Wafer throughput calculation method, calculation device and calculation system of a machine

Abstract

The application discloses a wafer production capacity calculation method, a calculation device and a calculation system of a machine table, relates to the technical field of semiconductor manufacturing devices, and aims at the fact that the prior art only outputs a comprehensive production capacity value and cannot locate the specific reason for efficiency reduction. The actual processing time, the waiting time and the preparation time of a wafer are calculated through log data of wafer operation, then the WPH of each chamber in a target machine table is calculated, and the WPH of the target machine table is obtained according to the physical topological structure of each chamber. When there are multiple batches of continuous or cross operation, the overlapping time is removed by time axis projection to avoid repeated calculation of the preparation time of the batch wafer. The application can accurately distinguish whether the efficiency reduction of the machine table is caused by the processing, the waiting or the preparation link, eliminates the repeated calculation caused by the overlapping of multiple batches, and provides a high-precision, dynamically-updatable production capacity index for real-time scheduling and production capacity optimization of a wafer factory.

Description

Technical Field

[0001] This application relates to the field of semiconductor manufacturing technology, and in particular to a method and computing device for calculating the wafer capacity of a machine. Background Technology

[0002] In semiconductor wafer manufacturing, WPH (Wafer Per Hour) is one of the most critical metrics for measuring the capacity and efficiency of semiconductor equipment. Accurate WPH data is fundamental for wafer fabs to conduct capacity planning, bottleneck analysis, and automated scheduling. As semiconductor process nodes continue to shrink, the demands for lean manufacturing and intelligent manufacturing in factories are increasing. Traditional WPH assessment methods based on macroscopic statistics are no longer sufficient to meet the real-time, high-precision scheduling requirements of modern automated wafer fabs.

[0003] Currently, the working capacity (WPH) of a semiconductor machine is typically obtained using static statistical methods based on historical average data or macroscopic estimation methods that multiply theoretical capacity by overall equipment efficiency (OEE). However, these existing technologies usually only provide an overall capacity value. Since the WPH of a semiconductor machine is affected by a variety of factors in actual operation, when machine efficiency declines, it is impossible to accurately pinpoint the specific cause of the efficiency decline (e.g., slower processing, excessive waiting time, or excessive preparation time), which in turn affects the optimization of production efficiency and the accuracy of real-time scheduling. Summary of the Invention

[0004] This application provides a method, computing device, and computing system for calculating the wafer capacity of a machine, with the aim of dynamically and accurately obtaining the wafer capacity of the machine and precisely identifying the specific factors affecting the machine capacity, so as to solve the problem of reduced production efficiency caused by the inability to locate the cause of efficiency decline in the prior art.

[0005] To achieve the above objectives, this application provides the following technical solution: A method for calculating the wafer capacity of a machine includes: Step S1: Obtain the time log data of the wafer operation on the target machine; Step S2: Calculate the time components corresponding to each chamber inside the target machine based on the time log data; the time components include: actual processing time, waiting time, and preparation time. Step S3: Based on the time components and the number of wafers processed in a single operation within the target machine, obtain the WPH of each chamber inside the target machine; Step S4: Based on the physical topology of each chamber inside the target machine, aggregate the WPH of each chamber into the WPH of the target machine.

[0006] Optionally, step S4 specifically includes: If the target machine has a parallel topology and each chamber performs the same process, then the sum of the WPH of each chamber is calculated to obtain the WPH of the target machine. If the target machine has a serial topology in each chamber and the wafer flows through each chamber sequentially for processing, then the minimum WPH of each chamber is taken as the WPH of the target machine. If each chamber of the target machine is a hybrid cluster topology consisting of serial and parallel topologies, then the WPH of all chambers with parallel topologies is calculated first, and then the minimum WPH of each chamber with serial topologies is taken as the WPH of the target machine.

[0007] Optionally, the time when the i-th batch or the i-th wafer begins processing in the chamber can be obtained based on the time log data. and processing time The time it takes for the (i-1)th batch or the (i-1)th wafer to be processed within the chamber. The time when the i-th batch of wafers enters the target machine (Track_in) and the time when it leaves the target machine (Track_out); the number of wafers in the i-th batch (Lotqty); According to the formula The actual processing time was calculated. According to the formula The waiting time was calculated. According to the formula The preparation time is calculated, wherein, This indicates the time when the i-th batch of wafers enters the machine but has not yet started operation. This indicates the time during which the i-th batch of wafers completes its operation but has not yet left the machine.

[0008] Optionally, when the target machine periodically performs cleaning operations, the time component further includes cleaning time; the step of calculating the time component corresponding to each chamber inside the target machine based on the time log data includes: For equipment in the cleaning area, it will be based on the formula. The calculated average time required to clean a single wafer is taken as the cleaning time, wherein, _1 represents the average time for the target machine chamber to perform one self-cleaning operation, and N represents the average number of wafers in the chamber that will perform one self-cleaning operation after completing the job. N is a positive integer. For diffusion zone equipment, it will be based on the formula The calculated average cleaning time per furnace is taken as the cleaning time, wherein, _2 represents the duration of a cleaning cycle, and max_val represents the average number of furnaces after which a cleaning cycle will be triggered.

