjustment rather than sharp change to reduce the impact on the oxidation process. The uneven temperature distribution means that there are differences in the temperatures of different regions inside the oxidation furnace, which may lead to inconsistent quality of the oxide layer. The temperature deviation refers to the difference between the actual temperature and the set target temperature, and is used to evaluate and adjust the temperature control in the oxidation process. The oxidation sensitivity refers to the degree of response of different regions to temperature changes, which affects the oxidation rate and the quality of the oxide layer. The precise adjustment of control parameters is a method for adjusting control parameters. When the temperature distribution is uneven, the oxidation process is optimized by precisely adjusting parameters such as temperature. The pressure fluctuation refers to the irregular change of the pressure inside the oxidation furnace, which may affect the stability of the oxidation atmosphere. The pressure change refers to the difference between the actual pressure and the set pressure, and is used to evaluate and adjust the pressure control in the oxidation process. The pressure compensation time refers to the time required to restore the pressure stability, and is used to adjust the control parameters to compensate for the pressure change. The real-time adjustment of control parameters is a method for adjusting control parameters. When the pressure fluctuates, the pressure and other parameters are adjusted in real time to maintain the stability of the oxidation process.

[0114] It can be understood that the present application describes a method for dynamically adjusting control parameters based on the changes in the furnace environment in an oxidation furnace. This method intelligently adjusts the control parameters to optimize the oxidation process by real-time monitoring of the changes in the gas flow, temperature, and pressure inside the furnace.

[0115] When it is detected that the gas flow in the furnace is unstable, the device will adopt the adjustment strategy of gradual correction to correct the control parameters according to the gas flow offset and the stable compensation time. This method helps to smooth the gas flow changes, reduce the interference to the oxidation process, and thus improve the uniformity and quality of the oxide layer.

[0116] During the oxidation process, if it is found that the temperature distribution is uneven, the device will precisely adjust the control parameters according to the temperature deviation and the oxidation sensitivity of each region. This precise adjustment helps to ensure the consistency of the oxidation rate on the entire wafer surface and avoid the problems of oxide layer quality caused by temperature differences.

[0117] When the pressure in the furnace fluctuates, the device will adjust the control parameters in real time according to the pressure change and the pressure compensation time. This real-time adjustment helps to maintain the stability of the oxidation atmosphere and ensure the continuity and uniformity of the oxidation process.

[0118] Generally speaking, by comprehensively considering the changes in the gas flow, temperature, and pressure inside the furnace, this application realizes the intelligent and dynamic adjustment of the control parameters of the oxidation furnace. This method not only improves the quality and uniformity of the oxide layer but also enhances the flexibility and adaptability of the production process, which is of great significance for improving the performance and reliability of semiconductor devices. Through this dynamic adjustment strategy, it is possible to better cope with the uncertainties in the production process and improve the overall efficiency and quality of semiconductor manufacturing.

[0119] This embodiment provides an optimization method for the control parameters of an oxidation furnace used in chip manufacturing. By adjusting the control parameters, it is possible to ensure that the oxidation degree of each wafer is consistent, reduce defects caused by uneven oxidation, and improve the qualification rate of the wafers. The oxidation prediction model can help avoid defects such as over-oxidation or under-oxidation and improve the quality of the wafers. Optimizing the oxidation process can reduce material waste and production time and lower production costs. Through accurate prediction and dynamic adjustment, the oxidation process of the wafers can be effectively controlled, thereby improving the yield of the oxidation process of the wafers.

[0120] In a feasible implementation manner, before the step of obtaining the sensor parameters during the wafer oxidation process, it includes:

[0121] Obtain historical sensor data samples and wafer oxidation result labels;

[0122] Input the historical sensor data samples into a preset model to be trained;

[0123] Predict the wafer oxidation process through the model to be trained and determine the wafer oxidation prediction result;

[0124] Judge the difference between the wafer oxidation prediction result and the wafer oxidation result label to obtain a prediction loss value;

[0125] If the prediction loss value does not meet the preset loss standard, return to the step of predicting the wafer oxidation through the model to be trained and determining the wafer oxidation prediction result until the prediction loss value meets the loss standard and then stop training to obtain an oxidation prediction model that meets the loss standard.

