Semiconductor factory power demand simulation device and method considering scheduling and supply

Abstract

The invention discloses a semiconductor factory power demand simulation device and method considering scheduling and supply, and the device comprises a production scheduling module which is used for obtaining semiconductor production task information, and giving a processing sequence of each semiconductor production device; the equipment energy consumption module is used for acquiring the processing sequence, given by the production scheduling module, of each semiconductor production equipment, simulating the processing behavior of each semiconductor production equipment, and giving sequential power demand data of each semiconductor production equipment; and the pipe network supply module is used for acquiring the time sequence power demand data of each semiconductor production device given by the device energy consumption module, simulating a pipe network operation behavior according to the pipe network layout, and giving time sequence pipe network operation state data. By constructing a semiconductor factory power demand simulation model of three levels of production scheduling-equipment energy consumption-pipe network supply, accurate prediction of the power demand of the semiconductor factory is realized, and the problem that power demand prediction and planning of the semiconductor factory are disjointed is solved.

Description

Technical Field

[0001] This invention belongs to the technical field of semiconductor factory simulation, specifically relating to a semiconductor factory power demand simulation device and method that takes into account scheduling and supply. Background Technology

[0002] As a strategic industry that is technology-intensive, capital-intensive, and energy-intensive, accurate forecasting and assessment of the power demand of semiconductor manufacturing is a key support for ensuring stable operation of production lines, controlling operating costs, and achieving sustainable development.

[0003] Currently, some research focuses on the simulation and prediction of power demand for semiconductor equipment. For example, patent application CN120124312A discloses an adaptive digital twin simulation device and method for semiconductor equipment energy consumption. By setting a parameter update module and introducing a real-time data feedback mechanism, it dynamically updates process parameter information, improving the accuracy and real-time performance of the process equipment energy consumption simulation model, and enabling high-precision real-time simulation of the power demand of a single piece of equipment. In addition, patent application CN117932976A discloses a method and device for acquiring energy consumption data of a process machine group. By acquiring energy consumption data of a process machine group, based on the standard attributes of the equipment in the design stage, it can quickly and accurately give the average and peak amounts of energy required by the power system. Its core is to predict the group energy consumption based on the preset historical energy consumption data of a single machine.

[0004] The aforementioned technologies primarily focus on simulating energy consumption behavior at the equipment level or making statistical inferences based on historical data, without considering the relationship between factory production plans and equipment power needs. Furthermore, semiconductor factories commonly employ complex pipeline systems for centralized power supply, and the transmission characteristics of these pipelines (such as pressure loss and flow attenuation—dynamic physical effects) can constrain the actual power supply capacity of the system.

[0005] The current problem to be solved is how to adjust the power demand of production equipment based on the dynamic characteristics of the pipeline system in a semiconductor factory during the transmission process in order to meet the production needs of the equipment. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a semiconductor factory power demand simulation device and method that considers scheduling and supply. The device includes a production scheduling module, which acquires semiconductor production task information and provides the processing sequence for each semiconductor production equipment; an equipment energy consumption module, which acquires the processing sequence for each semiconductor production equipment from the production scheduling module, simulates the processing behavior of each semiconductor production equipment, and provides time-series power demand data for each semiconductor production equipment; and a pipeline supply module, which acquires the time-series power demand data for each semiconductor production equipment from the equipment energy consumption module, and simulates pipeline operation behavior based on the pipeline layout, providing time-series pipeline operation status data. By constructing a three-level power demand simulation model for semiconductor factories—"production scheduling—equipment energy consumption—pipeline supply"—this invention achieves accurate prediction of power demand changes during production plan execution, effective identification of peak superposition risks and pipeline transmission bottlenecks, and accurate prediction of the entire semiconductor factory's power demand, fundamentally solving the problem of the disconnect between power demand prediction and planning in semiconductor factories.

[0007] In a first aspect, the present invention provides a semiconductor factory power demand simulation device that takes into account scheduling and supply, comprising: The production scheduling module acquires semiconductor production task information and provides the processing sequence for each semiconductor production equipment. The equipment energy consumption module obtains the processing sequence of each semiconductor production equipment given by the production scheduling module, simulates the processing behavior of each semiconductor production equipment, and provides the time-series power demand data of each semiconductor production equipment. The pipeline supply module acquires the time-series power demand data of each semiconductor production equipment from the equipment power consumption module, and simulates the pipeline operation behavior based on the pipeline layout to provide time-series pipeline operation status data.

[0008] Furthermore, the production task information includes production plan information and processing flow information. The processing sequence is a time-sequential processing operation instruction sequence for specific processes in semiconductors. The power demand data includes power flow demand data and power pressure demand data. The operating status data includes normal or abnormal status.

[0009] Furthermore, semiconductor production task information is obtained, and the processing sequence of each semiconductor production equipment is provided, specifically including: Standardize the production planning information to provide the target production volume for each type of semiconductor during the specified time period; Based on the processing flow information of various types of semiconductors, combined with the randomized logistics transmission time and the production status information of each semiconductor production equipment, the processing sequence of each semiconductor production equipment is given.

[0010] Furthermore, the production status information is either idle or occupied. Based on the processing flow information of various types of semiconductors, combined with randomized logistics transmission time and the production status information of each semiconductor production equipment, the processing sequence of each semiconductor production equipment is given, specifically including: The processing flow information of various types of semiconductors is mapped to the process information of each semiconductor manufacturing equipment; Based on the exponential distribution random generation rule, the logistics transmission time between each process is given; Based on the process information, determine the time trigger point, specify the target semiconductor production equipment, query the current status of the target semiconductor production equipment, and determine the processing time including queuing time; By integrating processing time and logistics transportation time, the processing sequence of each semiconductor manufacturing equipment is given.

[0011] Furthermore, combining the exponential distribution random generation rule, the logistics transmission time between each process is given, specifically expressed as follows:

[0012] Among them, T trans,a Let λ be the material transport time required to enter the a-th process, be a constant, ln() be a logarithmic function, and U be the time required for material transport. a Let λ be the random number generated based on an exponential distribution when entering the a-th process; in this example, λ = 0.2min. -1 U a These are random numbers uniformly distributed within the interval (0,1) in the generation of an exponentially distributed random variable. A random number closer to 0 indicates a shorter logistics delivery time, while a random number closer to 1 indicates a longer logistics delivery time. By using the exponential distribution random generation rule, the uncertain logistics delay phenomenon in actual semiconductor processing can be simulated.

