CN117870125A

CN117870125A - Semiconductor processing equipment environment regulation and control system and method

Info

Publication number: CN117870125A
Application number: CN202410200404.9A
Authority: CN
Inventors: 石军
Original assignee: Suzhou Lyade Intelligent Equipment Co ltd
Current assignee: Suzhou Lyade Intelligent Equipment Co ltd
Priority date: 2024-02-23
Filing date: 2024-02-23
Publication date: 2024-04-12

Abstract

The embodiment of the specification provides an environment regulation and control system and method for semiconductor processing equipment, wherein the system comprises a temperature and humidity monitoring device, interaction equipment, dust index monitoring equipment, temperature regulation equipment, movable purification equipment and control equipment. The temperature and humidity monitoring device is configured to monitor and acquire a temperature and humidity index of a workshop; the dust index monitoring device is configured to monitor and acquire a dust index of a workshop; the temperature regulating device is configured to regulate the temperature of the processing device; the movable cleaning device is configured to remove dust from the workshops; the interactive device is configured to display the current process, the temperature and humidity index, the dust index, and to obtain user feedback. The control equipment is configured to acquire a temperature and humidity index and a dust index; determining a temperature adjustment parameter; generating a temperature regulation instruction; determining a purification parameter; generating a dust removal instruction; and sending an early warning to the user; determining an initialization value of a device parameter; generating a temperature adjustment command and a purge command.

Description

Semiconductor processing equipment environment regulation and control system and method

Technical Field

The present disclosure relates to the field of environmental control of processing equipment, and in particular, to an environmental control system and method for semiconductor processing equipment.

Background

In the modern electronics industry, semiconductor processing equipment is commonly used to manufacture integrated circuits and other semiconductor devices. When semiconductor products are processed, bad production and equipment faults can be caused by severe environments such as temperature and humidity fluctuation, dust accumulation, particle pollution and the like. Therefore, to ensure the quality of the semiconductor finished product, these devices often need to operate in a highly controlled environment. In order to control the working environment of the equipment, CN105867256a proposes a circuit board production shop cleaning system. The system uses the inlet and outlet production device to remove dust from the interior of the production workshop, and uses the detection device to detect the dust content of the production workshop, so that the dust-free production environment can be ensured. However, the system is not related to temperature and humidity monitoring and adjustment of a production workshop, which may result in low production quality or not capable of effectively coping with complex production process requirements.

Therefore, in order to improve the quality and efficiency of semiconductor manufacturing, it is necessary to develop a comprehensive environmental control system to improve the reliability and productivity of semiconductor manufacturing.

Disclosure of Invention

One or more embodiments of the present specification provide an environmental conditioning system for a semiconductor processing tool. The system comprises a temperature and humidity monitoring device, interaction equipment, dust index monitoring equipment, temperature adjusting equipment, movable purifying equipment and control equipment. The temperature and humidity monitoring device is configured to monitor and acquire a temperature and humidity index of a workshop; the dust index monitoring device is configured to monitor and acquire a dust index of a workshop; the temperature regulating device is configured to regulate the temperature of the processing device; the mobile decontamination apparatus is configured to remove dust from the plant; the interactive device is configured to display the current process, the temperature and humidity index, the dust index, and obtain user feedback. The control equipment is configured to acquire the temperature and humidity index and the dust index based on the temperature and humidity monitoring device and the dust index monitoring equipment; determining a temperature adjustment parameter based on the current process and the temperature and humidity index; generating a temperature adjustment instruction based on the temperature adjustment parameter, and sending the temperature adjustment instruction to the temperature adjustment device; determining a purge parameter based on the current process recipe and the dust index; generating a dust removal instruction based on the purification parameters, and sending the dust removal instruction to the movable purification equipment; and responding to at least one of the temperature and humidity index and the dust index which does not meet the preset index condition, and sending an early warning to a user through the interaction equipment; determining an initialization value of a device parameter in response to obtaining restart feedback from the interaction device; and generating a temperature regulation instruction and a purification instruction based on the initialization value, and respectively sending the temperature regulation instruction and the purification instruction to the temperature regulation equipment and the movable purification equipment.

One or more embodiments of the present specification provide a method of environmental conditioning of semiconductor processing equipment. The method is executed by control equipment of an environment regulation and control system of semiconductor processing equipment, and comprises the steps of acquiring a temperature and humidity index and a dust index based on a temperature and humidity monitoring device and dust index monitoring equipment; determining a temperature adjustment parameter based on the current process and the temperature and humidity index; generating a temperature regulation instruction based on the temperature regulation parameter, and sending the temperature regulation instruction to temperature regulation equipment; determining a purge parameter based on the current process recipe and the dust index; the purification parameters at least comprise dust removal power; generating a dust removal instruction based on the purification parameters, and sending the dust removal instruction to movable purification equipment; and responding to at least one of the temperature and humidity index and the dust index which does not meet the preset index condition, and sending an early warning to a user through interaction equipment; determining an initialization value of a device parameter in response to obtaining restart feedback from the interaction device; the equipment parameters comprise a temperature regulation parameter and the purification parameter; and generating a temperature regulation instruction and a purification instruction based on the initialization value, and respectively transmitting the temperature regulation instruction and the purification instruction to the temperature regulation equipment and the movable purification equipment.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a system schematic diagram of an environmental conditioning system for a semiconductor processing tool according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow chart of a method of environmental conditioning of a semiconductor processing tool according to some embodiments of the present disclosure;

FIG. 3 is an exemplary diagram illustrating determining thermal energy scheduling parameters according to some embodiments of the present disclosure;

FIG. 4 is an exemplary flow chart for determining purge parameters according to some embodiments of the present disclosure;

FIG. 5 is an exemplary schematic diagram of a pest index distribution prediction model according to some embodiments of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

The embodiment of the invention provides an environment regulation and control system and method for semiconductor processing equipment, which integrate the functions of temperature and humidity monitoring, dust index monitoring, temperature regulation, air purification and the like, comprehensively control the working environment of the semiconductor equipment, and improve the production quality, the production efficiency, the production stability and the like of semiconductor products.