[0009] Optionally, obtaining the WPH corresponding to each chamber inside the target machine based on the time components and the number of wafers in a single operation of the target machine includes: The theoretical peak value WPH of each chamber is calculated based on the minimum actual processing time of a single wafer, the minimum waiting time of a single wafer, and the number of wafers per operation of the target machine. The actual WPH of each chamber is calculated based on the statistical values ​​of the actual processing time of a single wafer, the statistical values ​​of the waiting time of a single wafer, the preparation time of a batch of wafers, and the number of wafers in a single operation of the target machine.

[0010] Optionally, both the statistical value of the actual processing time and the statistical value of the waiting time are statistically preferred averages; the statistically preferred average is the average of data taken from a previous preset proportion or a previous preset quantity.

[0011] Optionally, when multiple batches of wafers are continuously or overlappingly processed on the target machine, the time periods of each batch of wafers processed on the target machine are projected onto the same time axis, overlapping time periods are identified, and the overlapping time periods are merged to obtain a merged time period. The net occupancy time of each batch of wafers is obtained based on the merged time period, and the actual processing time, waiting time, and preparation time of each batch of wafers are calculated based on the net occupancy time.

[0012] Optionally, a method for calculating the wafer capacity of a machine further includes: dynamically calculating the real-time WPH of the target machine based on a rolling time window, and scheduling a downstream task system according to the real-time WPH.

[0013] Optionally, a method for calculating the wafer capacity of a machine further includes: updating the WPH of the target machine to obtain the real-time WPH when a preset trigger event is detected, and scheduling a downstream task system according to the real-time WPH.

[0014] Optionally, the downstream task scheduling system according to the real-time WPH scheduler specifically includes: The dispatching strategy is adjusted based on the real-time WPH.

[0015] Optionally, the downstream task scheduling system according to the real-time WPH scheduler specifically includes: If the real-time WPH is lower than a preset threshold, an abnormal warning is triggered.

[0016] Optionally, the downstream task scheduling system according to the real-time WPH scheduler specifically includes: Equipment efficiency analysis and / or capacity prediction are performed based on the real-time WPH.

[0017] Optionally, acquiring the time log data of the target machine's wafer operation includes: Extract the time log data of the wafers of the target machine from the database.

[0018] Optionally, before retrieving the time log data of the wafers from the database for the target machine, the method further includes: Real-time capture of physical motion signals at the target machine end, the physical motion signals including at least the actions of batch wafers entering and leaving the machine and the actions of a single wafer entering and leaving the chamber; The physical action signals are converted into event logs with precise timestamps, i.e., time log data, and stored in the database.

[0019] Optionally, acquiring the time log data of the wafers on the target machine includes: The communication messages between the target machine and the host computer are monitored, and the time log data of the wafer of the target machine is obtained by parsing.

[0020] Optionally, acquiring the time log data of the wafers on the target machine includes: The time log data of the wafers in the target machine is determined based on the sensing signals of the sensors used to detect the wafers entering and leaving the machine.

[0021] Accordingly, this application also provides a wafer capacity calculation device for a machine, comprising: The data extraction module is used to acquire time log data of wafer operations on the target machine. The time component calculation module is used to calculate the time component corresponding to each chamber inside the target machine based on the time log data; the time component includes: actual processing time, waiting time, and preparation time. The chamber capacity calculation module is used to obtain the WPH of each chamber inside the target machine based on the time component and the number of wafers in a single operation within the target machine. The machine capacity calculation module is used to aggregate the WPH of each chamber into the WPH of the target machine based on the physical topology of each chamber inside the target machine.

[0022] Accordingly, this application also provides a wafer capacity calculation system for a machine, wherein the wafer capacity calculation device is used to execute any of the above-mentioned calculation methods.

[0023] Compared with existing technologies, the above technical solution has the following advantages: The calculation method provided in this application precisely decomposes the traditionally vague "processing time" of the equipment into the actual wafer processing time, waiting time, and preparation time. The preparation time is defined as "the period during which a batch of wafers occupies the equipment but is not actually processed." Therefore, this application can directly pinpoint the specific reasons for the decline in equipment efficiency: if the actual wafer processing time is abnormal, the problem lies in the processing stage itself; if the wafer waiting time is abnormal, the problem lies in wafer queuing or chamber scheduling; if the batch wafer preparation time is abnormal, the problem lies in non-processing operations such as heating, source replacement, wafer extraction, and cooling. This overcomes the limitations of existing technologies that only output a comprehensive WPH value and cannot attribute causes, making them essentially "black boxes."

