Scheduling methods, apparatus, equipment and storage media for thin film deposition equipment

Abstract

This invention relates to the field of semiconductor technology, and more particularly to a scheduling method, apparatus, device, and storage medium for thin film deposition equipment. The method includes: obtaining cleaning time information for each process module based on equipment information and assumed virtual conditions; obtaining idle time for each process module based on processing information and cleaning time information; constructing a ROPN model based on equipment information and processing information, and optimizing the ROPN model based on the idle time of each process module; and guiding the scheduling of the thin film deposition equipment based on the optimized ROPN model. The method disclosed in this application optimizes the ROPN model by calculating the idle time of each process module, thereby achieving staggered scheduling of cleaning times for each process module, effectively avoiding the uneven load on the vacuum end robot, and significantly improving the production efficiency of the thin film deposition equipment. Furthermore, the periodic scheduling strategy based on the ROPN model can ensure that the cleaning times of each process module are continuously staggered.

Description

Technical Field

[0001] This invention relates to the field of semiconductor technology, and in particular to a scheduling method, apparatus, device, and storage medium for a thin film deposition equipment. Background Technology

[0002] Semiconductor manufacturing is the core foundation of consumer electronics and high-end technology. Among them, wafer manufacturing is the most automated and complex process, encompassing key processes such as thin film deposition, coating, exposure, and etching. Thin film deposition, which uses technologies such as physical vapor deposition, chemical vapor deposition, and atomic layer deposition to deposit thin films such as silicon dioxide on the wafer surface, is one of the core steps in wafer manufacturing. The capacity utilization rate of thin film deposition equipment directly restricts the output of semiconductor chips, so its efficient scheduling has become a key issue for the industry.

[0003] Thin film deposition equipment mainly consists of an atmospheric end, a vacuum lock, and a vacuum end. The process module located at the vacuum end may have residual particles or chemicals after processing wafers. Therefore, after processing n wafers, the process module needs to be cleaned and maintained to ensure wafer quality.

[0004] Current thin-film deposition equipment scheduling mainly uses mathematical programming methods to construct mixed-integer programming models to generate action sequences, but it has the following significant problems:

[0005] 1. The model size grows exponentially with the increase of discrete variables, making it difficult to solve the scheduling scheme within an effective time in actual production, thus delaying the production process;

[0006] 2. In order to shorten the solution time, it is often necessary to reduce the number of wafers, relax constraints, or increase assumptions. This approach can easily lead to a non-optimal solution or even an infeasible solution.

[0007] 3. The centralized cleaning strategy causes the vacuum end robot to bear an excessive load during the non-cleaning phase of the process module, while it is idle during the cleaning phase, which leads to an imbalance in equipment load and consequently reduces the equipment's capacity utilization rate.

[0008] It is evident that existing technologies still need improvement and enhancement. Summary of the Invention

[0009] To overcome the shortcomings of existing technologies, the present invention aims to provide a scheduling method for thin film deposition equipment. Based on the ROPN model optimized by idle time, it realizes the continuous staggered scheduling of cleaning time of process modules, improves the stability and reliability of equipment operation, and avoids the phenomenon of unbalanced load on the robot arm.

[0010] The first aspect of this invention provides a scheduling method for a thin film deposition equipment, comprising: acquiring equipment information; acquiring cleaning time information of each process module based on the equipment information and preset hypothetical virtual conditions through theoretical derivation; acquiring processing information; acquiring idle time of each process module based on the processing information and the cleaning time information of each process module through theoretical derivation; constructing a ROPN model based on the equipment information and processing information, and optimizing the ROPN model based on the idle time of each process module to obtain an optimized ROPN model; and guiding the scheduling of the thin film deposition equipment based on the optimized ROPN model.

[0011] Optionally, in a first implementation of the first aspect of the present invention, the step of obtaining equipment information, based on the equipment information and preset hypothetical virtual conditions, and obtaining the cleaning time information of each process module through theoretical derivation, includes: obtaining equipment information, the equipment information including the cleaning cycle of the process module; obtaining preset hypothetical virtual conditions, the preset hypothetical virtual conditions assuming that all process modules enter the processing cycle synchronously; and simulating the processing and cleaning process of each process module through theoretical derivation based on the cleaning cycle of the process module and the preset hypothetical virtual conditions to obtain the cleaning time information of each process module, the cleaning time information including the first cleaning start time, end time, and cleaning duration.