[0126] It should be noted that historical sensor data samples refer to sensor data collected during past wafer oxidation processes. These data record various parameters during the oxidation process, such as temperature, pressure, gas flow rate, etc., and are used to train prediction models. Wafer oxidation result labels are marks corresponding to the historical sensor data samples, and these labels are usually determined by quality inspection steps, indicating whether the actual oxidation result meets the expected standard. The model to be trained refers to the model framework set before the start of training. This model has not learned historical data and needs to optimize its parameters and prediction ability through the training process. Wafer oxidation prediction results refer to the expected wafer oxidation results obtained by the model to be trained based on the input historical sensor data samples, and are used to compare with the actual result labels. The prediction loss value refers to the difference measure between the wafer oxidation prediction result and the wafer oxidation result label, and is used to evaluate the accuracy of the model prediction. The preset loss standard refers to a threshold set during the model training process. When the prediction loss value is lower than this standard, it is considered that the prediction accuracy of the model has reached an acceptable level. The oxidation prediction model refers to a model that has been trained and meets the preset loss standard, and this model can accurately predict the results of the wafer oxidation process.

[0127] It can be understood that this application describes a method for predicting and optimizing the wafer oxidation process based on an oxidation prediction model trained from historical data. This method involves the following key steps:

[0128] Data collection: Collect historical sensor data samples and wafer oxidation result labels, which are the basis for training the prediction model.

[0129] Model training: Input the historical sensor data samples into the model to be trained and use these data to train the model so that it can learn the relationship between the parameters and results during the oxidation process.

[0130] Prediction and evaluation: Use the trained model to predict the wafer oxidation process and compare its prediction results with the actual wafer oxidation result labels to calculate the prediction loss value.

[0131] Model optimization: If the prediction loss value does not meet the preset loss standard, it means that the prediction accuracy of the model needs to be improved. It is necessary to return and continue training the model until the prediction loss value meets the standard.

[0132] Model application: Once the model meets the preset loss standard, it can be used to predict the wafer oxidation process in real time, guide the adjustment of the control parameters of the oxidation furnace, and optimize the quality of the oxide layer.

[0133] The advantage of this method is that it can improve the understanding and control of the oxidation process through machine learning techniques, reduce the dependence on experience, and improve production efficiency and product quality. By continuously optimizing the model, it can better handle the variables in the production process, achieve more precise process control, and ultimately improve the performance and reliability of semiconductor devices.

[0134] Exemplarily, to facilitate understanding of the implementation process of the oxidation furnace control parameter optimization method for chip manufacturing obtained by combining the above-described first embodiment, please refer to Figure 2 , Figure 2 which provides a schematic diagram of the brief process of an oxidation furnace control parameter optimization method for chip manufacturing. Specifically:

[0135] Collect sensor data and corresponding oxidation result labels during the wafer oxidation process for training the oxidation prediction model. Input the collected data into a pre-set model that has not been trained yet. Use the model to be trained to predict the wafer oxidation process and obtain the prediction result. Compare the difference between the model prediction result and the actual oxidation result label, and calculate the prediction loss value. If the prediction loss value does not meet the preset standard, then it is necessary to return to the step of using the model to be trained to predict the wafer oxidation process and obtain the prediction result until the prediction loss value of the model meets the standard. At this time, the obtained model can be used for actual wafer oxidation prediction. During the actual wafer oxidation process, obtain the sensor parameters in real time. Input the sensor parameters obtained in real time into the trained oxidation prediction model. According to the input sensor parameters, the model predicts the result of wafer oxidation. According to the prediction result, dynamically adjust the control parameters of the oxidation furnace. Calculate the deviation between the prediction result and the target value, and calculate the deviation of the control parameters that need to be adjusted accordingly. According to the different stages of wafer oxidation (heating, oxidation, cooling), adjust the adjustment strategy of the control parameters. According to the change trend of the prediction result (oxidation rate too fast, too slow, or stable), adjust the control parameter adjustment strategy. According to the process environment factors monitored in real time (such as unstable air flow, uneven temperature distribution, pressure fluctuation), correct the control parameter adjustment strategy. Convert the calculated deviation of the control parameters into the actual control parameter adjustment value. Dynamically adjust the control parameters of the oxidation furnace according to the actual control parameter adjustment value.

[0136] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the oxidation furnace control parameter optimization method for chip manufacturing of the present application. Based on this technical concept, more forms of simple transformations are within the protection scope of the present application.