Fig. 1 is a system schematic diagram of an environmental conditioning system for a semiconductor processing tool according to some embodiments of the present disclosure. As shown in fig. 1, the system 100 includes a temperature and humidity monitoring apparatus 110, an interaction device 120, a dust index monitoring device 130, a temperature adjustment device 140, a movable cleaning device 150, and a control device 160.

The temperature and humidity monitoring device 110 is a device for monitoring the temperature and humidity index. For example, it may be a thermometer, hygrometer and other sensors that can monitor temperature and humidity. For a description of the temperature and humidity index, see the description of step 210 in fig. 2.

In some embodiments, the temperature and humidity monitoring device may be configured to monitor and acquire a temperature and humidity index of the plant.

In some embodiments, the temperature and humidity monitoring device may further include an infrared temperature measurement component 111. The infrared temperature measuring part 111 refers to a device for detecting the temperature of an article by detecting infrared radiation. For example, an infrared temperature measuring gun, an infrared temperature measuring camera, an infrared temperature measuring module and the like can be adopted. In some embodiments, the infrared thermometry component may be configured to detect infrared temperature data of the semiconductor product.

The interactive device 120 refers to a device that interacts with computer systems, users, or other devices. For example, it may be a touch screen display, a control panel, or the like.

In some embodiments, the interactive device may be configured to display the current process recipe, the temperature and humidity index, the dust index, and to obtain user feedback. User feedback refers to the user's reflection. In some embodiments, the user feedback may include user adjustments to the current process, logging of desired values for plant temperature, feedback information for dust removal effects, etc.

The dust index monitoring device 130 refers to a device for monitoring dust index. For example, an air quality sensor, a laser scatterometer, an optical dust meter, a resistive dust sensor, or the like may be used. For a description of the dust index, see the description of step 210 in fig. 2.

In some embodiments, the dust index monitoring device may be configured to monitor and acquire a dust index of the plant. In some embodiments, the dust index monitoring device may be further configured to monitor a dust concentration profile of the operating space. For a detailed description of the operating space, dust concentration distribution see the relevant description of fig. 4.

The temperature adjusting device 140 refers to a device that adjusts or controls the temperature of the environment. For example, thermostats, cooling towers, industrial fans, industrial air conditioners, and the like. In some embodiments, the temperature regulation apparatus is configured to regulate the temperature of the processing apparatus.

In some embodiments, the temperature regulating apparatus may further comprise a thermal energy storage 141. The thermal energy storage device 141 refers to an apparatus capable of storing and releasing thermal energy. For example, heat pumps, hot water tanks, phase change energy storage systems, etc.

In some embodiments, the thermal energy storage device may be configured to release thermal energy outwardly and collect and store desired thermal energy from the product to be processed and/or the finished product processed.

In some embodiments, the thermal energy storage device involves both endothermic and preheating processes. Wherein the thermal energy storage device and the product do not have to be in physical contact during heat absorption and preheating. For example, the product may be placed in a specific space, the inner walls of which are filled with a thermally conductive material. The heat conducting material can transfer heat energy radiated by the product and the heat energy storage device, and heat absorption and preheating of the product are realized.

As an example, after the product is processed, the temperature may be too high, and the heat energy storage device may be used to absorb heat, and when the product temperature satisfies the conditions, the product is placed in the natural environment. For another example, after the product to be processed is obtained from the natural environment, the temperature of the product to be processed may be too low, and the thermal energy storage device may be used to release heat to preheat the product to be processed, and when the product temperature satisfies the condition, the product is placed in the processing environment. In some embodiments, the thermal energy storage device may implement a schedule for thermal energy by both endothermic and preheating processes.

The movable purifying apparatus 150 refers to an air purifying apparatus having mobility. For example, an air cleaner with a roller, a cleaner with an extension tube, etc. may be mentioned. In some embodiments, the mobile decontamination apparatus may be configured to remove dust from a plant in a mobile manner.

The control device 160 refers to a device for controlling a system, machine, device or process. For example, it may be a computer, a Programmable Logic Controller (PLC), or other controller.

In some embodiments, the control device may be configured to determine the thermal energy scheduling parameters based on the current process recipe and process criteria, the temperature and humidity of the current plant. For a detailed description of the thermal energy scheduling parameters, the process schedule, see the associated description of step 220 in fig. 2.

See fig. 2, 3, 4 and 5 for further description of the control device.

In some embodiments, the system 100 may further include a distributed harmful substance monitoring device 170, a gas flow detector 180.

The distributed harmful substance monitoring apparatus 170 refers to a device capable of monitoring the harmful substance index of a plant at a plurality of locations. For example, a gas sensor for monitoring a harmful gas, a sound level meter for monitoring noise, a volatile organic compound monitor for monitoring a volatile substance, and the like may be mentioned. See fig. 4 for further description of harmful substances and its associated description.

The gas flow direction detector 180 refers to a device that detects air flow parameters. For example, it may be a thermal imaging gas detector, a wireless gas sensor, etc. Air flow parameters refer to parameters of various properties and characteristics of air movement and flow. In some embodiments, the air flow parameters may include air flow direction, air flow velocity, and the like.

In some embodiments, the environmental data such as the temperature and humidity index and the dust index in the workshop can be comprehensively monitored by the environmental regulation system of the semiconductor processing equipment, and the semiconductor processing environment is optimized by regulating the temperature, performing dust removal and other operations. The data can be displayed to a user through the interaction equipment, so that an operator can know the production condition in time, and the production management efficiency is improved. The environment regulation and control system of the semiconductor processing equipment can also send an early warning to a user through the interaction equipment, so that the user can conveniently prevent potential environmental problems in advance, and the production safety can be guaranteed. The environment regulation and control system of the whole semiconductor processing equipment can intelligently monitor and regulate and control the processing environment, so that the quality and controllability of the semiconductor processing environment are improved, and the production efficiency and the production quality of a higher level are realized.