[0013] Furthermore, the processing sequence of each semiconductor manufacturing equipment is obtained from the production scheduling module, the processing behavior of each semiconductor manufacturing equipment is simulated, and the time-series power demand data of each semiconductor manufacturing equipment is provided, specifically including: Determine the baseline operating requirements of semiconductor manufacturing equipment, as well as the energy requirements of various types of semiconductors at different processes in various semiconductor manufacturing equipment; Based on the screening of processing moments in the processing sequence, the energy demand data of semiconductor manufacturing equipment is spliced ​​together to provide initial time-series power demand data; Based on the filtering of idle moments in the processing sequence, and by integrating the initial time-series power demand data and the baseline operating demand data, the time-series power demand data of each semiconductor manufacturing equipment is given.

[0014] Furthermore, the system acquires time-series power demand data for each semiconductor manufacturing device from the equipment power consumption module, and simulates pipeline network operation behavior based on the pipeline network layout to provide time-series pipeline network operation status data, specifically including: Obtain the time-series power demand data for each semiconductor manufacturing equipment provided by the equipment power consumption module; Based on a pre-built pipeline network model, combined with the time-series power demand data of various semiconductor production equipment, the pipeline network operation behavior is simulated, and the power demand delivery data of the pipeline network is given. Based on the analysis of power demand data of the pipeline network and time-series power demand data of various semiconductor production equipment, time-series pipeline network operation status data is presented.

[0015] Furthermore, the construction of the pipeline network model specifically includes: Based on the pipeline network layout, establish the pipeline network topology; Obtain pipeline attribute information to generate pipeline configuration data; By integrating the pipeline network topology, pipeline network configuration data, and pipeline network hydraulic calculation module, an initial pipeline network model is constructed. Based on the processing attribute information of each semiconductor production equipment, the location information of the power demand of each semiconductor production equipment is given; Based on the power demand location information of each semiconductor production equipment, the corresponding connection location information of the pipeline network is given, the connection points are determined, and a mapping relationship is formed between the connection points, semiconductor production equipment and the power demand location information of semiconductor production equipment. A pipeline network model is constructed by combining the initial pipeline network model with the mapping relationship data.

[0016] Furthermore, the mapping relationship is specifically represented as follows:

[0017] Where Ф() is the mapping function, D i,x Let PO be the x-th power demand location for the i-th semiconductor manufacturing equipment, where x = 1, 2, ..., n, and n is the total number of power demand locations. j1 PO j2 PO jk These are the j1, j2, and jk connection points in the pipeline network, respectively.

[0018] Furthermore, the pipeline network layout is the overall arrangement scheme of the power system in geographic space. The pipeline attribute information includes pipeline size information, pipeline mechanical performance information, and valve and fitting performance information. The pipeline hydraulic calculation module is any one of PIPENET, EPANET, and Flowmaster. The processing attribute information is a set of systematic parameters of the power demand of semiconductor manufacturing equipment under different processes. The power demand transmission data includes power flow demand transmission data and power pressure demand transmission data.

[0019] Furthermore, based on a pre-built pipeline network model and combined with the time-series power demand data of various semiconductor production equipment, the pipeline network operation behavior is simulated to provide power demand delivery data for the pipeline network, specifically including: Based on the time-series power demand data of each semiconductor production equipment, a pipeline network model is matched to provide the location information of the power demand of each semiconductor production equipment. Based on the mapping relationship, the target connection points are determined, and combined with the time-series power flow demand data of each semiconductor production equipment, the power flow demand delivery data of each target connection point is determined. Using the power flow demand data of each target connection point, the inlet flow data of the pipeline network topology, and the inlet pressure data, the boundary conditions for simulating the operation behavior of the pipeline network are determined. By using a pipeline network model, the operation behavior of the pipeline network is simulated, and the power and pressure demand data of the target connection points are provided.

[0020] Furthermore, based on the analysis of power demand delivery data of the pipeline network and time-series power demand data of various semiconductor production equipment, time-series pipeline network operation status data is provided, specifically including: Based on the pipeline network model, taking into account pipeline segment losses and combining mapping relationships, a power demand transmission relationship function is formed; By integrating the power pressure demand delivery data and power demand delivery relationship function of the target connection point, the real-time power supply pressure data obtained by each semiconductor production equipment is given. Based on the comparative analysis of real-time power supply pressure data and time-series power pressure demand data obtained from various semiconductor production equipment, time-series pipeline network operation status data is presented.

[0021] Furthermore, based on the pipeline network model, taking into account pipeline segment losses and combining the mapping relationship, a power demand transmission relationship function is formed, specifically expressed as follows:

[0022] Among them, P i,t,x P represents the real-time power supply pressure data at time t for the x-th power demand location of the i-th semiconductor manufacturing equipment. j1,tTo transmit power pressure demand data for the j1th connection point in the pipeline network at time t, R j1→i,x Let Q be the pipeline resistance coefficient from the j1-th connection point in the pipeline network to the x-th power demand location of the i-th semiconductor manufacturing equipment. j1,t To transmit power flow demand data for the j1th connection point in the pipeline network at time t, P jk,t To transmit power pressure demand data for the jk-th connection point in the pipeline network at time t, R jk→i,x Let Q be the pipeline resistance coefficient from the jk-th connection point in the pipeline network to the x-th power demand location of the i-th semiconductor manufacturing equipment. jk,t To transmit data on the power flow demand of the jk-th connection point in the pipeline network at time t, Q i,x Q represents the power flow demand data for the x-th power demand location of the i-th semiconductor manufacturing equipment. j To transmit power flow demand data for the j-th connection point in the pipeline network, PO j Let Ф(D) be the j-th connection point in the pipeline network. i,x Let f(x) be the set of connection points that have a mapping relationship with the x-th power demand location of the i-th semiconductor manufacturing equipment.

[0023] Furthermore, it also includes a risk warning module, which is used to obtain time-series pipeline operation status data given by the pipeline supply module and output risk warning signals.