[0137] The present application also provides an oxidation furnace control parameter optimization device for chip manufacturing. Please refer to Figure 3 which includes:

[0138] An acquisition module 10, configured to acquire sensor parameters during the wafer oxidation process;

[0139] A prediction module 20, configured to input the sensor parameters into a preset oxidation prediction model to determine a wafer oxidation prediction result, wherein the oxidation prediction model is obtained by training a preset model to be trained with historical sensor data samples and wafer oxidation result labels;

[0140] An adjustment module 30, configured to dynamically adjust oxidation furnace control parameters according to the wafer oxidation prediction result.

[0141] And / or, the oxidation furnace control parameter optimization device for chip manufacturing includes:

[0142] A first acquisition module, configured to acquire historical sensor data samples and wafer oxidation result labels;

[0143] A first input module, configured to input the historical sensor data samples into a preset model to be trained;

[0144] A first determination module, configured to predict the wafer oxidation process through the model to be trained to determine a wafer oxidation prediction result;

[0145] A first judgment module, configured to judge the difference between the wafer oxidation prediction result and the wafer oxidation result label to obtain a prediction loss value;

[0146] A first loop module, configured to, if the prediction loss value does not meet a preset loss criterion, return to the step of predicting wafer oxidation through the model to be trained to determine a wafer oxidation prediction result, and stop training until the prediction loss value meets the loss criterion, so as to obtain an oxidation prediction model that meets the loss criterion.

[0147] And / or, the adjustment module 30 includes:

[0148] A first calculation module, configured to calculate a corresponding control parameter adjustment deviation according to the deviation between the wafer oxidation prediction result and a preset oxidation target value;

[0149] A first adjustment module, configured to adjust a preset control parameter adjustment strategy according to the sensitivity of control parameters in different stages during the wafer oxidation process;

[0150] A second adjustment module, configured to adjust the control parameter adjustment strategy according to the change trend of the wafer oxidation prediction result;

[0151] A first correction module, configured to correct the control parameter adjustment strategy according to process environment factors monitored in real time;

[0152] A third adjustment module, configured to adjust the control parameter adjustment deviation to an actual control parameter adjustment value according to the adjusted control parameter adjustment strategy.

[0153] A fourth adjustment module, configured to dynamically adjust the oxidation furnace control parameters according to the actual control parameter adjustment value.

[0154] And / or, the first adjustment module includes:

[0155] A second determination module, configured to determine the current wafer oxidation stage.

[0156] A first correction module, configured to, if the wafer oxidation stage is the heating stage, adjust the preset control parameter adjustment strategy to a temperature deviation, and perform a rapid correction by using an adjustment strategy with a large adjustment amplitude.

[0157] A second correction module, configured to, if the wafer oxidation stage is the oxidation stage, adjust the control parameter adjustment strategy to focus on controlling the temperature and oxygen partial pressure, and accurately adjust the temperature and oxygen partial pressure deviations according to different periods of the oxidation stage.

[0158] A third correction module, configured to, if the wafer oxidation stage is the cooling stage, adjust the control parameter adjustment strategy to focus on controlling the temperature cooling rate, and perform a slow correction by using an adjustment strategy with a small adjustment amplitude.

[0159] And / or, the second adjustment module includes:

[0160] A fourth correction module, configured to, if the predicted wafer oxidation result is that the oxidation rate is too fast, adjust the control parameter adjustment strategy to an adjustment strategy with a gradually decreasing amplitude according to the remaining time of the oxidation expectation.

[0161] A fifth correction module, configured to, if the predicted wafer oxidation result is that the oxidation rate is too slow, adjust the control parameter adjustment strategy to an adjustment strategy with a gradually increasing amplitude according to the remaining time of the oxidation expectation.

[0162] A sixth correction module, configured to, if the predicted wafer oxidation result is that the oxidation process operates stably, adjust the control parameter adjustment strategy to maintain the existing control parameter adjustment value.

[0163] And / or, the first correction module includes:

[0164] A seventh correction module, configured to, when there is an unstable phenomenon in the furnace gas flow, correct the control parameter adjustment strategy to a control parameter adjustment strategy with a gradual correction according to the gas flow offset and the stable compensation time.

[0165] The eighth correction module is used to correct the control parameter adjustment strategy to accurately adjust the control parameters according to the temperature deviation and the oxidation sensitivity of each region when the temperature distribution is uneven;

[0166] The ninth correction module is used to correct the control parameter adjustment strategy to adjust the control parameters in real time according to the pressure change and the adjustment strategy of the pressure compensation time when the pressure fluctuates.