Fig. 2 is an exemplary flow chart of a method of environmental conditioning of a semiconductor processing tool according to some embodiments of the present disclosure. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by a control device of a semiconductor processing tool environmental conditioning system.

Step 210, acquiring a temperature and humidity index and a dust index based on the temperature and humidity monitoring device and the dust index monitoring equipment.

The temperature and humidity index refers to an index reflecting the temperature and humidity of a plant. The dust index refers to an index reflecting the distribution of dust in a plant. In some embodiments, the dust index may be expressed by a dust concentration.

Step 220, determining a temperature adjustment parameter based on the current process and the temperature and humidity index.

The process flow refers to the process of producing the product. In some embodiments, the process may include solder paste printing, part mounting, oven curing, reflow soldering, and the like.

The temperature adjustment parameter refers to a parameter for guiding the temperature adjustment device to perform temperature adjustment. In some embodiments, the temperature adjustment parameter may include a regulation magnitude of temperature, or the like.

In some embodiments, the control device may determine the temperature adjustment parameter in a variety of ways. For example, the control device may determine the expected temperature according to the process standard corresponding to the current process step, determine the current temperature difference according to the current temperature and humidity index and the expected temperature, determine the current temperature difference as the temperature range to be regulated and controlled, and further determine the temperature regulation parameter.

In some embodiments, the temperature adjustment parameters further comprise thermal energy scheduling parameters of the thermal energy storage device.

The thermal energy scheduling parameter refers to a related parameter for controlling the thermal energy storage device to store or release heat. In some embodiments, the thermal energy scheduling parameter may include an endothermic amount, an endothermic power, of an endothermic process of the thermal energy storage device; exothermic heat, exothermic power, etc. of the exothermic process.

In some embodiments, the control device may determine the thermal energy scheduling parameters in a variety of ways. For example, the control device may determine the thermal energy scheduling parameters based on the current process recipe and process criteria, the current shop floor temperature and humidity.

In some embodiments, the control device may determine, based on the current process, whether the thermal energy storage device needs to absorb heat or release heat, for example, when the product processing is completed, the processed product needs to be transferred from the processing device to the natural environment, where the temperature of the processed product is far above the ambient temperature, and the thermal energy storage device needs to absorb heat in advance to transfer to the natural environment; when the product needs to be processed, the product to be processed needs to be transferred to processing equipment from the natural environment, and the temperature of the product to be processed is far lower than that of the processing equipment, so that the product to be processed needs to be pre-heated by the heat energy storage device and can be transferred to the processing equipment.

In some embodiments, the control apparatus may calculate the heat absorption or release of the thermal energy storage device by equation (2):

U＝M×N×(T ₂ -T ₁ ) (2)

when the heat energy storage device needs to absorb heat, U is the heat absorption quantity, and when the heat energy storage device needs to release heat, U is the heat release quantity; m is the mass of the semiconductor product; n is the specific heat capacity of the semiconductor product; t (T) ₂ Is the temperature of a finished product; t (T) ₁ Is the temperature of the product to be processed.

In some embodiments, the control device may determine the temperature of the processing device in the process standard as the estimated finished product temperature T ₂ The method comprises the steps of carrying out a first treatment on the surface of the The temperature and humidity of the workshop can be determined as the temperature T of the product to be processed ₁ The method comprises the steps of carrying out a first treatment on the surface of the The specific heat capacity N of the semiconductor product may be preset based on a priori experience.

In some embodiments, the control apparatus may establish the first preset table based on a historical process schedule, a historical thermal energy storage device model, a historical temperature of the product to be processed, a historical process finished temperature, and a historical heat absorption/release rate. The first preset table includes a historical process schedule, a historical thermal energy storage device model, a historical temperature of a product to be processed, a historical process finished product temperature, and a correspondence of different historical heat absorption/release rates. The control device may determine the current heat absorption/release power by referring to the first preset table according to the current process, the type of the thermal energy storage device, the temperature of the product to be processed, the temperature of the finished product to be processed.

In some embodiments of the present disclosure, a semiconductor product may be preheated or pre-heat treated by using a thermal energy storage device, so that a delicate and fragile semiconductor product may undergo a relatively gentle temperature rise/fall process without severe temperature fluctuations, resulting in defects in product quality; and by using the heat energy storage device, the heat energy utilization rate can be improved, the energy consumption can be reduced, and the production cost can be saved.

At step 230, purge parameters are determined based on the current process recipe and the dust index.

The cleaning parameter refers to a control parameter for controlling the movable cleaning apparatus to perform a dust removing operation. In some embodiments, the purging parameters may include dust removal power, or the like.

In some embodiments, the control device may determine the purging parameters in a variety of ways. For example, the control device may determine an expected dust index based on the process criteria corresponding to the current process step, and determine the magnitude of the difference based on the current dust index and the expected dust index. The control device may determine the purging parameters by looking up a first preset table based on the magnitude of the difference and the purging capacity (e.g., maximum power) of the movable purging device.

The first preset table may include preset correspondence of the magnitudes of the differences of the various values and the purifying capacity and the like of the movable purifying apparatus, and the purifying parameters. For example, when it is desired to determine the purge parameter, the desired purge parameter can be determined according to the first preset table based on the magnitude of the difference, the purge capacity of the movable purge device. Wherein the first preset table may be constructed based on historical data or a priori experience.

In some embodiments, the control device may also predict the dust index for a future period of time based on the current process step, the subsequent process run, the dust index through a dust index prediction model.

The dust index prediction model refers to a prediction model for evaluating dust index of a product at a future point of time. In some embodiments, the dust index prediction model may be a machine learning model. For example, a convolutional neural network (Convolutional Neural Networks, CNN) model may be used.

In some embodiments, the input of the dust index prediction model may include a current process run, a subsequent process run, a dust index, and outputting the dust index for a future period of time.