[0024] Based on the actual wafer processing time, waiting time, preparation time, and the number of wafers per operation of the target machine, the WPH (Wafer Power Per Hour) of each chamber within the target machine is calculated. This allows the chamber-level capacity index to reflect the true processing efficiency, waiting losses, and preparation losses of that chamber, providing an accurate micro-data foundation for subsequent whole-machine aggregation. According to the physical topology of the chambers, the WPH of each chamber within the target machine is aggregated into the WPH of the target machine. This application can accurately map micro-chamber-level capacity data to macro-machine-level capacity indicators, thereby providing the production scheduling system with high-precision WPH values ​​that match the physical structure of the machine, avoiding capacity estimation errors caused by ignoring the cooperation between chambers. Furthermore, by dynamically acquiring the real-time WPH of the machine, a dynamically updatable capacity index is provided to the production scheduling system, enabling dynamic scheduling. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a method for calculating wafer capacity of a machine, as provided in this application embodiment; Figure 2 This is a schematic diagram of a wafer capacity calculation device for a machine provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0029] Secondly, this application will be described in detail with reference to the schematic diagrams. When describing the embodiments of this application in detail, for ease of explanation, the drawings showing the device structure will be partially enlarged without adhering to the general scale. Moreover, the schematic diagrams are only examples and should not limit the scope of protection of this application.

[0030] The various parts of this manual are described in a combination of parallel and progressive methods. Each part focuses on the differences between the other parts, and the same or similar parts can be referred to each other.

[0031] This application provides a method for calculating the wafer capacity of a machine, and the flowchart of the calculation method is as follows: Figure 1 As shown, it includes: S1: Obtain the time log data of the wafer operation on the target machine.

[0032] Specifically, the operation data for each wafer and each batch (Lot) of wafers is obtained, with a focus on extracting the following operation data: the time when the batch of wafers enters the target machine (Track_in), the time when the batch of wafers leaves the target machine (Track_out), the time when the batch or a single wafer begins processing in the target machine's chamber (Process_start), the time when the batch or a single wafer completes processing in the target machine's chamber (Process_end), and the number of wafers in the batch (Lotqty). The above time values ​​are multiple sets of data corresponding to each wafer or each batch of wafers.

[0033] S2: Calculate the time components corresponding to each chamber inside the target machine based on the time log data; the time components include: actual processing time, waiting time, and preparation time.

[0034] Specifically, the actual processing time is the actual processing time for a batch or a single wafer, the waiting time is the actual waiting time for a batch or a single wafer, and the preparation time is the preparation time for a batch of wafers. Simultaneously, this method automatically matches and invokes the corresponding underlying indicator calculation model based on the machine type, operating mode, and machine configuration information in the database. The calculation methods that this model can use are as follows: The time when the i-th batch or the i-th wafer begins processing in the chamber is obtained from the time log data. and processing time The time it takes for the (i-1)th batch or the i-th wafer to be processed within the chamber. The time when the i-th batch of wafers enters the machine but has not yet started operation. The time during which the i-th batch of wafers completes its operation but has not yet left the machine. The quantity of wafers in the i-th batch is Lotqty; Actual processing time (PT) for a batch or a single wafer: PT i =Process_end i -Process_start i ; Batch or single wafer wait time (WT) reflects the time the wafer spends in the chamber: WT i =Process_start i - Process_end i-1 ; The batch wafer setup time is calculated using the following formula:

[0035] The core idea of ​​this calculation formula is that the time during which a batch of wafers occupies the physical space of the chamber but does not produce any actual wafer processing action is accurately identified and categorized as Setup time.

[0036] In some optional implementations, when multiple batches of wafers are being processed consecutively or concurrently on the target machine, overlapping time periods are identified, and the overlapping time periods are merged to obtain a merged time period. The net occupancy time of each batch of wafers is obtained based on the merged time period, and the actual processing time, waiting time, and preparation time of each batch of wafers are calculated based on the net occupancy time.

[0037] Specifically, the time of all batches of operations on the target machine is laid out on the same timeline; overlapping segments on all timelines are identified and merged, and then the net occupied segment of each batch is segmented. This method accurately calculates setup time, preventing WPH from being artificially high or low, which could affect the WPH calculation results for the target machine.

[0038] In a preferred embodiment of this application, if the target equipment belongs to a process type that requires periodic cleaning operations (e.g., wet cleaning equipment or diffusion furnace tube equipment), then an additional cleaning time (Purge time) is extracted from the time components as one of the time components. This cleaning time reflects the time loss incurred by the equipment in periodically performing cleaning operations to maintain the stability of the process environment. This time cannot be directly attributed to the actual processing time, waiting time, or preparation time of a single wafer, and therefore needs to be calculated independently.