[0012] Optionally, in a second implementation of the first aspect of the present invention, the step of simulating the processing and cleaning flow of each process module based on the cleaning cycle of the process module and preset hypothetical virtual conditions to obtain the cleaning time information of each process module includes: constructing a simulation mathematical model based on the cleaning cycle of the process module and preset hypothetical virtual conditions; and running the constructed simulation mathematical model to obtain the cleaning time information of each process module.

[0013] Optionally, in a third implementation of the first aspect of the present invention, the step of obtaining processing information, and obtaining the idle time of each process module through theoretical derivation based on the processing information and the cleaning time information of each process module, includes: obtaining processing information, the processing information including the processing cycle of the process module; selecting a process module as a reference module, and obtaining the idle time of the reference module based on the first cleaning start time and cleaning duration of the reference module; confirming the interval principle based on the processing cycle, and obtaining the idle time of other process modules through reverse calculation based on the interval principle.

[0014] Optionally, in a fourth implementation of the first aspect of the present invention, the step of selecting a process module as a reference module and obtaining the idle time of the reference module based on the first cleaning start time and cleaning duration of the reference module includes: selecting a process module as a reference module and obtaining the first cleaning start time and cleaning duration corresponding to the reference module; calculating the actual start time corresponding to the reference module based on the first cleaning start time and cleaning duration; and deriving the idle time of the reference module using algebraic operations based on the actual start time, combined with the processing cycle and cleaning duration.

[0015] Optionally, in the fifth implementation of the first aspect of the present invention, the step of determining the interval based on the processing cycle and obtaining the idle time of other process modules by theoretical derivation based on the interval principle includes: setting at least one processing cycle between the first cleaning end time of each process module and the first cleaning end time of the adjacent process module as the interval principle; and obtaining the idle time of other process modules by numerical sequence patterns based on the interval principle.

[0016] Optionally, in the sixth implementation of the first aspect of the present invention, the step of constructing a ROPN model based on equipment information and processing information, and optimizing the ROPN model based on the idle time of each process module to obtain an optimized ROPN model, includes: constructing a ROPN model based on equipment information and processing information, wherein the equipment information further includes equipment composition and vacuum end robot load conditions, and the processing information further includes process path and wafer flow quantity; using the idle time of each process module as the initial state of the ROPN model to optimize the ROPN model to obtain an optimized ROPN model.

[0017] A second aspect of the present invention provides a scheduling device for a thin film deposition equipment, comprising: a first derivation module for acquiring equipment information and, based on the equipment information and preset hypothetical virtual conditions, acquiring cleaning time information of each process module through theoretical derivation; a second derivation module for acquiring processing information and, based on the processing information and the cleaning time information of each process module, acquiring idle time of each process module through theoretical derivation; an optimization module for constructing a ROPN model based on the equipment information and processing information, and optimizing the ROPN model based on the idle time of each process module to obtain an optimized ROPN model; and a guidance module for guiding the scheduling of the thin film deposition equipment based on the optimized ROPN model.

[0018] A third aspect of the present invention provides a scheduling device for a thin film deposition apparatus, the scheduling device comprising: a memory and at least one processor, the memory storing instructions; the at least one processor calling the instructions in the memory to cause the scheduling device for the thin film deposition apparatus to execute the various steps of the scheduling method for the thin film deposition apparatus described in any of the preceding claims.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the steps of the scheduling method for the thin film deposition apparatus described in any of the preceding claims.

[0020] In the technical solution of this invention, by calculating the idle time of each process module to optimize the ROPN model, the staggered arrangement of cleaning time of process modules is realized, which effectively avoids the uneven load of the vacuum end robot, which not only helps to ensure the quality of wafer products, but also significantly improves the production efficiency of thin film deposition equipment. The periodic scheduling strategy based on the ROPN model ensures the continuous staggered cleaning time of each process module, thereby improving the stability and reliability of thin film deposition equipment operation. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the structure of a thin film deposition apparatus provided in an embodiment of the present invention;

[0022] Figure 2 A logic flowchart of a scheduling method for a thin film deposition apparatus provided in an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of the scheduling device for a thin film deposition apparatus provided in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the scheduling device of the thin film deposition apparatus provided in an embodiment of the present invention. Detailed Implementation