In some embodiments, the control device may train the dust index predictive model based on a plurality of first training samples with the first tag. In some embodiments, the first training sample may include a process run at the first historic time and a subsequent process run after the first historic time in the historic processing of the sample product, along with the sample dust index. In some embodiments, the first label may be an actual dust index at a second historical time in the historical processing of the sample product. The first historical time is before the second historical time. In some embodiments, the first training sample may be obtained based on historical data, and the first tag may be obtained based on an actual dust index at a historical point in time corresponding to the historical data.

In some embodiments, the control device may train the dust index prediction model by a plurality of methods based on the first training sample and the first tag. For example, training may be based on a gradient descent method. For example only, a plurality of first training samples with first labels may be input into the initial dust index prediction model, a loss function is constructed from the outputs of the first labels and the initial dust index prediction model, and parameters of the initial dust index prediction model are iteratively updated based on the loss function. When the loss function of the initial dust index prediction model meets the preset condition, model training is completed, and a trained dust index prediction model is obtained. The preset condition may be that the loss function converges, the number of iterations reaches a threshold value, etc.

In some embodiments, the control device may determine the purging parameters based on a dust index for a future period of time. Wherein the purification parameters further comprise dust removal parameters.

The dust removal parameter refers to a control parameter for controlling the movable purification equipment to perform dust removal operation. In some embodiments, the dedusting parameters can include dedusting power, and the like.

In some embodiments, the dedusting parameters can be determined by querying a second preset table. The control device may construct a second preset table based on the dust removal performance of the movable cleaning device. The second preset table comprises the corresponding relation between the dust removal index difference values of different average dust removal indexes and target dust removal indexes and different dust removal parameters.

In some embodiments, the control device may determine a value interval of the difference value of the dust removal index based on the actual situation, select a set of values from the value interval, perform a dust removal experiment using the movable cleaning device, obtain a minimum dust removal power that may reduce the difference value to zero in a specified time, and store the minimum dust removal power in the second preset table. And continuously repeating the numerical selection until all the numerical values in the numerical value interval have the corresponding minimum dust removal power. At this time, the second preset table construction is completed. Wherein the target dust removal index and the prescribed time may be determined based on a priori experience.

When the control device needs to determine the dust removal parameter based on the dust removal index difference value, a second preset table can be queried, and the minimum dust removal power corresponding to the current difference value is selected and determined as the required dust removal parameter.

In some embodiments of the present disclosure, by constructing a preset table, determining the minimum dust removal power corresponding to different dust removal index differences, and determining the required dust removal parameters, it is possible to ensure that the dust content in the air can be reduced to the target dust content within a specified time, and also to avoid noise problems and energy waste caused by excessive power of the movable cleaning apparatus. By utilizing the dust index prediction model to predict the dust index for a period of time in the future and determining the purification parameters, the error of artificial prediction can be reduced, the more accurate dust index is predicted, and the reliability and accuracy of the prediction result are improved.

Step 240, generating a dust removal instruction based on the cleaning parameters, and transmitting the dust removal instruction to the movable cleaning apparatus.

The dust removal instruction refers to an instruction for controlling the movable cleaning apparatus to perform a dust removal operation. In some embodiments, the dedusting instructions may include a removable decontamination apparatus performing a dedusting operation in accordance with decontamination parameters, and the like.

And step 250, sending an early warning to a user through the interaction equipment in response to at least one of the temperature and humidity index and the dust index not meeting the preset index condition.

The preset index condition refers to a critical condition for judging whether the early warning needs to be carried out to the user. In some embodiments, the preset index condition may include the temperature and humidity index being in a preset temperature and humidity range and/or the dust index being in a preset dust concentration range.

The early warning means that the control equipment gives out warning and alarm to the user according to the preset index condition. In some embodiments, the early warning content may include parameter indicators that do not meet the preset condition, a degree of deviation between the actual condition and the preset condition, and the like.

In response to obtaining the restart feedback from the interactive device, an initialization value for the device parameter is determined, step 260.

Restarting feedback refers to feedback that resumes production after suspending production equipment operation. In some embodiments, when receiving the early warning, the user may instruct to suspend the operation of the production equipment, wait for the recovery of various parameter indexes, and send a restart feedback for continuing the production, where the control equipment may receive the restart feedback based on the interaction equipment and confirm the initialization values of the parameters of the movable purification equipment and the temperature adjustment equipment.

The initialization value refers to the preset value of the parameters of the movable purifying equipment and the temperature regulating equipment after production recovery. In some embodiments, the initialization values may include initial temperature adjustment parameters, initial purge parameters, and the like.

In some embodiments, the control device may determine the initialization value in a variety of ways. For example, the control device may determine the initialization value of the device parameter according to whether the time difference between the time when the user resumes production and the early warning time exceeds the preset time difference.

By way of example only, case a: when the time difference between the moment of recovering the production operation and the early warning moment is smaller than the preset time difference, the heat dissipation or the preheating of the production equipment may not be fully completed at the moment, the expected effect cannot be achieved, and then the initialization values of the equipment parameters corresponding to the movable purification equipment and the temperature regulation equipment are the equipment parameters before restarting the production operation.

Case B: when the time difference between the time of recovering the production operation and the early warning time is not smaller than the preset time difference, the movable purifying equipment and the temperature adjusting equipment can be adjusted to be in an initial working state, namely, the initial operation parameters of the equipment are determined to be the initialization values of the corresponding equipment parameters. Wherein the initial operating parameters may be preset based on a priori experience.

Step 270, generating a temperature adjustment instruction and a purge instruction based on the initialization value, and transmitting the temperature adjustment instruction and the purge instruction to the temperature adjustment apparatus and the movable purge apparatus, respectively.

In some embodiments, the control device may directly generate the temperature adjustment command from the initialization value of the temperature adjustment device, generate the purge command from the initialization value of the purge device, and send the purge command to the temperature adjustment device and the movable purge device, respectively.