[0039] Specifically, the duration of a single cleaning operation on the target machine and the workload between two adjacent cleaning operations are obtained. The duration of a single cleaning operation is divided by the workload to obtain the average cleaning time allocated per unit of workload. The workload is the number of wafers or the number of wafer batches. The workload is determined based on the number of wafers (e.g., how many wafers were processed) or the number of wafer batches (e.g., how many batches of wafers were processed). The specific workload used depends on the machine type and the triggering mechanism of the cleaning operation.

[0040] As a specific embodiment, for the cleaning zone equipment, it will be based on the formula The calculated average time required to clean a single wafer is taken as the cleaning time, wherein, _1 represents the average time for the target machine chamber to perform one self-cleaning operation, and N represents the average number of wafers in the chamber that will perform one self-cleaning operation after completing the job. N is a positive integer. As a specific embodiment, for the diffusion region device, it will be based on the formula The calculated average cleaning time per furnace is taken as the cleaning time, wherein, _2 represents the duration of a cleaning cycle, and max_val represents the average number of furnaces after which a cleaning cycle will be triggered.

[0041] S3: Based on the time components and the number of wafers in a single operation within the target machine, obtain the WPH of each chamber inside the target machine.

[0042] In a preferred embodiment of this application, calculating the WPH of each chamber specifically includes calculating the theoretical peak WPH and the actual WPH of each chamber. These two WPHs reflect the machine's capacity level from different dimensions, where the theoretical peak WPH characterizes the machine's limit throughput capacity under ideal conditions, while the actual WPH characterizes the machine's true throughput capacity after including reasonable losses.

[0043] Specifically, the theoretical peak capacity (Peak_WPH) is calculated using the following formula:

[0044] Where BatchSize is the number of wafers processed in a single operation in each chamber of the target machine, min(PT) is the minimum actual processing time for a single wafer, and min(WT) is the minimum waiting time for a single wafer. Since the parameters in the formula are all in seconds, the numerator (3600) of the formula represents 3600 seconds corresponding to one hour.

[0045] The actual WPH (Internal WPH) is calculated using the following formula:

[0046] Here, mean50% (PT) is the statistical value of the actual processing time of a single wafer, and mean50% (WT) is the statistical value of the waiting time of a single wafer. It should be noted that the statistical values ​​here are the average of the top 50% of the data arranged from smallest to largest, which is the optimal statistical mean. Setup time is the preparation time for the batch of wafers mentioned above, and Purge time is the cleaning time mentioned above. This item needs to be added for target machines that require periodic cleaning operations.

[0047] Optionally, the preferred statistical mean here can be the average of the data from the first preset proportion (e.g., the first 50% of data) or the first preset number (e.g., the first 50 data points).

[0048] S4: Based on the physical topology of each chamber inside the target machine, aggregate the WPH of each chamber into the WPH of the target machine.

[0049] As a preferred embodiment of this application, the following aggregation method is specifically adopted: Since semiconductor equipment typically contains multiple parallel or serial cavities, the chamber-level WPH needs to be calculated based on the physical topology of each chamber in the target equipment. For parallel topologies: if multiple chambers within the target equipment perform the same process and are mutually redundant (i.e., wafers can be allocated to any available chamber for parallel processing), then the target equipment's WPH is equal to the sum of the WPHs of each chamber. For example, if a machine has four identical etching chambers, and each chamber has a WPH of 30 wafers / hour, then the overall machine-level WPH is 120 wafers / hour.

[0050] For a serial topology: the wafers need to flow through multiple chambers in a fixed order. The WPH of the target machine is limited by the slowest link, which is equal to the minimum WPH of each chamber in the serial structure. For example, if a machine has three serial chambers with WPHs of 40 wafers / hour, 30 wafers / hour, and 50 wafers / hour respectively, then the overall machine-level WPH is 30 wafers / hour.

[0051] For hybrid topologies: When both parallel and serial topologies exist within the target machine (e.g., a group of parallel chambers followed by one or more chambers in series, or multiple parallel groups in a serial relationship), the WPH (Wafer Power Per Hour) of the parallel chambers at the same level is first accumulated. The accumulated result is then considered the equivalent WPH for that level. Finally, the minimum value is taken for the upstream and downstream serial levels. For example, a machine has two parallel chambers A and B (WPHs of 20 and 30 respectively), followed by a chamber C in series (WPH of 40). The equivalent WPH of the parallel combination is first calculated as 20 + 30 = 50, then the minimum value is taken with the WPH of chamber C. Therefore, the overall machine-level WPH = min(50, 40) = 40 wafers / hour. If the series relationship is more complex, the above rules can be applied recursively.

[0052] Using the above method, this application can accurately map the capacity data at the micro-chamber level to the capacity indicators at the macro-machine level, thereby providing the production scheduling system with a WPH value that matches the physical structure of the machine, and avoiding capacity estimation errors caused by ignoring the cooperation relationship between chambers.