[0167] The oxidation furnace control parameter optimization device for chip manufacturing provided by the present application adopts the oxidation furnace control parameter optimization method in the above embodiment, and can solve the technical problem of low yield of produced wafers. Compared with the prior art, the beneficial effects of the oxidation furnace control parameter optimization device for chip manufacturing provided by the present application are the same as those of the oxidation furnace control parameter optimization method provided by the above embodiment, and other technical features in the oxidation furnace control parameter optimization device for chip manufacturing are the same as the features disclosed in the method of the above embodiment, and will not be elaborated here.

[0168] The present application provides an oxidation furnace control parameter optimization device for chip manufacturing. The oxidation furnace control parameter optimization device for chip manufacturing includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the oxidation furnace control parameter optimization method in the first embodiment above.

[0169] Next, refer to Figure 4 , which shows a schematic structural diagram of an oxidation furnace control parameter optimization device for chip manufacturing suitable for implementing the embodiments of the present application. The oxidation furnace control parameter optimization device for chip manufacturing in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, tablet computers, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PMPs (Portable Media Player), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The oxidation furnace control parameter optimization device shown is only an example, and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0170] As Figure 4As shown, the equipment for optimizing the control parameters of the oxidation furnace for chip manufacturing may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the equipment for optimizing the control parameters of the oxidation furnace for chip manufacturing are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the equipment for optimizing the control parameters of the oxidation furnace for chip manufacturing to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows the equipment for optimizing the control parameters of the oxidation furnace for chip manufacturing with various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems can be alternatively implemented or had.

[0171] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0172] The equipment for optimizing the control parameters of the oxidation furnace for chip manufacturing provided by the present application adopts the method for optimizing the control parameters of the oxidation furnace for chip manufacturing in the above-mentioned embodiment, and can solve the technical problem of low yield of produced wafers. Compared with the prior art, the beneficial effects of the equipment for optimizing the control parameters of the oxidation furnace for chip manufacturing provided by the present application are the same as those of the method for optimizing the control parameters of the oxidation furnace for chip manufacturing provided by the above-mentioned embodiment, and other technical features in the equipment for optimizing the control parameters of the oxidation furnace for chip manufacturing are the same as the features disclosed in the method of the previous embodiment, and will not be elaborated here.

[0173] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0174] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0175] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for optimizing the control parameters of the oxidation furnace for chip manufacturing in the above embodiments.

[0176] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0177] The above computer-readable storage medium can be included in the device for optimizing the control parameters of the oxidation furnace for chip manufacturing; it can also exist alone without being assembled into the device for optimizing the control parameters of the oxidation furnace for chip manufacturing.

[0178] The above computer-readable storage medium carries one or more programs, which, when executed by the oxidation furnace control parameter optimization device for chip manufacturing, cause the oxidation furnace control parameter optimization device for chip manufacturing to: obtain sensor parameters during the wafer oxidation process;

[0179] input the sensor parameters into a preset oxidation prediction model to determine the wafer oxidation prediction result, where the oxidation prediction model is obtained by training a preset model to be trained with historical sensor data samples and wafer oxidation result labels;

[0180] dynamically adjust the oxidation furnace control parameters according to the wafer oxidation prediction result.

[0181] Computer program code for performing the operations of the present application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by connecting through an Internet service provider via the Internet).

[0182] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0183] The modules involved in the embodiments of the present application can be implemented in software or in hardware. In some cases, the name of the module does not constitute a limitation on the unit itself.

[0184] The readable storage medium provided by the present application is a computer-readable storage medium, and the computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for optimizing the control parameters of the oxidation furnace for chip manufacturing, which can solve the technical problem of low yield of wafers produced. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as those of the method for optimizing the control parameters of the oxidation furnace for chip manufacturing provided in the above embodiments, and will not be elaborated here.

[0185] The present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the control parameters of the oxidation furnace for chip manufacturing as described above are implemented.

[0186] The computer program product provided by the present application can solve the technical problem of low yield of wafers produced. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the method for optimizing the control parameters of the oxidation furnace for chip manufacturing provided in the above embodiments, and will not be elaborated here.

[0187] The above are only some embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made under the technical concept of the present application by using the content of the specification and drawings of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A method for optimizing control parameters of an oxidation furnace for chip manufacturing, characterized in that: The method includes: Obtain sensor parameters during wafer oxidation; Inputting the sensor parameters into a preset oxidation prediction model to determine a wafer oxidation prediction result, wherein the oxidation prediction model is obtained by training a preset to-be-trained model using historical sensor data samples and wafer oxidation result labels; According to the wafer oxidation prediction result, the oxidation furnace control parameters are dynamically adjusted.