In some embodiments of the present disclosure, by judging whether the current production condition meets the requirement and performing early warning in time when the current production condition does not meet the requirement, relevant personnel can be effectively reminded to perform treatment, and flaws and damages of products caused by environmental factors are avoided; when production is continued, the initialization values of the temperature adjusting equipment and the purifying equipment are redetermined according to the actual conditions, so that the temperature adjusting equipment and the purifying equipment can be restored to the initial working states, energy sources are saved, and the waste of electric power is reduced.

FIG. 3 is an exemplary schematic diagram illustrating determining thermal energy scheduling parameters according to some embodiments of the present description.

In some embodiments, the control device may determine the product temperature variation trend 381 based on the current process recipe 310, the subsequent process recipe 320, the operating parameters 330 of the semiconductor processing device, the current temperature adjustment parameters 340, the current shop temperature 350; the thermal energy schedule parameters 390 are determined based on the current process recipe, process criteria, product temperature variation trend.

The operating parameters 330 of the semiconductor processing apparatus refer to control parameters that control the semiconductor processing apparatus to perform a production process. In some embodiments, the operating parameters may include production rate, length of time that has been operating, operating power, etc.

The product temperature trend 381 may reflect the current product temperature trend. For example +2%, indicating that the current temperature trend is increasing by 2%.

In some embodiments, the control device may determine the product temperature profile in a variety of ways.

In some embodiments, a plurality of sample data may be obtained from historical production data of a sample product, constructing a corresponding standard vector. For example, elements of the temperature change vector may include current process progress, subsequent process progress, operating parameters, temperature regulation parameters, plant temperature and humidity build-up of the sample product. For example, the current process of the sample product is a, the subsequent process is B, the working parameter is C, the temperature adjustment parameter is D, and the temperature and humidity of the workshop are E, and the corresponding standard vectors are [ (a, B), C, D, E ].

The control device can cluster the standard vectors in the sample data through a clustering algorithm to obtain a plurality of cluster centers. The types of clustering algorithms may include a variety of, for example, the clustering algorithms may include K-Means clustering, density-based clustering methods (DBSCAN), and the like. There is no limitation regarding the kind of clustering algorithm.

When the trend of the temperature change of the current product needs to be determined, constructing a vector to be matched according to the current process, the subsequent process, the working parameters, the temperature adjustment parameters, the workshop temperature and humidity and other parameters of the current product, determining the closest cluster by determining the vector distance between the vector to be matched and the cluster center, and taking the trend of the temperature change corresponding to the cluster center of the cluster as the trend of the temperature change of the product under the current condition. The trend of the temperature change corresponding to the cluster center can be determined through the historical data corresponding to the cluster center.

In some embodiments, the product temperature variation trend 381 is also related to the product characteristics 360 of the semiconductor product. The control device may also determine a product temperature variation trend 381 based on the current process recipe 310, the subsequent process recipe 320, the operating parameters 330 of the semiconductor processing device, the current temperature adjustment parameters 340, the current shop temperature and humidity 350, the product characteristics 360, the infrared temperature data 370, and the product temperature prediction model 380.

The temperature prediction model 380 refers to a predictive model for evaluating the trend of temperature change at a future point in time of a product. In some embodiments, the temperature prediction model may be a machine learning model. For example, a recurrent neural network (Recurrent Neural Network, RNN) model.

As shown in fig. 3, the inputs of the temperature prediction model may include a current process recipe 310, a subsequent process recipe 320, operating parameters 330 of the semiconductor processing device, current temperature adjustment parameters 340, a current shop temperature and humidity 350, infrared temperature data 370 of a current semiconductor product, product characteristics 360, and output corresponding product temperature variation trend 381.

Product characteristics 360 may reflect characteristics and performance of the semiconductor product. In some embodiments, product characteristics may include the material, purpose, size, etc. of the current semiconductor product.

In some embodiments, the control device may train the temperature prediction model based on a plurality of second training samples with the second tag. In some embodiments, the second training samples may include a sample current process run of the sample product at the first historical time, sample infrared temperature data, sample subsequent process runs, sample operating parameters, sample temperature adjustment parameters, sample shop temperature and humidity, and sample product characteristics. In some embodiments, the second label may be an actual temperature change profile of the sample product corresponding at the second historical time. In some embodiments, the second training sample may be obtained based on historical data, and the second tag may be obtained based on an actual temperature profile corresponding to the semiconductor product in the historical data. The first historical time is before the second historical time.

The specific training process of the temperature prediction model is similar to that of the dust index prediction model, and reference may be made to fig. 2 and the related contents.

In some embodiments of the present disclosure, due to the complexity of the semiconductor manufacturing process, the actual temperature of the product tends to deviate from the temperature required by the process standards during actual manufacturing. For example, the welding temperature during the current welding process is 400 ℃, but the temperature of the welding site may be higher than 400 ℃ (or lower than 400 ℃) in practice due to the heat accumulation (or heat dissipation) of the welding station during the process, which may be caused by the previous welding process. In order to reduce the negative effects caused by temperature deviation, the trend of the temperature change needs to be predicted so as to facilitate the adjustment of the subsequent production process. By using the model to predict the trend of the temperature change, the relation between various factors and the temperature can be more accurately perceived, and the prediction accuracy of the trend of the temperature change is effectively improved.

The process standard refers to a standard index for ensuring the quality of a product in the production process of a semiconductor. In some embodiments, the process criteria may include the highest temperature, lowest temperature, and average temperature of the product produced during each process run, etc.

In some embodiments, the control device may also determine the thermal energy scheduling parameters 390 based on the current process recipe, process criteria, product temperature variation trend. The control device can determine whether the thermal energy storage device needs to absorb or release heat and whether the current product is before (needs to be preheated) or after (needs to be cooled) processing based on the current process. Based on the process criteria, the temperature at which the product is processed is determined. The highest temperature/lowest temperature/average temperature of the product over a period of time in the future is determined based on the product temperature variation profile.