[0024] Secondly, the present invention also provides a semiconductor factory power demand simulation method considering scheduling and supply, employing a semiconductor factory power demand simulation apparatus considering scheduling and supply as described above, comprising: Obtain semiconductor production task information and provide the processing sequence for each semiconductor production equipment; Based on the processing sequence of each semiconductor manufacturing equipment, the processing behavior of each semiconductor manufacturing equipment is simulated, and the time-series power demand data of each semiconductor manufacturing equipment is given. Based on the time-series power demand data of various semiconductor production equipment, the operation behavior of the pipeline network is simulated, and based on the pipeline network layout and the power supply of the semiconductor factory's power system, time-series pipeline network operation status data is provided.

[0025] The present invention provides a semiconductor factory power demand simulation device and method that takes into account scheduling and supply, which has at least the following beneficial effects: By constructing a three-tiered power demand simulation model for semiconductor factories, encompassing "production scheduling, equipment energy consumption, and pipeline supply," the system enables accurate prediction of power demand changes during production plan execution, effective identification of peak superposition risks and pipeline transmission bottlenecks, and accurate prediction of the overall power demand of the semiconductor factory. This fundamentally solves the problem of the disconnect between power demand forecasting and planning in semiconductor factories. Attached Figure Description

[0026] Figure 1 A structural block diagram of a semiconductor factory power demand simulation device that takes into account scheduling and supply, provided in an embodiment of the present invention; Figure 2 A flowchart for determining the processing sequence provided in an embodiment of the present invention; Figure 3 A flowchart for determining time-series power demand data provided in an embodiment of the present invention; Figure 4 A schematic diagram of energy demand data provided in an embodiment of the present invention; Figure 5 A flowchart illustrating the simulated pipeline network operation behavior provided in this embodiment of the invention; Figure 6 A flowchart for constructing a pipeline network model provided in an embodiment of the present invention; Figure 7 A schematic diagram of the pipeline network model provided in an embodiment of the present invention; Figure 8 A flowchart providing power demand delivery data for a pipeline network, provided for embodiments of the present invention; Figure 9 A simplified structural diagram of the pipeline network model provided in an embodiment of the present invention; Figure 10 A flowchart providing time-series pipeline network operation status data is provided for embodiments of the present invention; Figure 11 A structural block diagram of a risk warning module provided for embodiments of the present invention; Figure 12 A flowchart of a semiconductor factory power demand simulation method that takes into account scheduling and supply, provided for an embodiment of the present invention.

[0027] Among them, 201 is the production scheduling module; 202 is the equipment energy consumption module; 203 is the pipeline supply module; and 204 is the risk warning module. Detailed Implementation

[0028] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0029] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0030] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0031] In semiconductor manufacturing settings, power supply systems encompass multiple categories, including electricity, pure water, specialty gases, bulk gases, and chemicals, serving as crucial support for ensuring production continuity and manufacturing yield. However, the industry currently lacks accurate methods for forecasting power demand and effective means for assessing the actual supply capacity of semiconductor factories, making it difficult to identify and mitigate potential supply risks in a timely manner. Specific problems include: (1) The dynamic matching problem between production planning and power demand forecasting. Semiconductor factories involve thousands of different types of production equipment and need to process multiple products simultaneously. When different production equipment processes different products, the type and timing of power demand vary significantly. Current forecasting methods mostly rely on historical monitoring data and cannot be dynamically adjusted according to product type, capacity target, process steps, equipment allocation plan, etc., resulting in a large deviation between forecast results and actual demand.

[0032] (2) Risk prediction problem of overlapping peak power demand of multiple devices. Current capacity assessment methods are usually based on historical averages or the rated demand of a single device, and fail to consider the effect of "overlapping peak power demand of multiple devices" caused by production scheduling. When multiple devices are operating at high load at the same time, the instantaneous energy demand of the system may exceed the predicted value, thereby causing a power supply shortage, which directly threatens the stable operation of the production line and product yield.

[0033] (3) Identification of local transmission bottlenecks in the power supply network. Semiconductor factories rely on complex pipeline systems for power medium transportation. During the transmission process, the medium experiences pressure loss, flow rate attenuation, and purity fluctuations. Due to significant differences in pipe diameter, topology, and the number of load devices in different areas of the pipeline network, when multiple devices in a certain area are simultaneously at peak energy consumption, even if the total supply capacity of the plant system meets the needs of the entire plant, transmission bottlenecks in the local pipeline network may still cause the power parameters of key equipment to fail to meet process requirements.

[0034] To address the aforementioned issues, this invention proposes a semiconductor factory power demand simulation device and method that considers scheduling and supply. The device includes a production scheduling module for acquiring semiconductor production task information and providing the processing sequence for each semiconductor production equipment; an equipment energy consumption module for acquiring the processing sequence for each semiconductor production equipment provided by the production scheduling module, simulating the processing behavior of each semiconductor production equipment, and providing time-series power demand data for each semiconductor production equipment; and a pipeline supply module for acquiring the time-series power demand data for each semiconductor production equipment provided by the equipment energy consumption module, and simulating pipeline operation behavior based on the pipeline layout, providing time-series pipeline operation status data.

[0035] By constructing a three-tiered power demand simulation model for semiconductor factories, encompassing production scheduling, equipment energy consumption, and pipeline supply, this model enables accurate prediction of power demand changes during production plan execution, effective identification of peak load risks and pipeline transmission bottlenecks, and precise prediction of the entire semiconductor factory's power demand. This fundamentally solves the problem of the disconnect between power demand forecasting and planning in semiconductor factories. While deeply integrated with production planning, it also fully considers pipeline transmission characteristics, providing crucial support for proactive early warning and intelligent management of the semiconductor factory's power system. This is of significant strategic importance for ensuring the efficient and stable operation of semiconductor manufacturing.

[0036] like Figure 1 As shown, this embodiment of the invention provides a semiconductor factory power demand simulation device that considers scheduling and supply. Specifically, it includes a production scheduling module, an equipment energy consumption module, and a pipeline supply module. Specifically, the production scheduling module acquires semiconductor production task information and provides the processing sequence for each semiconductor production equipment; the equipment energy consumption module acquires the processing sequence for each semiconductor production equipment provided by the production scheduling module, simulates the processing behavior of each semiconductor production equipment, and provides time-series power demand data for each semiconductor production equipment; the pipeline supply module acquires the time-series power demand data for each semiconductor production equipment provided by the equipment energy consumption module, and simulates pipeline operation behavior based on the pipeline layout, providing time-series pipeline operation status data.