[0053] In summary, the calculation method provided in this embodiment accurately decomposes the machine occupancy time into the actual wafer processing time, waiting time, and preparation time, enabling direct identification of the specific reasons for machine efficiency decline: if the actual wafer processing time is abnormal, the problem lies in the processing stage itself; if the wafer waiting time is abnormal, the problem lies in wafer queuing or chamber scheduling; if the batch wafer preparation time is abnormal, the problem lies in non-processing operations such as heating, source replacement, wafer extraction, and cooling. This overcomes the limitations of existing technologies that only output a comprehensive WPH value and cannot attribute causes, essentially acting as a "black box."

[0054] Based on the actual wafer processing time, waiting time, preparation time, and the number of wafers per operation of the target machine, the WPH of each chamber is calculated, so that the chamber-level capacity index can reflect the true processing efficiency, waiting loss, and preparation loss of the chamber, providing an accurate microscopic data basis for subsequent whole-machine polymerization.

[0055] Furthermore, by projecting the time periods of each batch of wafers onto the same time axis, overlapping time periods are identified and merged. These overlapping time periods are then combined to obtain a merged time period, ensuring that the preparation time of each batch of wafers is not counted twice. Based on the merged time period, the net occupancy time of each batch of wafer operations can be obtained, thereby calculating the actual processing time, waiting time, and preparation time of each batch of wafer operations. This mechanism avoids the same physical time being counted multiple times in different batches due to concurrent operations, ensuring the physical accuracy of the calculation of net machine occupancy time and preparation time, and preventing distortion of capacity analysis.

[0056] In some preferred embodiments, the above calculation method further includes: dynamically calculating the real-time WPH of the target machine based on a rolling time window; the time length and sliding step size of the rolling time window are both configurable parameters; and scheduling the downstream task system according to the real-time WPH.

[0057] Understandably, the system constructs a rolling time window model to replace the traditional static periodic update mode. Both the length of the rolling time window and the sliding step size are configurable parameters. Users can customize the length of the time window (e.g., several hours, days, weeks, or months) and the sliding step size (i.e., the update frequency of WPH) according to actual needs. Whenever the sliding step size is reached, the system automatically recalculates the WPH based on historical job data within the current time window, thereby generating a near real-time dynamic WPH value. Based on the real-time WPH value, downstream task systems are then scheduled for in-depth modeling of equipment efficiency and accurate prediction of future output, assisting factories in capacity planning. Through the aforementioned rolling time window mechanism, this application achieves near real-time tracking of machine capacity, providing downstream task systems with highly timely and confident WPH input, effectively solving the problems of long update cycles and inability to support dynamic scheduling in traditional static WPH data.

[0058] As an alternative, in addition to the above-described rolling time window (time-driven) embodiments, this application also provides another alternative dynamic scheduling triggering mechanism.

[0059] Upon detecting a preset trigger event, the WPH of the target machine is updated to obtain the real-time WPH, and the downstream task system is scheduled based on the real-time WPH.

[0060] This can be achieved by extending "time-driven" to "event-driven" or "time-event hybrid driven". In event-driven mode, WPH is not recalculated at a fixed time frequency, but rather triggered immediately upon detection of a preset trigger event.

[0061] The advantages of event-driven or hybrid-driven approaches are: they can greatly save system computing resources because there is no need for repeated calculations when machines are idle or unchanged. At the same time, the response latency to sudden events approaches zero, and scheduling is more agile.

[0062] In some optional embodiments, the real-time WPH is pushed to downstream business systems to perform the following operations: adjust the dispatch strategy based on the real-time WPH.

[0063] To adjust the dispatching strategy based on real-time WPH (Wafer Health Per Hour), the real-time WPH is pushed to the automated material handling system and the real-time dispatching system. These systems use the real-time WPH as a true reflection of the machine's current capacity, and optimize the dispatching strategy accordingly: when the real-time WPH is high, the number of wafer batches dispatched can be appropriately increased to fully utilize the machine's capacity; when the real-time WPH is low, the dispatching volume can be reduced to avoid congestion in front of the machine or excessive wafer waiting time. Through dynamic adjustments, production line load balance is achieved.

[0064] In some optional embodiments, the real-time WPH is pushed to the downstream business system to perform the following operations: if the real-time WPH is lower than a preset threshold, an anomaly warning is triggered.

[0065] For abnormal warnings, the system presets one or more WPH thresholds, compares the calculated real-time WPH with the thresholds, and if the real-time WPH is lower than the preset threshold, it is determined that the machine may have performance degradation, process deviation or imminent failure, and the system automatically triggers a warning signal. This mechanism enables operators to respond to production fluctuations in real time and reduce unplanned downtime.

[0066] In some optional embodiments, the real-time WPH is pushed to downstream business systems for performing the following operations: performing equipment efficiency analysis or capacity forecasting based on the real-time WPH.