The control device may determine the thermal energy scheduling parameter based on a third preset table. The third preset table comprises the corresponding relation between the process progress, the historical process standard and the historical temperature change trend of different historical products and different historical heat energy dispatching parameters. Wherein the third preset table may be constructed based on the actual working effect of the temperature adjusting device.

Illustratively, constructing the third preset table may be divided into the following steps:

step 1: and setting a power interval and a temperature interval included in a third preset table according to the power range and the temperature range which can be achieved by the temperature regulating equipment.

Step 2: and selecting a group of untested initial temperatures and powers from the power interval and the temperature interval, and performing multiple tests.

Step 3: and counting the highest temperature/lowest temperature/average temperature of the multiple test results in a future period, and selecting the mode of the highest temperature/lowest temperature/average temperature as the estimated highest temperature/estimated lowest temperature/estimated average temperature of the future time points corresponding to the initial temperature and the power of the group.

Step 4: and (2) repeating the step until the third preset table is constructed to be completed when the third preset table contains each group of initial temperature and power and the corresponding estimated highest temperature/estimated lowest temperature/estimated average temperature for a period of time in the future.

The control equipment can determine the heat energy scheduling parameters of the current product by inquiring the third preset table based on the technological process, the technological standard and the temperature change trend of the current product.

In some embodiments of the present disclosure, the thermal energy scheduling parameter is determined based on the current process, the process standard and the product temperature variation trend, and the thermal energy of the semiconductor product is regulated, so that it is ensured that the thermal energy of the semiconductor product can be accurately regulated, and the deviation between the actual temperature and the ideal temperature of the product is reduced.

FIG. 4 is an exemplary flow chart for determining purge parameters according to some embodiments of the present description.

In some embodiments, the control device may determine the operating space; determining a future period of hazardous material index profile 460 based on the operating space meshing result 412, the future period of dust index 450, the dust type 420, the air flow parameters 430, and the hazardous material index profile 440; the distribution of the harmful substance indexes in the future period of time comprises the harmful substance indexes of each grid of the operation space after meshing; the decontamination parameters 470 are determined based on the future period of harmful material index profile 460, the future period of dust index 450.

The operation space refers to a position space where an operation table for semiconductor production is located.

The gridding result 412 is a result obtained by gridding the operation space. In some embodiments, the gridding results may include a size of each grid (e.g., an area of each grid, etc.) after gridding the operation space based on the grid size and the grid shape. Wherein the mesh size and mesh shape may be preset based on a priori experience.

In some embodiments, the meshing result 412 of the operating space is also related to a first grid size 411, which is related to the dust concentration profile 410 of the operating space.

The dust concentration distribution 410 refers to the dust concentration distribution of a preset point in the gridded operation space. For example. Assuming that there are three preset points in the operation space, the corresponding dust concentrations are 12, 13, 14, respectively, the dust concentration distribution can be expressed as (12, 13, 14).

In some embodiments, the degree of dispersion of the dust concentration profile may be determined based on the dust concentration profile. The degree of dispersion of the dust concentration distribution can be expressed by the variance of the dust concentration distribution. For example. Continuing with the previous example, the dust concentration distribution in this operating space is discrete = (12-13) ² +(13-13) ² +(14-13) ² ＝2。

In some embodiments, the first grid size is inversely related to the dust concentration distribution dispersion degree. That is, the more uneven the dust concentration distribution, the larger the variance of the dust concentration distribution, and in order to represent the dust distribution, the smaller the first grid needs to be used. The specific comparison value of the first grid size and the dust concentration distribution discrete degree can be set based on historical experience, and a grid comparison table is constructed, so that the corresponding first grid size can be determined based on the dust concentration distribution discrete degree table lookup.

In some embodiments, the control device may divide the operation space according to the first mesh size to obtain the meshing result.

In some embodiments of the present disclosure, the size of the grid is determined based on the dust concentration distribution, and dynamic adjustment of grid division may be achieved, and when the dust distribution is more uniform, larger grids may be divided, saving computing power. When the dust distribution is more discrete, smaller grids can be divided to ensure the accuracy of the result.

The harmful substances refer to toxic and harmful substances generated in the production process of semiconductor products. In some embodiments, the hazardous materials may include hazardous chemical gases, hazardous chemical particles, etc. generated during the patch welding process.

In some embodiments, the decontamination parameters further include a decontamination parameter. The harmful substance removing parameter refers to a control parameter for controlling the movable purifying equipment to adsorb and remove harmful substances. In some embodiments, the decontamination parameters include at least the power of the movable decontamination apparatus to decontaminate the hazardous substance.

In some embodiments, the control device may determine the future period of hazardous material index profile 460 in a number of ways based on the operating space meshing result 412, the future period of dust index 450, the current dust type 420, the air flow parameters 430, and the hazardous material index profile 440. Wherein the future period of harmful substance index distribution comprises harmful substance indices in each grid of the operating space after meshing. For more on the air flow parameters and the harmful substances see fig. 1 and the description thereof.

For example, the harmful substance index distribution may be determined by a method of constructing a vector database. For example, a plurality of sample data may be obtained from historical data of the operation space, and corresponding sample vectors may be constructed. For example, the elements of the sample vector may include a gridding result of the operation space, a dust index, a dust type, an air flow parameter, a harmful substance index distribution at a first historical time point, and a harmful substance index distribution at a second historical time point. Wherein the first historical time point is earlier than the second historical time point. For example, if the meshing result of the operation space is a, the dust index is B, the dust type is C, the air flow parameter is D, the harmful substance index distribution at the first historical time point is E, and the harmful substance index distribution at the second historical time point is F, the corresponding sample vector is [ a, B, C, D, E, F ].

The control device can cluster the sample vectors in the sample data through a clustering algorithm to obtain a plurality of cluster centers. And determining the sample vector corresponding to the cluster center as a standard vector, and storing the standard vector into a vector database. The types of clustering algorithms may include a variety of, for example, the clustering algorithms may include K-Means clustering, density-based clustering methods (DBSCAN), and the like.