2. The method according to claim 1, characterized in that The step of obtaining sensor parameters during wafer oxidation process includes: Obtain historical sensor data samples and wafer oxidation result labels; Inputting the historical sensor data samples into a preset model to be trained; Predicting the wafer oxidation process by using the model to be trained, and determining a wafer oxidation prediction result; Determine the difference between the wafer oxidation prediction result and the wafer oxidation result label to obtain a predicted loss value; If the predicted loss value does not meet the preset loss standard, return to the step of predicting wafer oxidation through the model to be trained and determining the wafer oxidation prediction result, and stop training until the predicted loss value meets the loss standard to obtain an oxidation prediction model that meets the loss standard.

3. The method according to claim 1, characterized in that The step of dynamically adjusting the oxidation furnace control parameters comprises: Calculating a corresponding control parameter adjustment deviation according to a deviation between the wafer oxidation prediction result and a preset oxidation target value; Adjust the preset control parameter adjustment strategy according to the sensitivity of the control parameters at different stages of the wafer oxidation process; Adjusting the control parameter adjustment strategy according to the change trend of the wafer oxidation prediction result; Modifying the control parameter adjustment strategy according to the process environment factors monitored in real time; According to the modified control parameter adjustment strategy, converting the control parameter adjustment deviation into an actual control parameter adjustment value; The oxidation furnace control parameters are dynamically adjusted according to the actual control parameter adjustment values.

4. The method according to claim 3, characterized in that The step of adjusting the preset control parameter adjustment strategy according to the sensitivity of the control parameters at different stages of the wafer oxidation process includes: Determine the current wafer oxidation stage; If the wafer oxidation stage is a heating stage, the preset control parameter adjustment strategy is adjusted to a temperature deviation, and a large-scale adjustment adjustment strategy is used for rapid correction; If the wafer oxidation stage is the oxidation stage, the control parameter adjustment strategy is adjusted to focus on controlling the temperature and the oxygen partial pressure, and the deviation of the temperature and the oxygen partial pressure is accurately adjusted according to different periods of the oxidation stage; If the wafer oxidation stage is a cooling stage, the control parameter adjustment strategy is adjusted to focus on controlling the temperature cooling rate, and a small adjustment strategy is used for slow correction.

5. The method according to claim 3, characterized in that The step of adjusting the control parameter adjustment strategy according to the change trend of the wafer oxidation prediction result includes: If the wafer oxidation prediction result indicates that the oxidation rate is too fast, the control parameter adjustment strategy is adjusted to adopt a step-by-step reduction adjustment strategy according to the expected remaining time of oxidation; If the wafer oxidation prediction result is that the oxidation rate is too slow, adjusting the control parameter adjustment strategy to adopt a step-by-step increase adjustment strategy according to the expected remaining time of oxidation; If the wafer oxidation prediction result indicates that the oxidation process is operating stably, the control parameter adjustment strategy is adjusted to maintain the existing control parameter adjustment value.

6. The method according to claim 3, characterized in that The step of modifying the control parameter adjustment strategy according to the process environment factors monitored in real time includes: When the airflow in the furnace is unstable, the control parameter adjustment strategy is modified to adopt a step-by-step correction adjustment strategy control parameter according to the airflow offset and the stable compensation time; When the temperature distribution is uneven, the control parameter adjustment strategy is modified to accurately adjust the control parameters according to the temperature deviation and the oxidation sensitivity of each area; When the pressure fluctuates, the control parameter adjustment strategy is modified to adjust the control parameters in real time according to the adjustment strategy of the pressure change and the pressure compensation time.

7. A device for optimizing control parameters of an oxidation furnace for chip manufacturing, characterized in that: The device comprises: An acquisition module, used for acquiring sensor parameters during the wafer oxidation process; A prediction module, used for inputting the sensor parameters into a preset oxidation prediction model to determine a wafer oxidation prediction result, wherein the oxidation prediction model is obtained by training a preset model to be trained using historical sensor data samples and wafer oxidation result labels; The adjustment module is used to dynamically adjust the oxidation furnace control parameters according to the wafer oxidation prediction result.

8. An oxidation furnace control parameter optimization device for chip manufacturing, characterized in that: The device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for optimizing control parameters of an oxidation furnace for chip manufacturing as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for optimizing control parameters of an oxidation furnace for chip manufacturing according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing control parameters of an oxidation furnace for chip manufacturing according to any one of claims 1 to 6 are implemented.

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