[0067] For equipment efficiency analysis or capacity forecasting, real-time WPH data can be stored in an analytics engine (such as a data warehouse or big data platform) through standardized interfaces. The analytics engine utilizes historically accumulated real-time WPH data to perform in-depth modeling of equipment efficiency, identifying long-term trends, cyclical fluctuations, or abnormal patterns. Simultaneously, in conjunction with production plans, it accurately predicts output over a future period based on the current real-time WPH, assisting factories in capacity planning and resource allocation.

[0068] Specifically, downstream business systems may include one or more of automated scheduling systems, real-time monitoring systems, and decision analysis systems. This application does not limit the specific type and number of downstream systems, and they can be flexibly configured according to the factory's automation level and management needs during actual deployment.

[0069] It should be noted that the above three operations can be implemented independently or in any combination. For example, some factories may only need to use real-time WPH to adjust dispatching strategies and trigger anomaly warnings, without requiring a separate decision analysis module. This application does not limit this; as long as at least one operation is implemented, it falls within the scope of protection of this claim. Through the above methods, the real-time WPH of this application not only serves capacity calculation itself but also directly empowers production scheduling, equipment monitoring, and decision analysis, forming a complete closed loop from data acquisition and calculation to application feedback, significantly improving the automation level and production efficiency of wafer fabs.

[0070] In some possible embodiments, step S1 (acquiring time log data of wafer operations on the target machine) includes: Extract the time log data of the target machine's wafers from the database. The database stores event logs with precise timestamps, i.e., time log data, which facilitates subsequent accurate calculations of time components.

[0071] In some possible embodiments, before retrieving the time log data of the target machine wafer operation from the database, the process further includes: Real-time capture of physical motion signals at the target machine end, including at least the actions of batch wafers entering and leaving the machine and the actions of a single wafer entering and leaving the chamber; Physical action signals are converted into event logs with precise timestamps and stored in the database.

[0072] Specifically, the physical motion signals at the target machine are captured in real time through the Equipment Automation Program (EAP). These physical motion signals include at least: the entry of batches of wafers into the machine, the exit of batches of wafers from the machine, the entry of a single wafer into the chamber to begin processing, and the exit of a single wafer from the chamber to complete processing. These signals reflect the physical events of interaction between the machine and the wafers and are the original source for subsequent timestamp extraction.

[0073] After capturing the aforementioned physical action signals, the EAP system converts them into event logs with precise timestamps. Each event log entry records at least the event type (e.g., "batch enters," "batch leaves," "processing begins," "processing completes"), the time of occurrence (accurate to the second), the identifiers of the involved objects (e.g., batch ID, wafer ID), and relevant parameters (e.g., number of wafers). The converted log data is uploaded to the Manufacturing Execution System (MES) via the factory network. Upon receiving the event logs, the MES structures and organizes them, ultimately storing them in the production database. Through this acquisition and storage process, the database accumulates a large amount of wafer operation history records with precise timestamps, providing a reliable data foundation for subsequent data extraction and WPH calculation in this application.

[0074] As an alternative, for older machines that do not have a complete EAP system, external hardware sensors (such as robotic arm vibration sensors and machine main power supply current / power fluctuation monitoring modules) can be used to physically sense the start and end times of processing actions and convert the collected physical signals into timestamp logs.

[0075] Therefore, in some possible embodiments, obtaining the time log data of wafer operations on the target machine includes: determining the time log data of the wafers on the target machine based on the sensing signals of sensors in the target machine used to detect the wafers entering and leaving the machine.

[0076] In addition, it is also possible to directly acquire the underlying messages based on the SECS (Semiconductor Equipment Communication Standard) / GEM (Generic Equipment Model) protocol.

[0077] Therefore, in some possible embodiments, acquiring the time log data of the target machine's wafer operation includes: Monitor the communication messages between the target machine and the host computer, and parse them to obtain the time log data of the wafers on the target machine.

[0078] The two methods described above for obtaining wafer time log data can bypass network latency or software logging vulnerabilities in the MES system and obtain the actual physical processing time. Accordingly, embodiments of this application also provide a wafer capacity calculation device for a machine, such as... Figure 2 As shown, it specifically includes: The data extraction module is used to acquire time log data of wafer operations on the target machine. The time component calculation module is used to calculate the time component corresponding to each chamber inside the target machine based on the time log data; the time component includes: actual processing time, waiting time, and preparation time. The chamber capacity calculation module is used to obtain the WPH of each chamber inside the target machine based on the time component and the number of wafers in a single operation within the target machine. The machine capacity calculation module is used to aggregate the WPH of each chamber into the WPH of the target machine based on the physical topology of each chamber inside the target machine.