[0037] The production task information includes production plan information and processing flow information; the processing sequence is a time-sequential processing operation instruction sequence for specific processes of semiconductors; the power demand data includes power flow demand data and power pressure demand data; and the operating status data includes normal or abnormal status.

[0038] As can be understood, production planning information includes the products that the semiconductor factory needs to produce and the corresponding production capacity for each product. Specific production planning information can be broken down to the products and production capacity required for each semiconductor manufacturing unit within the factory. Processing flow information describes the sequence of processing steps for each product to be produced, which can also be understood as the sequence of semiconductor manufacturing equipment that the product must pass through during production. Power flow demand data represents the power demand for flow rate during the operation of each semiconductor manufacturing unit, while power pressure demand data represents the power demand for pressure during the operation of each semiconductor manufacturing unit.

[0039] In one specific implementation, the production scheduling module generates a processing sequence for each semiconductor production equipment based on specified semiconductor production task information, including production plans for various products and capacities, to achieve sequential control of each semiconductor production equipment to complete the corresponding processing tasks. The equipment energy consumption module simulates the processing behavior of each semiconductor production equipment based on the processing steps, obtaining time-series power demand data generated by the semiconductor production equipment during operation. In this example, the output time-series power demand data is a power demand time-series diagram; other methods can be used in other examples, and this is not limited. The time-series power demand data, including the power demand of each semiconductor production equipment at each moment, is input into the pipeline supply module. The pipeline supply module simulates pipeline operation behavior, provides power demand delivery data for the pipeline, and then compares and analyzes it with the time-series power demand data of each semiconductor production equipment through mapping relationships to provide time-series pipeline operation status data.

[0040] Furthermore, semiconductor production task information is obtained, and the processing sequence of each semiconductor production equipment is provided, specifically including: Standardize the production planning information to provide the target production volume for each type of semiconductor during the specified time period; Based on the processing flow information of various types of semiconductors, combined with randomized logistics transmission time and production status information of each semiconductor manufacturing equipment, the processing sequence of each semiconductor manufacturing equipment is given. The production status information generally indicates an idle or occupied state.

[0041] In a specific example, the production plan information represents the monthly capacity of different products. Standardizing this production plan information converts the monthly capacity into the target production capacity for generating processing sequences. Generally, the target production capacity can be the daily production capacity for different semiconductor products, using an average production method: dividing the monthly capacity by 30 days and rounding up to obtain the daily production capacity (target production capacity). The daily production capacity is specifically expressed as follows:

[0042] Among them, D foupin,A M represents the daily production output of semiconductor product A. foupin,A Let be the monthly production output of semiconductor product A, and [·] be the function for rounding up.

[0043] Specifically, based on the processing flow information of various types of semiconductors, combined with randomized logistics transmission time and production status information of each semiconductor manufacturing equipment, the processing sequence of each semiconductor manufacturing equipment is given, referring to... Figure 2 Specifically, it includes: The processing flow information of various types of semiconductors is mapped to the process information of each semiconductor manufacturing equipment; Based on the exponential distribution random generation rule, the logistics transmission time between each process is given; Based on the process information, determine the time trigger point, specify the target semiconductor production equipment, query the current status of the target semiconductor production equipment, and determine the processing time including queuing time; By integrating processing time and logistics transportation time, the processing sequence of each semiconductor manufacturing equipment is given.

[0044] Among them, the logistics transmission time between each process is given by combining the exponential distribution random generation rule, specifically expressed as follows:

[0045] Among them, T trans,a Let λ be the material transport time required to enter the a-th process, be a constant, ln() be a logarithmic function, and U be the time required for material transport. a Let λ be the random number generated based on an exponential distribution when entering the a-th process; in this example, λ = 0.2min. -1 U a These are random numbers uniformly distributed within the interval (0,1) in the generation of an exponentially distributed random variable. A random number closer to 0 indicates a shorter logistics delivery time, while a random number closer to 1 indicates a longer logistics delivery time. By using the exponential distribution random generation rule, the uncertain logistics delay phenomenon in actual semiconductor processing can be simulated.

[0046] In one specific implementation, the processing flow information of various types of semiconductors is mapped to the process information of various semiconductor manufacturing equipment. The processing flow information of different types of semiconductors includes the equipment sequence of various semiconductor manufacturing equipment that various semiconductor products need to go through and the initial processing time corresponding to each semiconductor manufacturing equipment. Based on the process information, the time trigger point of the semiconductor product, i.e. the process start time, is determined, and the semiconductor product is included in the processing queue.

[0047] The system queries the current status of the semiconductor manufacturing equipment required for the first processing step in the current semiconductor product's process information. If the equipment is currently occupied, it waits based on the longer of the generated logistics transmission time and the remaining processing time. If the equipment is currently idle, it waits based on the generated logistics transmission time. Then, the corresponding action for the current semiconductor product is directly added to the semiconductor manufacturing equipment's processing sequence. In a specific example, there is a process information entry "0:00:00:30, Product A, Process 1," indicating that the semiconductor manufacturing equipment needs to process semiconductor product A at day 0, hour 0, minute 30, and second 0, and the semiconductor product is in process step 1.

[0048] Furthermore, the processing sequences of each semiconductor manufacturing equipment are obtained from the production scheduling module, the processing behavior of each semiconductor manufacturing equipment is simulated, and the time-series power demand data of each semiconductor manufacturing equipment is provided, with reference to... Figure 3 Specifically, it includes: Determine the baseline operating requirements of semiconductor manufacturing equipment, as well as the energy requirements of various types of semiconductors at different processes in various semiconductor manufacturing equipment; Based on the screening of processing moments in the processing sequence, the energy demand data of semiconductor manufacturing equipment is spliced ​​together to provide initial time-series power demand data; Based on the filtering of idle moments in the processing sequence, and by integrating the initial time-series power demand data and the baseline operating demand data, the time-series power demand data of each semiconductor manufacturing equipment is given.