When the harmful substance index distribution in the future time period needs to be determined, constructing a vector to be matched according to the meshing result, the dust index in the future time period, the dust type, the air flow parameters, the harmful substance index distribution and other parameters, calculating the similarity between the vector to be matched and a standard vector in a vector database, and taking the harmful substance index distribution at a second historical time point corresponding to the standard vector with the highest similarity as the harmful substance index distribution in the future time period.

In some embodiments, the control device may determine the harmful substance index distribution by a harmful substance index distribution prediction model based on the dust index, the dust type, the air flow parameter, and the harmful substance index distribution for a future period of time. For more on the pest index distribution prediction model, see fig. 5 and its related description.

In some embodiments, determining the purging parameter further includes, in response to a preset threshold condition being met: the decontamination parameters are determined based on the future period of harmful material index profile and the future period of dust index.

The preset threshold condition is a critical condition for judging whether the gas in the operation space will cause damage to the human body. In some embodiments, the preset threshold condition may include a grid having a grid harmful substance index greater than a harmful threshold in a harmful substance index distribution for a future period of time, and/or a dust index greater than a dust threshold for a future period of time having a grid. When the preset threshold is met, the control device may determine the evolution parameter. In some embodiments, the pest threshold, dust threshold in the preset threshold condition may be preset based on a priori experience.

In some embodiments, when the preset threshold condition is met, the grid position where the movable cleaning device needs to be placed can be determined, the cleaning power is determined by querying a cleaning power preset table according to the harmful substance index and/or dust index in the grid, and the cleaning power is determined as a required cleaning parameter.

The grid location at which the mobile decontamination device is placed may be determined based on a variety of ways. For example, the grid position where the harmful substance index and/or dust index is highest may be determined as the grid position where the movable purification apparatus is placed.

In some embodiments, the control device may further establish a fourth preset table based on the historical harmful substance index distribution, the historical dust index, and the historical grid location. The fourth preset table comprises the corresponding relation between the historical harmful substance index distribution, the historical dust index, the historical grid position and different historical purification parameters. The control device may determine the current purification power by referring to a fourth preset table according to the current harmful substance index distribution, the dust index, and the corresponding grid position where the movable purification device is placed. The cleaning power may include, among other things, harmful substance removal power and/or dust removal power.

In some embodiments of the present description, the determination of whether the harmful material index distribution dust index exceeds the threshold value at a future point in time is divided into two subsequent cases: determining a purging parameter when the threshold is exceeded; and when the threshold is not exceeded, the purge parameter may not be set. Therefore, the cost loss caused by long-time air purification can be avoided when the gas safety of the operation space is ensured.

In some embodiments, the pest-killing parameter may be determined based on a fifth preset table. The control device may construct a fifth preset table based on the harmful substance removal performance of the movable purification device. The fifth preset table comprises the corresponding relation between the index difference value of the harmful substance removal and different harmful removal parameters.

In some embodiments, the control device may determine a value interval of the index difference value of the harmful substance removal index based on the actual situation, select a set of values from the value interval, perform the harmful substance removal experiment using the movable purifying device, obtain the minimum harmful substance removal power that can reduce the difference value to zero in the specified time, and store the minimum harmful substance removal power in the fifth preset table. And continuously repeating the numerical selection until all the numerical values in the numerical value interval have the corresponding minimum harmful substance removal power. At this time, the fifth preset table construction is completed. Wherein the target harmful substance index and the prescribed time may be determined based on a priori experience.

When the control device needs to determine the pest removal parameter based on the current pest removal index difference value, a fifth preset table can be queried, and the minimum pest removal power corresponding to the current difference value is selected and determined as the required pest removal parameter.

In some embodiments, the control device may determine the obtained dust removal parameter and the harmful substance removal parameter as the purification index, perform the corresponding parameters in the order of removing the harmful substance first and then removing the dust, or perform the corresponding parameters in the order of removing the harmful substance first and then removing the harmful substance.

In some embodiments of the present disclosure, by constructing a preset table, determining the minimum harmful substance removal power corresponding to different harmful substance removal index differences, and determining the minimum harmful substance removal power as a required harmful substance removal parameter, it is possible to ensure that the content of harmful substances in air can be reduced to a target harmful substance index within a specified time, and also to avoid noise problems and energy waste caused by excessive power of the movable purification apparatus; the purification parameters are determined in the meshed operation space based on the harmful substance index distribution at the future time point and the dust index at the future time point, and the purification operation is performed, so that the dust in the air and the harmful gas are prevented from being accumulated, and the dust and the harmful gas are timely removed, so that the physical health of staff can be fully ensured.

In some embodiments, the control device constructs an operating space map 510 based on the operating space meshing result 412, the dust index 450 for a future period of time, the dust type 420, the air flow parameters 430, and the hazardous material index profile 440.

The operation space map 510 refers to a knowledge map representing the energy and range of the movable purification apparatus for air purification. The operating space map may reflect the characteristics of the distribution of the mobile purification devices in the operating space. The operational space graph may be composed of at least one node 511 and at least one edge 512.

In some embodiments, nodes 511 comprise grid nodes, one corresponding to each grid region. The node attribute of the grid node can be the harmful substance index of the grid, the dust index of a future period of time, whether the grid node is in the action range of the air purifying device (in the case of 1 and not in the case of 0), the purifying power of the air purifying device (in the case of corresponding power, in the case of 0), the space linear distance of the grid from an operator and the space linear distance of the grid from an operating point.

In some embodiments, when two nodes correspond to adjacent grid areas, the nodes are connected by an edge 512, and the attributes of the edge include air flow parameters such as air flow rate, air flow direction, and the like.

In some embodiments, the control device may determine the pest index distribution 460 for a future period of time based on the operational spatial map 510 by the pest index distribution prediction model 520.

The pest index distribution prediction model 520 refers to a prediction model for evaluating pest index distribution at a future point of time of the operation space. In some embodiments, the pest index distribution prediction model may be a machine learning model. The pest index distribution prediction model may be, for example, a graph neural network (Graph Neural Networks, GNN) model.