[0079] In the optional implementation, the machine capacity calculation module is also used for: If the target machine has a parallel topology and each chamber performs the same process, then the sum of the WPH of each chamber is calculated to obtain the WPH of the target machine. If the target machine has a serial topology in each chamber and the wafer flows through each chamber sequentially for processing, then the minimum WPH of each chamber is taken as the WPH of the target machine. If each chamber of the target machine is a hybrid cluster topology consisting of serial and parallel topologies, then the WPH of all chambers with parallel topologies is calculated first, and then the minimum WPH of each chamber with serial topologies is taken as the WPH of the target machine.

[0080] In the optional implementation, if the target machine needs to perform cleaning operations periodically, the time component calculation module is also used to extract the cleaning time as one of the time components; The step of calculating the time components corresponding to each chamber inside the target machine based on the time log data includes: For equipment in the cleaning area, it will be based on the formula. The calculated average time required to clean a single wafer is taken as the cleaning time, wherein, _1 represents the average time for the target machine chamber to perform one self-cleaning operation, and N represents the average number of wafers in the chamber that will perform one self-cleaning operation after completing the job. N is a positive integer. For diffusion zone equipment, it will be based on the formula The calculated average cleaning time per furnace is taken as the cleaning time, wherein, _2 represents the duration of a cleaning cycle, and max_val represents the average number of furnaces after which a cleaning cycle will be triggered.

[0081] In the optional implementation, the chamber capacity calculation module is specifically used to calculate the theoretical peak WPH and actual WPH of each chamber: The theoretical peak value WPH of each chamber is calculated based on the minimum actual processing time of the single wafer, the minimum waiting time of the single wafer, and the number of wafers in a single operation of the target machine. The actual WPH of each chamber is calculated based on the statistical values ​​of the actual processing time of the single wafer, the statistical values ​​of the waiting time of the single wafer, the preparation time of the batch wafers, and the number of wafers in a single operation of the target machine.

[0082] In an optional implementation, a wafer capacity calculation device for a machine includes a time component calculation module that, when multiple batches of wafers are continuously or overlappingly processed on the target machine, projects the time periods of each batch of wafers processed on the target machine onto the same time axis, identifies overlapping time periods, merges the overlapping time periods to obtain a merged time period, obtains the net occupancy time of each batch of wafers based on the merged time period, and calculates the actual processing time, waiting time, and preparation time of each batch of wafers based on the net occupancy time.

[0083] In an optional implementation, a wafer capacity calculation device for a machine further includes: The first dynamic calculation module is used to dynamically calculate the real-time WPH of the target machine based on a rolling time window, and to schedule the downstream task system according to the real-time WPH.

[0084] In an optional implementation, a wafer capacity calculation device for a machine further includes: The second dynamic calculation module is used to update the WPH of the target machine when a preset trigger event is detected, to obtain the real-time WPH, and to schedule the downstream task system according to the real-time WPH.

[0085] In an optional implementation, a wafer capacity calculation device for a machine further includes: The first task scheduling module is used to adjust the dispatch strategy based on the real-time WPH.

[0086] In an optional implementation, a wafer capacity calculation device for a machine further includes: The second task scheduling module is used to trigger an abnormal warning if the WPH is lower than a preset threshold.

[0087] In an optional implementation, a wafer capacity calculation device for a machine further includes: The third task scheduling module is used to perform equipment efficiency analysis and / or capacity prediction based on the real-time WPH.

[0088] In an optional implementation, a wafer capacity calculation device for a machine includes a data extraction module further configured to extract the time log data of the wafers of the target machine from a database.

[0089] In an optional implementation, a wafer capacity calculation device for a machine also includes an event log recording module for capturing physical action signals at the target machine in real time. The physical action signals include at least the actions of batch wafers entering and leaving the machine and the actions of a single wafer entering and leaving the chamber. The physical action signals are converted into event logs with precise timestamps, i.e., time log data, and stored in the database.

[0090] In an optional implementation, a wafer capacity calculation device for a machine includes a data extraction module that is further configured to monitor communication messages between the target machine and a host computer, and parse the time log data of the wafers of the target machine.

[0091] In an optional implementation, a wafer capacity calculation device for a machine includes a data extraction module further configured to determine the time log data of the wafers in the target machine based on the sensing signals of sensors used to detect the wafers entering and leaving the machine.

[0092] Accordingly, this application also provides a wafer capacity calculation system for a machine, including: the wafer capacity calculation device described above, wherein the calculation system is used to execute the calculation method described above.

[0093] The features described above regarding the disclosed embodiments can be substituted or combined with each other to enable those skilled in the art to implement or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating wafer production capacity, characterized in that, include: Step S1: Obtain the time log data of the wafer operation on the target machine; Step S2: Calculate the time components corresponding to each chamber inside the target machine based on the time log data; The time components include: actual processing time, waiting time, and preparation time; Step S3: Based on the time components and the number of wafers processed in a single operation within the target machine, obtain the WPH of each chamber inside the target machine; Step S4: Based on the physical topology of each chamber inside the target machine, aggregate the WPH of each chamber into the WPH of the target machine.