[0049] In one specific implementation, the equipment energy module pre-stores the baseline operating demand data of each semiconductor production equipment in the non-processing state, as well as the corresponding energy demand data in the processing state of different semiconductor products and different processes. The energy demand data in the processing state is determined by the baseline operating demand data and the attributes of each semiconductor production equipment itself. The energy demand data in the processing state is related to the type of semiconductor production equipment, semiconductor product, and processing process. Different semiconductor products require different energy demand data, which includes power demand and demand time.

[0050] In a specific example, refer to Figure 4The power demand and demand time in the energy demand data can be represented by a stepped curve. The baseline operating demand data under non-processing conditions is used to maintain the semiconductor manufacturing equipment in a good processing state, cyclically using set values. Based on the obtained processing sequence, energy consumption simulation is performed on the semiconductor manufacturing equipment. First, based on the processing times in the processing sequence, the energy demand data of the semiconductor manufacturing equipment under processing conditions is spliced ​​together according to the processing times to obtain initial time-series power demand data. Then, based on the non-processing state of the semiconductor manufacturing equipment, the baseline operating demand data is determined, and the initial time-series power demand data is merged to finally obtain the time-series power demand data for each semiconductor manufacturing equipment.

[0051] By establishing a direct link between production planning and power demand, it is possible to accurately generate equipment-level power demand time-series curves based on dynamic parameters such as product type, process formula, and equipment scheduling. This fundamentally solves the forecasting lag problem caused by traditional reliance on historical statistical data, and enables forward-looking and accurate forecasting of power demand.

[0052] The system acquires time-series power demand data for each semiconductor manufacturing device from the equipment power consumption module, and simulates pipeline network operation behavior based on the pipeline network layout to provide time-series pipeline network operation status data. Figure 5 Specifically, it includes: Obtain the time-series power demand data for each semiconductor manufacturing equipment provided by the equipment power consumption module; Based on a pre-built pipeline network model, combined with the time-series power demand data of various semiconductor production equipment, the pipeline network operation behavior is simulated, and the power demand delivery data of the pipeline network is given. Based on the analysis of power demand data of the pipeline network and time-series power demand data of various semiconductor production equipment, time-series pipeline network operation status data is presented.

[0053] In one specific implementation, the pipeline supply module inputs the time-series power demand data of each semiconductor production equipment into a pre-built pipeline model. Based on the pipeline topology (such as pipeline routing) and the connection relationship of each semiconductor production equipment in the pipeline model, it simulates the operation process of the pipeline at various times to obtain the power demand delivery data of the pipeline at the corresponding time. Combining the mapping relationship, it analyzes and judges the time-series power demand data of each semiconductor production equipment to obtain the time-series pipeline operation status data.

[0054] It's important to understand that semiconductor factories employ a centralized pipeline power supply system, providing power to all semiconductor production equipment within the area. Changes in the power demand data of these equipment directly impact the operation of the pipeline network. This includes factors such as flow velocity in different sections of the network and pressure at various connection points. In a specific example, the time-series pipeline operation status data might show a very low pressure value in the current power demand data. Based on the mapping relationship, this pressure value cannot meet the time-series power demand data of each semiconductor production device, resulting in an abnormal status.

[0055] By incorporating pipeline power demand simulation into the semiconductor factory simulation system, and establishing a pipeline model that includes pipeline attribute information, pipeline topology, and pipeline hydraulic calculation modules, the power demand transmission data of the pipeline can be quantified. Combined with mapping relationships, based on the analysis of the power demand transmission data of the pipeline, the time-series power demand data of each semiconductor production equipment, and the pipeline configuration data, time-series pipeline operation status data can be provided. This allows for the identification of local supply bottlenecks caused by pipeline transmission capacity limitations, effectively avoiding the risk of insufficient power supply to semiconductor production equipment.

[0056] Furthermore, the construction of the pipeline network model refers to... Figure 6 Specifically, it includes: Based on the pipeline network layout, establish the pipeline network topology; Obtain pipeline attribute information to generate pipeline configuration data; By integrating the pipeline network topology, pipeline network configuration data, and pipeline network hydraulic calculation module, an initial pipeline network model is constructed. Based on the processing attribute information of each semiconductor production equipment, the location information of the power demand of each semiconductor production equipment is given; Based on the power demand location information of each semiconductor production equipment, the corresponding connection location information of the pipeline network is given, the connection points are determined, and a mapping relationship is formed between the connection points, semiconductor production equipment and the power demand location information of semiconductor production equipment. A pipeline network model is constructed by combining the initial pipeline network model with the mapping relationship data.

[0057] Specifically, the pipeline network layout is the overall arrangement plan of the power system in geographical space. The pipeline attribute information includes pipeline size information, pipeline mechanical performance information and valve and fitting performance information. The pipeline hydraulic calculation module is any one of PIPENET, EPANET and Flowmaster. The processing attribute information is a set of systematic parameters of the power demand of semiconductor production equipment under different processes. The power demand transmission data includes power flow demand transmission data and power pressure demand transmission data.

[0058] A pipeline network is a system composed of interconnected pipes, fittings, valves, pumping stations, control devices, etc. The pipeline network layout is the overall spatial arrangement of the power system, that is, the connection relationships between components such as pipes, fittings, valves, pumping stations, and control devices, and the establishment of the pipeline network topology based on these connections. Pipeline attribute information includes pipe size information (e.g., pipe length, pipe height, pipe outer diameter, pipe wall thickness), pipe mechanical performance information (e.g., pipe material, pipe roughness), and valve and fitting performance information (e.g., valve type, valve flow coefficient, elbow resistance coefficient, tee resistance coefficient, reducer resistance coefficient, weld resistance coefficient, etc.). The pipeline hydraulic calculation module can be any one of PIPENET, EPANET, or Flowmaster. It serves as the core calculation engine and professional analysis platform. Combining the pipeline topology and configuration data, the pipeline hydraulic calculation module can quickly calculate the pressure distribution and flow allocation of the fluid in the pipeline under specific operating conditions based on the input fluid data (such as inlet flow and pressure data of the pipeline topology and power flow demand data at specific points).