In some embodiments, the input to the pest index distribution prediction model may be an operational space diagram and the output may be the pest index distribution of each node over a future period of time.

In some embodiments, the third training sample for training the pest index distribution prediction model may be a sample operation space map constructed based on the historical data of the first historical time point, and the corresponding third label may actually detect the pest concentration of each grid area at the first historical time point. The control device may train the pest index distribution prediction model based on the third training samples and the corresponding third labels.

The specific training process of the pest index distribution prediction model is similar to that of the temperature prediction model, and reference can be made to the specific content related to fig. 3.

In some embodiments of the present description, by using a model to predict pest index distribution at a future point in time, the data processing capacity and mining capacity of the model can be fully utilized to obtain more accurate pest index distribution prediction results.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. An environmental conditioning system for a semiconductor processing apparatus, comprising: the device comprises a temperature and humidity monitoring device, interaction equipment, dust index monitoring equipment, temperature adjusting equipment, movable purifying equipment and control equipment;

the temperature and humidity monitoring device is configured to monitor and acquire a temperature and humidity index of a workshop; the dust index monitoring device is configured to monitor and acquire a dust index of a workshop; the temperature regulating device is configured to regulate the temperature of the processing device; the mobile decontamination apparatus is configured to remove dust from the plant; the interactive device is configured to display the current process, the temperature and humidity index and the dust index, and acquire user feedback;

the control device is configured to:

Acquiring the temperature and humidity index and the dust index based on the temperature and humidity monitoring device and the dust index monitoring equipment;

determining a temperature adjustment parameter based on the current process and the temperature and humidity index;

generating a temperature adjustment instruction based on the temperature adjustment parameter, and sending the temperature adjustment instruction to the temperature adjustment device;

determining a purge parameter based on the current process recipe and the dust index;

generating a dust removal instruction based on the purification parameters, and sending the dust removal instruction to the movable purification equipment; the method comprises the steps of,

responding to at least one of the temperature and humidity index and the dust index not meeting a preset index condition, and sending an early warning to a user through the interaction equipment;

determining an initialization value of a device parameter in response to obtaining restart feedback from the interaction device;

and generating a temperature regulation instruction and a purification instruction based on the initialization value, and respectively sending the temperature regulation instruction and the purification instruction to the temperature regulation equipment and the movable purification equipment.

2. The system of claim 1, wherein the temperature conditioning apparatus further comprises a thermal energy storage device;

The thermal energy storage device is configured to release thermal energy outwardly and to collect and store desired thermal energy from the product to be processed and/or the finished processed product;

the temperature adjustment parameters further include thermal energy scheduling parameters of the thermal energy storage device;

the control device is further configured to:

and determining the heat energy scheduling parameters based on the current process progress, the process standard and the temperature and humidity of the current workshop.

3. The system of claim 2, wherein the control device is further configured to:

determining the temperature change trend of a product based on the current process, the subsequent process, the working parameters of semiconductor processing equipment, the current temperature adjustment parameters and the temperature and humidity of the current workshop;

and determining the heat energy scheduling parameters based on the current process progress, the process standard and the product temperature change trend.

4. The system of claim 1, wherein the control device is further configured to:

predicting the dust index for a future period of time through a dust index prediction model based on the current process, the subsequent process and the dust index; the dust index prediction model is a machine learning model;

Determining a purging parameter based on the dust index for the future period of time, the purging parameter comprising a dust removal parameter.

5. The system of claim 4, further comprising a distributed harmful substance monitoring device, a gas flow detector;

the distributed harmful substance monitoring device is configured to detect an exponential distribution of harmful substances in air; the gas flow direction detector is configured to detect an air flow parameter; the purification parameters also comprise harmful parameters;

the control device is further configured to:

determining an operation space;

determining the hazardous material index profile for the future period of time based on the meshing result of the operating space, the dust index, the dust type, the air flow parameters, and the hazardous material index profile for the future period of time; the future period of harmful matter index distribution includes the harmful matter index of each grid of the operating space after meshing;

the purification parameters are determined based on the harmful material index distribution for the future period of time, the dust index for the future period of time.

6. A method of environmental conditioning of a semiconductor processing apparatus, the method performed by a control apparatus of a semiconductor processing apparatus environmental conditioning system, the method comprising:

Acquiring a temperature and humidity index and a dust index based on the temperature and humidity monitoring device and the dust index monitoring equipment;

generating a temperature regulation instruction based on the temperature regulation parameter, and sending the temperature regulation instruction to temperature regulation equipment;

determining a purge parameter based on the current process recipe and the dust index; the purification parameters at least comprise dust removal power;

generating a dust removal instruction based on the purification parameters, and sending the dust removal instruction to movable purification equipment; the method comprises the steps of,

responding to at least one of the temperature and humidity index and the dust index not meeting a preset index condition, and sending an early warning to a user through interaction equipment;

determining an initialization value of a device parameter in response to obtaining restart feedback from the interaction device; the equipment parameters comprise a temperature regulation parameter and the purification parameter; and

7. The method of claim 6, wherein the temperature regulation apparatus further comprises a thermal energy storage device;

the determining the temperature adjustment parameter based on the current process and the temperature and humidity index comprises:

8. The method of claim 7, wherein determining the thermal energy scheduling parameter based on the current process recipe and process criteria, a current shop floor temperature and humidity comprises:

determining the temperature change trend of a product based on the current process, the subsequent process, the working parameters of semiconductor processing equipment, the current temperature adjustment parameters and the temperature and humidity of the current workshop; the working parameters of the semiconductor processing equipment comprise the working time and the working power of the semiconductor processing equipment;

9. The method of claim 6, wherein said determining a purge parameter based on said current process recipe and said dust index comprises:

Predicting the dust index for a future period of time through a dust index prediction model based on the current process, the subsequent process and the dust index;

10. The method of claim 9, wherein the determining a cleaning parameter based on the dust index for the future period of time comprises:

determining an operation space;