[0025] This invention provides a scheduling method, apparatus, device, and storage medium for a thin film deposition equipment. In this invention, the terms "first," "second," "third," "fourth," etc. (if present)," in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] For ease of understanding, the thin film deposition equipment applicable to the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1Thin film deposition equipment mainly consists of three regions: the atmospheric end region, the vacuum lock region, and the vacuum end region. In the atmospheric end region, the LoadPort (wafer loading / unloading machine) serves as the interface between the thin film deposition equipment and the outside world, used to place wafers to be processed or already processed. Each LoadPort has a capacity of 25. The CoolingBuffer is the wafer cooling module; processed wafers must be cooled here before being placed into the LoadPort. The ATR (Atmospheric End Robot) is responsible for transporting wafers between the LoadPort, CoolingBuffer, and LoadLock. The Aligner is the wafer calibration module; wafers taken from the LoadPort must be calibrated here before entering the vacuum lock area. The vacuum lock area (LoadLock) connects the atmospheric end and the vacuum end. It uses a pump (vacuuming) operation to transfer unprocessed wafers from the atmospheric end to the vacuum end, and a vent (atmospheric filling) operation to transfer processed wafers from the vacuum end to the atmospheric end. The vacuum end region includes the PM (Process Module), which is used for thin film deposition processing of wafers. After processing a certain number of wafers, the process module needs to perform a cleaning (wac) operation. The VTR (Vacuum End Robot) transports wafers between the PMs and between the PMs and the LoadLock.

[0027] The semiconductor assembly equipment disclosed in this embodiment has the following processing flow: The atmospheric end robot is responsible for transporting the wafer to be processed from the storage unit to the calibration station for calibration, and then transporting it to the vacuum lock; After receiving the wafer in the atmosphere, the vacuum lock performs a vacuuming operation. After the vacuuming is completed, the vacuum end robot takes out the unprocessed wafer from the vacuum lock and puts it into the process module for processing; If the wafer path recipe specifies that the processing chamber needs to be cleaned before processing, the wafer must be cleaned first before the wafer can enter the processing chamber of the process module; After the wafer is processed according to the recipe path, the vacuum end robot takes it out of the processing chamber and puts it back into the vacuum lock; After receiving the wafer, the vacuum lock performs an atmospheric filling operation; Finally, the atmospheric end robot puts the processed wafer into the cooling station for cooling, and finally puts it into the wafer loading and unloading machine. If there are unprocessed wafers for this task in the wafer loading and unloading machine, the wafer must first be put into the storage unit for buffering, and then transferred to the wafer loading and unloading machine when there are no unprocessed wafers in the wafer loading and unloading machine.

[0028] Furthermore, for ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 2 One embodiment of the scheduling method for the thin film deposition equipment in this invention includes:

[0029] 101. Obtain equipment information. Based on the equipment information and preset hypothetical virtual conditions, obtain the cleaning time information of each process module through theoretical derivation.

[0030] In this embodiment, by introducing preset hypothetical virtual conditions, the computational complexity of cleaning time information can be simplified, making it easier to quickly establish theoretical benchmarks for the cleaning time of each process module, and providing basic data support for subsequent peak-shifting scheduling.

[0031] 102. Obtain processing information. Based on the processing information and the cleaning time information of each process module, obtain the idle time of each process module through theoretical derivation.

[0032] 103. Construct a ROPN model based on equipment information and processing information, and optimize the ROPN model based on the idle time of each process module to obtain an optimized ROPN model;

[0033] 104. Scheduling of thin film deposition equipment based on optimized ROPN model;

[0034] In this embodiment, a scheduling sequence is generated based on an optimized ROPN model to guide the scheduling process of the thin film deposition equipment. The ROPN model is a graphical mathematical model consisting of places (representing system states), transitions (representing system changes or events), and tokens (which flow between places and transitions). The ROPN model has the advantages of small size and strong versatility, making it suitable for modeling in industrial fields. Using the ROPN model to generate a scheduling sequence to guide the thin film deposition equipment requires very low computing resources and can complete the scheduling scheme within 1 second, effectively reducing equipment energy consumption.