[0059] Based on the processing attribute information of each semiconductor manufacturing equipment, the power demand location information of each piece of equipment is provided. The processing attribute information is a set of systematic parameters regarding the power demand of semiconductor manufacturing equipment at different processes. It is important to understand that for the same semiconductor manufacturing equipment, the required power demand differs in different processing steps for different semiconductor products. The processing attribute information corresponds to the processing step, and from this information, the type of power demand required for the current processing step and its corresponding location information can be obtained. For example, if the current processing step requires pure water, the corresponding power demand location information is the connection location information in the pipeline network that can provide pure water. Based on the power demand location information of each semiconductor manufacturing equipment, the corresponding connection location information in the pipeline network is obtained, the connection points are determined, and a mapping relationship is formed between the connection points, the semiconductor manufacturing equipment, and the power demand location information of the semiconductor manufacturing equipment. That is, a mapping relationship is established between the semiconductor manufacturing equipment and the connection points in the pipeline network. Combined with the initial pipeline network model, a pipeline network model is obtained. Figure 7 As shown, EQP1, EQP2, EQP3, EQP4, EQP5, and EQP6 are semiconductor manufacturing equipment. Based on the processing attribute information, the power demand locations of each semiconductor manufacturing equipment and the connection points are determined, thereby realizing the mapping between the semiconductor manufacturing equipment and the connection points in the pipeline network.

[0060] Furthermore, the mapping relationship between connection points, semiconductor manufacturing equipment, and the location information of the power demand of semiconductor manufacturing equipment is specifically represented as follows:

[0061] Where Ф() is the mapping function, D i,x Let PO be the x-th power demand location for the i-th semiconductor manufacturing equipment, where x = 1, 2, ..., n, and n is the total number of power demand locations. j1 PO j2 PO jk These are the j1, j2, and jk connection points in the pipeline network, respectively.

[0062] Understandably, each semiconductor manufacturing equipment requires multiple power supply points, and each power supply point is supplied by multiple connection points in the pipeline network. Through mapping, it can be ensured that the power needs of each semiconductor manufacturing piece of equipment are precisely matched to the corresponding connection points in the pipeline network.

[0063] Furthermore, based on a pre-built pipeline network model and combined with the time-series power demand data of various semiconductor production equipment, the pipeline network operation behavior is simulated, providing power demand delivery data for the pipeline network, with reference to... Figure 8 Specifically, it includes: Based on the time-series power demand data of each semiconductor production equipment, a pipeline network model is matched to provide the location information of the power demand of each semiconductor production equipment. Based on the mapping relationship, the target connection points are determined, and combined with the time-series power flow demand data of each semiconductor production equipment, the power flow demand delivery data of each target connection point is determined. Using the power flow demand data of each target connection point, the inlet flow data of the pipeline network topology, and the inlet pressure data, the boundary conditions for simulating the operation behavior of the pipeline network are determined. By using a pipeline network model, the operation behavior of the pipeline network is simulated, and the power and pressure demand data of the target connection points are provided.

[0064] In one specific implementation, firstly, based on the time-series power demand data of each semiconductor production equipment, power demand is matched from the pipeline network model to obtain the power demand location information of each semiconductor production equipment. Simultaneously, combined with mapping relationships, the target connection points corresponding to the power demand location information are determined. Then, combined with the time-series power flow demand data of each semiconductor production equipment, the power flow demand delivery data for each target connection point is determined. Finally, using the power flow demand delivery data for each target connection point, the inlet flow data of the pipeline network topology, and the inlet pressure data, the boundary conditions for simulating the pipeline network operation behavior are determined; through the pipeline network model, the pipeline network operation behavior is simulated, and the power pressure demand delivery data for the target connection points is provided.

[0065] like Figure 9As shown, boundary conditions are set for the pipeline network model, namely, providing the power flow demand data of the target connection point, the inlet flow data and inlet pressure data of the pipeline network topology, with the pipeline network topology inlet as the pressure source, the target connection point as the flow sink, and the pipeline network topology return port as the pressure sink. The simulation calculation of the pipeline network model is then performed to provide the pressure of each node in the pipeline network and the flow of each pipe segment, including the power and pressure demand data of the target connection point.

[0066] like Figure 10 As shown, based on the analysis of power demand delivery data of the pipeline network and the time-series power demand data of various semiconductor production equipment, the time-series pipeline network operation status data is presented, specifically including: Based on the pipeline network model, taking into account pipeline segment losses and combining mapping relationships, a power demand transmission relationship function is formed; By integrating the power pressure demand delivery data and power demand delivery relationship function of the target connection point, the real-time power supply pressure data obtained by each semiconductor production equipment is given. Based on the comparative analysis of real-time power supply pressure data and time-series power pressure demand data obtained from various semiconductor production equipment, time-series pipeline network operation status data is presented.

[0067] Regarding the acquisition of time-series pipeline operation status data, by constructing a power demand transmission relationship function that takes into account pipeline segment losses, the time-series accurate mapping and dynamic loss compensation of pressure from pipeline connection points to semiconductor production equipment demand locations can be achieved. This supports real-time prediction and evaluation of pipeline operation status, root cause diagnosis, and scheduling optimization, realizing a technological leap from "passive response" to "active prediction" in semiconductor factory pipeline systems.

[0068] Furthermore, based on the pipeline network model, taking into account pipeline segment losses and combining the mapping relationship, a power demand transmission relationship function is formed, specifically expressed as follows:

[0069] Among them, P i,t,x P represents the real-time power supply pressure data at time t for the x-th power demand location of the i-th semiconductor manufacturing equipment. j1,t To transmit power pressure demand data for the j1th connection point in the pipeline network at time t, R j1→i,x Let Q be the pipeline resistance coefficient from the j1-th connection point in the pipeline network to the x-th power demand location of the i-th semiconductor manufacturing equipment. j1,t To transmit power flow demand data for the j1th connection point in the pipeline network at time t, P jk,t To transmit power pressure demand data for the jk-th connection point in the pipeline network at time t, R jk→i,xLet Q be the pipeline resistance coefficient from the jk-th connection point in the pipeline network to the x-th power demand location of the i-th semiconductor manufacturing equipment. jk,t To transmit data on the power flow demand of the jk-th connection point in the pipeline network at time t, Q i,x Q represents the power flow demand data for the x-th power demand location of the i-th semiconductor manufacturing equipment. j To transmit power flow demand data for the j-th connection point in the pipeline network, PO j Let Ф(D) be the j-th connection point in the pipeline network. i,x Let f(x) be the set of connection points that have a mapping relationship with the x-th power demand location of the i-th semiconductor manufacturing equipment.