2. The calculation method according to claim 1, characterized in that, Step S4 specifically includes: If the target machine has a parallel topology and each chamber performs the same process, then the sum of the WPH of each chamber is calculated to obtain the WPH of the target machine. If the target machine has a serial topology in each chamber and the wafer flows through each chamber sequentially for processing, then the minimum WPH of each chamber is taken as the WPH of the target machine. If each chamber of the target machine is a hybrid cluster topology consisting of serial and parallel topologies, then the WPH of all chambers with parallel topologies is calculated first, and then the minimum WPH of each chamber with serial topologies is taken as the WPH of the target machine.

3. The calculation method according to claim 1, characterized in that, The time when the i-th batch or the i-th wafer begins processing in the chamber is obtained from the time log data. and processing time The time it takes for the (i-1)th batch or the (i-1)th wafer to be processed within the chamber. The time when the i-th batch of wafers enters the target machine (Track_in) and the time when it leaves the target machine (Track_out); the number of wafers in the i-th batch (Lotqty); According to the formula The actual processing time was calculated. According to the formula The waiting time was calculated. According to the formula The preparation time is calculated, wherein, This indicates the time when the i-th batch of wafers enters the machine but has not yet started operation. This indicates the time during which the i-th batch of wafers completes its operation but has not yet left the machine.

4. The calculation method according to claim 1, characterized in that, When the target machine performs cleaning operations periodically, the time component also includes cleaning time; The step of calculating the time components corresponding to each chamber inside the target machine based on the time log data includes: For equipment in the cleaning area, it will be based on the formula. The calculated average time required to clean a single wafer is taken as the cleaning time, wherein, _1 represents the average time for the target machine chamber to perform one self-cleaning operation, and N represents the average number of wafers in the chamber that will perform one self-cleaning operation after completing the operation. N is a positive integer. For diffusion zone equipment, it will be based on the formula The calculated average cleaning time per furnace is taken as the cleaning time, wherein, _2 represents the duration of a cleaning cycle, and max_val represents the average number of furnaces after which a cleaning cycle will be triggered.

5. The calculation method according to claim 1, characterized in that, The step of obtaining the WPH corresponding to each chamber inside the target machine based on the time components and the number of wafers in a single operation of the target machine includes: The actual WPH of each chamber is calculated based on the statistical values ​​of the actual processing time of a single wafer, the statistical values ​​of the waiting time of a single wafer, the preparation time of a batch of wafers, and the number of wafers in a single operation of the target machine.

6. The calculation method according to claim 5, characterized in that, The statistical values ​​of the actual processing time and the waiting time are both statistically preferred averages; the statistically preferred averages are the average values ​​of data taken from the previous preset proportion or the previous preset quantity.

7. The calculation method according to claim 1, characterized in that, When multiple batches of wafers are continuously or overlappingly processed on the target machine, the time periods of each batch of wafers processed on the target machine are projected onto the same time axis, overlapping time periods are identified, and the overlapping time periods are merged to obtain a merged time period. The net occupancy time of each batch of wafers is obtained based on the merged time period, and the actual processing time, waiting time, and preparation time of each batch of wafers are calculated based on the net occupancy time.

8. The calculation method according to claim 1, characterized in that, Also includes: The real-time WPH of the target machine is dynamically calculated based on a rolling time window, and the downstream task system is scheduled according to the real-time WPH.

9. The calculation method according to claim 1, characterized in that, Also includes: Upon detecting a preset trigger event, the WPH of the target machine is updated to obtain the real-time WPH, and the downstream task system is scheduled according to the real-time WPH.

10. The calculation method according to claim 8 or 9, characterized in that, The real-time WPH scheduling downstream task system specifically includes: The dispatching strategy is adjusted based on the real-time WPH.

11. The calculation method according to claim 8 or 9, characterized in that, The real-time WPH scheduling downstream task system specifically includes: If the real-time WPH is lower than a preset threshold, an abnormal warning is triggered.

12. The calculation method according to claim 8 or 9, characterized in that, The real-time WPH scheduling downstream task system specifically includes: Equipment efficiency analysis and / or capacity prediction are performed based on the real-time WPH.

13. A wafer capacity calculation device for a machine, characterized in that, include: The data extraction module is used to acquire time log data of wafer operations on the target machine. The time component calculation module is used to calculate the time component corresponding to each chamber inside the target machine based on the time log data. The time components include: actual processing time, waiting time, and preparation time; The chamber capacity calculation module is used to obtain the WPH of each chamber inside the target machine based on the time component and the number of wafers in a single operation within the target machine. The machine capacity calculation module is used to aggregate the WPH of each chamber into the WPH of the target machine based on the physical topology of each chamber inside the target machine.

14. A wafer capacity calculation system for a machine, characterized in that, include: The wafer capacity calculation apparatus of claim 13, wherein the calculation system is used to execute the calculation method of any one of claims 1-12.

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