[0035] The scheduling method for thin film deposition equipment disclosed in this application optimizes the ROPN model by calculating the idle time of each process module, thereby achieving staggered scheduling of cleaning times for process modules. This effectively avoids the uneven load on the vacuum end robot, which not only helps ensure the quality of wafer products but also significantly improves the production efficiency of the thin film deposition equipment. The periodic scheduling strategy based on the ROPN model ensures continuous staggered cleaning times for each process module, thereby improving the stability and reliability of the thin film deposition equipment operation.

[0036] In this embodiment, the acquisition of equipment information, based on the equipment information and preset hypothetical conditions, involves obtaining the cleaning time information of each process module through theoretical derivation, including:

[0037] 201. Obtain equipment information, including the cleaning cycle of the process module;

[0038] In this embodiment, the cleaning cycle is a threshold number of wafers that the process module needs to clean, such as cleaning the process module once every n wafers processed. The cleaning cycle can be preset by the designer according to the actual processing situation and equipment parameters to ensure that the cleaning operation matches the actual processing load and avoids wasting equipment utilization due to cleaning too early or causing wafer contamination risk due to cleaning too late.

[0039] 202. Obtain a preset hypothetical virtual condition, wherein the preset hypothetical virtual condition assumes that all process modules enter the processing cycle synchronously;

[0040] In this embodiment, by assuming that all process modules enter the processing cycle synchronously, the complex scenario of multiple modules starting asynchronously can be simplified into a synchronous model with a unified starting point, eliminating the interference of initial time difference, facilitating the subsequent unified derivation of cleaning time information through mathematical methods, reducing problem complexity, and improving computational efficiency.

[0041] 203. Based on the cleaning cycle of the process module and the preset hypothetical virtual conditions, the processing and cleaning process of each process module is simulated through theoretical derivation to obtain the cleaning time information of each process module. The cleaning time information includes the first cleaning start time, end time and cleaning duration.

[0042] In this implementation, by simulating the actual cleaning process through theoretical derivation, the precise time point for the first cleaning of each process module can be systematically calculated, providing a quantifiable initial benchmark for subsequent off-peak scheduling, ensuring that cleaning operations are carried out as planned, and avoiding equipment conflicts or quality hazards caused by time chaos.

[0043] In this embodiment, based on the cleaning cycle of the process module and preset hypothetical virtual conditions, the processing and cleaning flow of each process module is simulated through theoretical derivation to obtain the cleaning time information of each process module, including:

[0044] 301. Construct a simulation mathematical model based on the cleaning cycle of the process module and preset hypothetical virtual conditions;

[0045] In this embodiment, the mathematical model abstracts the physical problem into a computable expression, that is, it converts the cleaning cycle of the process module and the preset hypothetical virtual conditions into mathematical formulas, ensuring the rigor and reproducibility of the theoretical derivation.

[0046] 302. Run the constructed simulation mathematical model to obtain the cleaning time information for each process module;

[0047] In this embodiment, by running the constructed simulation mathematical model, the cleaning time information of each process module is automatically calculated, which not only avoids human error, but also quickly generates accurate cleaning time information.

[0048] In this embodiment, the acquisition of processing information, based on the processing information and the cleaning time information of each process module, involves obtaining the idle time of each process module through theoretical derivation, including:

[0049] 401. Obtain processing information, wherein the processing information includes the processing cycle of the process module;

[0050] In this embodiment, the processing cycle is the processing time of a single wafer, which is a key indicator for measuring equipment capacity and the basis for calculating idle time. By combining the processing cycle and cleaning time information, the idle time of the module outside of processing and cleaning can be accurately assessed, providing data support for load balancing.

[0051] 402. Select a process module as the baseline module, and obtain the idle time of the baseline module based on the first cleaning start time and cleaning duration of the baseline module;

[0052] In this embodiment, a benchmark module is selected as the time reference point. This benchmark module is the first module to start processing in the actual scheduling process. The idle time is derived from its cleaning time to establish a unified benchmark for time coordination of multiple modules. This avoids time confusion caused by multiple starting point calculations and ensures that the time parameters of each process module are comparable.

[0053] 403. Based on the processing cycle confirmation interval principle, and based on the interval principle, obtain the idle time of other process modules through reverse calculation;

[0054] In this embodiment, reverse calculation is performed to deduce the initial idle time from the target state, i.e., off-peak cleaning, to ensure that the cleaning time of all process modules is distributed according to the preset interval. Compared with forward calculation, reverse derivation is more efficient and targeted, can quickly meet the load balancing requirements, and reduce computational redundancy.