[0070] The pressure loss from the connection point to the power demand location includes friction loss and local resistance, and the pressure loss from different connection points to the same power demand location is also different. The pressure loss from each connection point to the power demand location can be expressed as the product of the resistance coefficient and the square of the flow rate of the corresponding pipe segment. The resistance coefficient can be pre-calculated. In one embodiment, the resistance coefficient can be calculated comprehensively based on the Darcy-Weisbach formula, that is, by using parameters such as the dynamic fluid density, pipe friction coefficient, pipe length, pipe diameter, and local resistance coefficient, the friction loss and the local resistance of pipe fittings such as valves and elbows are calculated separately and then superimposed to obtain the pressure drop of the pipe segment. This achieves a quantitative characterization of the pressure drop of the pipe segment.

[0071] The power demand transmission relationship function is established by creating a loss compensation relationship between the pressure at the connection point in the pipeline network and the location of power demand (i.e., R). j1→i,x ·Q j1,t 2 R jk→i,x ·Q jk,t 2 This allows for direct deduction of pipe segment pressure drop when supplying at a single point, and forced pressure balance at the end of each branch when supplying multiple points in parallel, while also satisfying flow superposition (i.e., Q). j (The superposition of data) allows for accurate prediction of the actual available pressure at each power demand location of each device. This is then compared and analyzed with time-series power pressure demand data to provide a calculable and verifiable quantitative benchmark for pipeline supply compliance assessment, fault diagnosis, and dynamic scheduling.

[0072] Reference Figure 11 In one specific implementation, the semiconductor factory power demand simulation device that takes into account scheduling and supply also includes a risk warning module. The risk warning module is used to acquire the time-series pipeline operation status data given by the pipeline supply module and output a risk warning signal.

[0073] In a specific example, the pipeline supply module simulates the operation of the pipeline network, calculates the supply pressure of each semiconductor manufacturing device, and provides time-series pipeline operation status data. If the supply pressure of a semiconductor manufacturing device does not meet the rated requirements in the time-series pipeline operation status data, a risk warning signal is issued through the risk warning module. Specifically, after the pipeline network model is constructed, the time-series power demand data of each semiconductor manufacturing device at different times is input into the pipeline network model to calculate the pressure at each machine connection point. If the supply pressure of a semiconductor manufacturing device is lower than the minimum pressure requirement, a risk warning signal is issued through the risk warning module.

[0074] By establishing a risk warning module, a comprehensive supply risk warning mechanism is created. This mechanism can identify power supply gaps and potential risks before or during project execution, providing crucial technical support for proactive management and intelligent decision-making in semiconductor factory power systems. This ensures production continuity and product yield. Simultaneously, a multi-dimensional risk warning mechanism is established to issue early warning signals when the plant's supply capacity is found to be insufficient to meet demand. This effectively avoids product losses due to power supply issues and guarantees the continuous and stable production of the semiconductor factory.

[0075] This invention generates processing sequences for each semiconductor production equipment based on production planning information, and then simulates the real-time power demand of different semiconductor production equipment in the process of performing specific tasks. The power demand data of all semiconductor production equipment in the semiconductor factory at the same time are input into the pipeline simulation model. Finally, based on the model output results, it is determined whether the actual supply capacity of the plant system meets the equipment demand, thus completing the construction of a multi-level dynamic simulation system that is deeply coupled with the production plan.

[0076] This invention incorporates pipeline power demand simulation into a semiconductor factory simulation system. By establishing a pipeline model that includes pipeline attribute information, pipeline topology, and pipeline hydraulic calculation modules, it can quantify the pipeline's power demand transmission data. Combined with mapping relationships, and based on the analysis of the pipeline's power demand transmission data, the time-series power demand data of each semiconductor production equipment, and pipeline configuration data, it provides time-series pipeline operation status data. This allows for the identification of local supply bottlenecks caused by pipeline transmission limitations during system design or production scheduling, achieving end-to-end supply reliability verification from "source supply" to "end-point equipment." Furthermore, it employs a multi-dimensional power supply risk assessment mechanism: ① Total volume risk assessment: Based on production task allocation and equipment energy consumption, it determines whether the total power demand of the entire plant or region exceeds the system supply threshold; ② Peak value superposition risk assessment: Through time-series superposition analysis of the power demand curves of all equipment, it identifies whether the system-level power demand peak exceeds the system's instantaneous supply capacity; ③ Pipeline transmission risk assessment: By introducing a pipeline pressure loss and flow attenuation calculation model, it assesses whether the power parameters at the end of the pipeline at any given time can still meet the process requirements of all equipment. By introducing a time-series superposition algorithm for the power demand of all equipment in the plant, it is possible to accurately identify system-level peak demand caused by the parallel operation of multiple devices and effectively warn of the risk of supply shortage caused by the superposition of instantaneous loads.

[0077] Reference Figure 12 This invention provides a method for simulating the power demand of a semiconductor factory that takes into account scheduling and supply. The specific steps are as follows: Obtain semiconductor production task information and provide the processing sequence for each semiconductor production equipment; Based on the processing sequence of each semiconductor manufacturing equipment, the processing behavior of each semiconductor manufacturing equipment is simulated, and the time-series power demand data of each semiconductor manufacturing equipment is given. Based on the time-series power demand data of various semiconductor production equipment, the operation behavior of the pipeline network is simulated, and based on the pipeline network layout and the power supply of the semiconductor factory's power system, time-series pipeline network operation status data is provided.

[0078] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the described steps can be referred to the corresponding process in the foregoing device embodiments, and will not be repeated here.

[0079] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and variations of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A semiconductor factory power demand simulation device that takes into account scheduling and supply, characterized by, Specifically comprising: A production scheduling module, which acquires semiconductor production task information and gives a processing sequence of each semiconductor production device; A device energy consumption module, which acquires the processing sequence of each semiconductor production device given by the production scheduling module, simulates the processing behavior of each semiconductor production device, and gives time-sequenced power demand data of each semiconductor production device; A pipe network supply module, which acquires the time-sequenced power demand data of each semiconductor production device given by the device energy consumption module, simulates the pipe network operation behavior according to the pipe network layout, and gives time-sequenced pipe network operation state data.