[0055] In this embodiment, selecting a process module as a baseline module and obtaining the idle time of the baseline module based on its first cleaning start time and cleaning duration includes:

[0056] 501. Select a process module as the baseline module, and obtain the first cleaning start time and cleaning duration corresponding to the baseline module;

[0057] 502. Calculate the actual start time corresponding to the baseline module based on the initial cleaning start time and cleaning duration;

[0058] In this embodiment, the time difference between the actual start time and the first cleaning start time is set as the cleaning duration. The actual start time reflects the starting point of the actual processing of the process module. By separating the cleaning time, the time distribution of the processing stage can be analyzed separately, providing a clear boundary for the calculation of idle time.

[0059] 503. Based on the actual start time, combined with the processing cycle and cleaning time, derive the idle time of the benchmark module using algebraic operations;

[0060] In this embodiment, the idle time from the start of the module to the actual processing is quantified by algebraic formulas. Algebraic operations are deterministic and verifiable, ensuring that the idle time calculation result is unique and accurate, providing reliable input for subsequent model optimization.

[0061] In this embodiment, the step of determining the interval based on the processing cycle and obtaining the idle time of other process modules through theoretical derivation based on the interval principle includes:

[0062] 601. Set the interval between the first cleaning end time of each process module and the first cleaning end time of the adjacent process module to be at least one processing cycle, as the interval principle;

[0063] In this embodiment, the interval principle physically prevents multiple process modules from finishing cleaning at the same time, and prevents the vacuum end robot from handling the restart tasks of multiple process modules in a short period of time, thereby balancing the load of the vacuum end robot and avoiding equipment jamming or failure caused by instantaneous overload of the vacuum end robot.

[0064] 602. Based on the interval principle, obtain the idle time of other process modules through the sequence pattern;

[0065] In this embodiment, the idle time of each process module can be quickly derived using an arithmetic sequence or an equally spaced sequence model. The sequence pattern simplifies the complex time coordination process, making the multi-module staggered scheduling mathematically regular, which is convenient for program implementation and dynamic adjustment.

[0066] To further illustrate the reverse calculation process of idle time for each process module, a thin film deposition equipment with a cleaning cycle of n=3, a processing cycle of T, and a cleaning duration of WacTime is used as an example. In the virtual phase, i.e., during the execution of step 203, it is assumed that all process modules start synchronously from time 0 without any idle time. At this time, the initial cleaning end time for PM3 and PM2 is 3T+WacTime, and for PM1 it is 6T+WacTime, resulting in concentrated cleaning causing the robotic arm to be idle. To solve this problem, the actual start time is set to the virtual start time + WacTime. Taking PM3 as the baseline, its idle time is... 0 indicates that the system can be started directly, meaning it enters the processing cycle immediately. The idle time of other process modules is determined by reverse derivation: PM2 needs to wait for time T after the actual start time to start, so that its first processing start time is T later than PM3, and the first cleaning end time is 4T+2WacTime, with an interval of T between PM2 and PM3; PM1 needs to wait for time 2T to start, and the first cleaning end time is 8T+2WacTime. Although the initial cleaning intervals are T and 4T, based on the periodicity of the ROPN model scheduling, after the system enters a steady state, the cleaning interval of each PM will be fixed at T, avoiding concentrated cleaning, balancing the load of the vacuum end robot, and improving equipment efficiency.

[0067] In this embodiment, the construction of the ROPN model based on equipment information and processing information, and the optimization of the ROPN model based on the idle time of each process module to obtain the optimized ROPN model, includes:

[0068] 701. Construct a ROPN model based on equipment information and processing information, wherein the equipment information includes equipment composition and vacuum end robot load conditions, and the processing information includes process path and wafer flow quantity;

[0069] In this embodiment, parameters such as equipment composition, robot load conditions, and process path are incorporated to construct the ROPN model, so that the constructed ROPN model truly reflects the physical constraints and production logic of the thin film deposition equipment, avoiding the infeasibility of the scheduling scheme due to ignoring constraints.

[0070] In this embodiment, taking wafer flow as m1=(3), cleaning cycle as n=3, and robotic arm pick-up and drop-off as swap strategy as an example, a resource optimization-based Petri net (ROPN) model is constructed to manage its cleaning cycle; in the constructed ROPN model, taking three parallel production modules (PM1, PM2, PM3) as an example, the warehouse P3 uses three tokens to represent the availability of the three PMs, and each token represents an idle PM; transition t1 represents the wafer being taken out of the LoadLock, and warehouse P1 represents the VTR moving a wafer from the vacuum lock to the PM; transition t2 represents placing the wafer into the PM for processing, which will consume one token in P3, meaning that one PM The process module is occupied; transition t3 indicates that a wafer is removed from the PM, and the P3 token is not restored temporarily because each PM module needs to perform a cleaning operation after processing three wafers; transition t4 indicates that the VTR places the wafer in the vacuum lock, and place P4 indicates that the VTR moves a wafer from the PM to the vacuum lock; place P5 indicates the resource availability of the VTR, and place P6 indicates that the PM performs a cleaning operation; when a PM has processed three wafers, transition t6 is triggered, indicating that a cleaning operation is required. At this time, the process module is unavailable, and the token in place P3 is not restored temporarily; in this way, the ROPN model ensures that each PM performs cleaning after processing three wafers, thereby maintaining production efficiency and product quality.

[0071] 702. The idle time of each process module is used as the initial state of the ROPN model to optimize the ROPN model and obtain the optimized ROPN model.

[0072] In this embodiment, the ROPN model is optimized by adjusting its initial state, specifically by adjusting the token distribution corresponding to the space-time of PM. Using the theoretically derived idle time as the initial state of the ROPN model can directly guide the ROPN model to generate a scheduling scheme that meets the peak-shaving requirements, reducing the time cost of the model searching for feasible solutions. At the same time, the rationality of the initial state ensures that the optimized ROPN model outputs a periodic and stable solution, achieving continuous peak-shaving of cleaning time.

[0073] The scheduling method of the thin film deposition equipment in the embodiments of the present invention has been described above. The scheduling device of the thin film deposition equipment in the embodiments of the present invention will be described below. Please refer to [link / reference]. Figure 3 One embodiment of the scheduling device for the thin film deposition equipment in this invention includes:

[0074] The first derivation module 801 is used to obtain equipment information and, based on the equipment information and preset hypothetical virtual conditions, obtain the cleaning time information of each process module through theoretical derivation.

[0075] The second derivation module 802 is used to obtain processing information. Based on the processing information and the cleaning time information of each process module, the idle time of each process module is obtained through theoretical derivation.

[0076] The optimization module 803 is used to construct a ROPN model based on equipment information and processing information, and to optimize the ROPN model based on the idle time of each process module to obtain an optimized ROPN model.

[0077] The guidance module 804 is used to guide the scheduling of thin film deposition equipment based on the optimized ROPN model.

[0078] Based on the same ideas as the methods in the above embodiments, the apparatus provided in this application can implement the methods in the above embodiments.

[0079] above Figure 3 The scheduling device of the thin film deposition equipment in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The scheduling device of the thin film deposition equipment in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0080] Figure 4This is a schematic diagram of the structure of a scheduling device for a thin film deposition apparatus according to an embodiment of the present invention. The scheduling device 900 can vary considerably depending on its configuration or performance, and may include one or more central processing units (CPUs) 910 (e.g., one or more processors) and a memory 920, and one or more storage media 930 (e.g., one or more mass storage devices) storing application programs 933 or data 932. The memory 920 and storage media 930 can be temporary or persistent storage. The program stored in the storage media 930 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the scheduling device 900 of the thin film deposition apparatus. Furthermore, the processor 910 may be configured to communicate with the storage media 930 and execute the series of instruction operations in the storage media 930 on the scheduling device 900 of the thin film deposition apparatus to implement the steps of the scheduling method for the thin film deposition apparatus provided in the above-described method embodiments.

[0081] The scheduling device 900 of the thin film deposition equipment may also include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating systems 931, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 4 The scheduling device structure of the thin film deposition apparatus shown does not constitute a limitation on the scheduling device of the thin film deposition apparatus. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0082] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of a scheduling method for a thin film deposition apparatus.