2. The apparatus for simulating power demand of a semiconductor factory considering scheduling and supply of claim 1, wherein, The production task information includes production plan information and processing flow information, the acquisition of semiconductor production task information and the giving of a processing sequence of each semiconductor production device specifically comprises: Standardizing the production plan information to give a target period production quantity of each type of semiconductor; According to the processing flow information of each type of semiconductor, combining the randomized logistics transmission time and the production state information of each semiconductor production device, the processing sequence of each semiconductor production device is given.

3. The apparatus for simulating power demand of a semiconductor factory considering scheduling and supply of claim 2, wherein, The production state information is an idle state or an occupied state, according to the processing flow information of each type of semiconductor, combining the randomized logistics transmission time and the production state information of each semiconductor production device, the processing sequence of each semiconductor production device is given, specifically comprising: Mapping the processing flow information of each type of semiconductor to the process information of each semiconductor production device; Combining the exponential distribution random generation rule, the logistics transmission time between each process is given; According to the process information, the time trigger point is determined, the target semiconductor production device is given, and the current state of the target semiconductor production device is queried to determine the processing time including the queuing time; Fusion processing time, logistics transmission time, give the processing sequence of each semiconductor production device.

4. The apparatus for simulating power demand of a semiconductor factory considering scheduling and supply of claim 1, wherein, Acquiring the processing sequence of each semiconductor production device given by the production scheduling module, simulating the processing behavior of each semiconductor production device, and giving time-sequenced power demand data of each semiconductor production device, specifically comprising: Determine the baseline operation demand data of the semiconductor production device, and the energy demand data of each type of semiconductor at different processes of each semiconductor production device; Based on the screening of the processing time in the processing sequence, the energy demand data of the semiconductor production device is spliced to give the initial time-sequenced power demand data; Based on the screening of the idle time in the processing sequence, the initial time-sequenced power demand data and the baseline operation demand data are fused to give the time-sequenced power demand data of each semiconductor production device.

5. The apparatus for simulating power demand of a semiconductor factory considering scheduling and supply of claim 1, wherein, Acquiring the time-sequenced power demand data of each semiconductor production device given by the device energy consumption module, and simulating the pipe network operation behavior according to the pipe network layout to give time-sequenced pipe network operation state data, specifically comprising: Acquiring the time-sequenced power demand data of each semiconductor production device given by the device energy consumption module; Based on the pre-constructed pipe network model, combining the time-sequenced power demand data of each semiconductor production device, simulating the pipe network operation behavior, and giving the power demand delivery data of the pipe network; Based on the analysis of the power demand delivery data of the pipe network and the time-sequenced power demand data of each semiconductor production equipment, time-sequenced pipe network operation state data is given.

6. The apparatus for simulating power demand of a semiconductor factory considering scheduling and supply of claim 5, wherein, The construction of the pipe network model specifically includes: Based on the pipe network layout, a pipe network topology is established; Pipe network pipeline attribute information is obtained to form pipe network configuration data; The initial pipe network model is constructed by fusing the pipe network topology, the pipe network configuration data, and a pipe network hydraulic calculation module; Based on the power demand location information of each semiconductor production equipment, corresponding connection location information of the pipe network is given to determine the connection points and form a mapping relationship of the connection points, the semiconductor production equipment, and the power demand location information of the semiconductor production equipment; The pipe network model is constructed by combining the initial pipe network model and the mapping relationship. Based on the pre-constructed pipe network model, the time-sequenced power demand data of each semiconductor production equipment is combined to simulate the pipe network operation behavior, and the power demand delivery data of the pipe network is given, specifically including:

7. The apparatus for simulating power demand of a semiconductor factory considering scheduling and supply of claim 6, wherein, Based on the time-sequenced power demand data of each semiconductor production equipment, the power demand location information of each semiconductor production equipment is matched with the pipe network model; Based on the mapping relationship, the target connection points are determined, and the power flow demand delivery data of each target connection point is determined in combination with the time-sequenced power flow demand data of each semiconductor production equipment; The boundary conditions for simulating the pipe network operation behavior are determined based on the power flow demand delivery data of each target connection point, the pipe network topology import flow data, and the import pressure data; The power pressure demand delivery data of the target connection points is given by simulating the pipe network operation behavior through the pipe network model. Based on the analysis of the power demand delivery data of the pipe network and the time-sequenced power demand data of each semiconductor production equipment, time-sequenced pipe network operation state data is given, specifically including:

8. The apparatus for simulating power demand of a semiconductor factory considering scheduling and supply of claim 7, wherein, Based on the pipe network model, the pipe network segment loss is considered, and in combination with the mapping relationship, a power demand delivery relationship function is formed; In combination with the power pressure demand delivery data of the target connection points and the power demand delivery relationship function, real-time power supply pressure data obtained by each semiconductor production equipment is given; Based on the comparative analysis of the real-time power supply pressure data obtained by each semiconductor production equipment and the time-sequenced power pressure demand data, time-sequenced pipe network operation state data is given. It also includes a risk early warning module, which is used to obtain the time-sequenced pipe network operation state data given by the pipe network supply module and output a risk early warning signal.

9. The apparatus of claim 1, wherein the scheduling and supply are considered in the simulation of the power demand of the semiconductor factory. The semiconductor factory power demand simulation device considering scheduling and supply according to any one of claims 1-9 specifically includes the following steps:

10. A method of semiconductor factory power demand simulation accounting for dispatch and supply, the method comprising: determining a power demand of a semiconductor factory; determining a power supply of the semiconductor factory; determining a power balance of the semiconductor factory; and determining a power balance error of the semiconductor factory. Obtain semiconductor production task information to give the processing sequence of each semiconductor production equipment; Based on the processing sequence of each semiconductor production equipment, the processing behavior of each semiconductor production equipment is simulated to give the time-sequenced power demand data of each semiconductor production equipment; ​ According to the time-sequenced power demand data of various semiconductor production equipment, the pipe network operation behavior is simulated, and time-sequenced pipe network operation state data is given according to the pipe network layout and power supply of the power system of the semiconductor factory.

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