[0083] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0084] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0085] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A scheduling method for a thin film deposition apparatus, characterized in that, include: The equipment information is obtained, and based on the equipment information and preset hypothetical virtual conditions, the cleaning time information of each process module is obtained through theoretical derivation. The cleaning time information includes the first cleaning start time, end time and cleaning duration. The process involves acquiring processing information, and based on this information and the cleaning time information of each process module, obtaining the idle time of each process module through theoretical derivation. Specifically, the process information includes the processing cycle of each process module. A process module is selected as a baseline module, and the first cleaning start time and cleaning duration corresponding to the baseline module are obtained. The selection of the baseline module serves as a time reference point, establishing a unified benchmark for time coordination among multiple modules. The actual start time corresponding to the baseline module is calculated based on the first cleaning start time and cleaning duration. Based on the actual start time, combined with the processing cycle and cleaning time, the idle time of the benchmark module is derived using algebraic operations. Set the interval between the first cleaning end time of each process module and the first cleaning end time of the adjacent process module to be at least one processing cycle, as the interval principle; Based on the interval principle, the idle time of other process modules is obtained through the sequence pattern; A ROPN model is constructed based on equipment information and processing information, and the ROPN model is optimized based on the idle time of each process module to obtain an optimized ROPN model. The scheduling of thin film deposition equipment is guided by an optimized ROPN model.

2. The scheduling method for a thin film deposition apparatus according to claim 1, characterized in that, The acquisition of equipment information, based on equipment information and preset hypothetical conditions, involves obtaining the cleaning time information of each process module through theoretical derivation, including: Obtain equipment information, including the cleaning cycle of the process module; Obtain a preset hypothetical virtual condition, wherein the preset hypothetical virtual condition is that all process modules enter the processing cycle synchronously; Based on the cleaning cycle of the process module and the preset hypothetical virtual conditions, the processing and cleaning process of each process module is simulated through theoretical derivation to obtain the cleaning time information of each process module. The cleaning time information includes the start time of the first cleaning, the end time, and the cleaning duration.

3. The scheduling method for a thin film deposition apparatus according to claim 2, characterized in that, Based on the cleaning cycle of the process module and preset hypothetical virtual conditions, the processing and cleaning flow of each process module is simulated through theoretical derivation to obtain the cleaning time information of each process module, including: A simulation mathematical model is constructed based on the cleaning cycle of the process module and the preset hypothetical virtual conditions. The constructed simulation mathematical model is run to obtain the cleaning time information for each process module.

4. The scheduling method for a thin film deposition apparatus according to claim 1, characterized in that, The ROPN model is constructed based on equipment and processing information, and then optimized based on the idle time of each process module to obtain the optimized ROPN model, including: The ROPN model is constructed based on equipment information and processing information. The equipment information includes equipment composition and vacuum end robot load conditions. The processing information includes process path and wafer flow quantity. The idle time of each process module is used as the initial state of the ROPN model to optimize the ROPN model and obtain the optimized ROPN model.

5. A scheduling device for a thin film deposition apparatus, characterized in that, include: The first derivation module is used to obtain equipment information. Based on the equipment information and preset hypothetical virtual conditions, it obtains the cleaning time information of each process module through theoretical derivation. The cleaning time information includes the first cleaning start time, end time and cleaning duration. The second derivation module is used to acquire processing information. Based on the processing information and the cleaning time information of each process module, the idle time of each process module is obtained through theoretical derivation. Specifically, the processing information includes the processing cycle of the process module; a process module is selected as a reference module, and the first cleaning start time and cleaning duration corresponding to the reference module are acquired. The selection of the reference module serves as a time reference point to establish a unified benchmark for the time coordination of multiple modules; the actual start time corresponding to the reference module is calculated based on the first cleaning start time and cleaning duration. Based on the actual start time, combined with the processing cycle and cleaning time, the idle time of the benchmark module is derived using algebraic operations. Set the interval between the first cleaning end time of each process module and the first cleaning end time of the adjacent process module to be at least one processing cycle, as the interval principle; Based on the interval principle, the idle time of other process modules is obtained through the sequence pattern; The optimization module is used to build a ROPN model based on equipment information and processing information, and optimize the ROPN model based on the idle time of each process module to obtain an optimized ROPN model. The guidance module is used to guide the scheduling of thin film deposition equipment based on the optimized ROPN model.

6. A scheduling device for a thin film deposition apparatus, characterized in that, The scheduling device of the thin film deposition apparatus includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the scheduling device of the thin film deposition apparatus to perform the various steps of the scheduling method of the thin film deposition apparatus as claimed in any one of claims 1-4.

7. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the scheduling method for the thin film deposition apparatus as described in any one of claims 1-4.

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