Advanced process control systems, methods, apparatus, and media for semiconductor manufacturing

Abstract

The application discloses a kind of advanced process control systems, methods, equipment and media for semiconductor manufacturing.The system uses modular architecture, aims to improve the flexibility of advanced process control in complex production scenarios.The system includes multiple core modules: request receiving module is responsible for receiving process parameter adjustment request;Protocol analysis module converts request into internal protocol;Workflow preprocessing module carries out data preprocessing and integrates device state, production batch and other context information.Rule engine module executes logical judgment through its configurable rule search, rule execution and script plug-in unit.Workflow judgment module determines whether the workflow is ended according to the preset process parameter threshold or time threshold.Result aggregation module and response return module finally return the processing result to equipment automation program (EAP).The system effectively enhances the ability of workflow to cope with complex production conditions through modular design and integration of intelligent rule engine.

Description

Technical Field

[0001] This invention belongs to the field of advanced process control technology in semiconductor manufacturing, and particularly relates to an advanced process control system, method, equipment and medium for semiconductor manufacturing. Background Technology

[0002] In the semiconductor manufacturing industry, advanced process control (APC) systems are crucial for improving chip yield and production efficiency. Traditional APC systems typically rely on pre-defined, fixed algorithms and processes to adjust and control process parameters. These systems implement workflows through hard-coding and lack adaptive mechanisms. In existing technologies, APC workflows often exhibit insufficient flexibility when dealing with complex and changing production scenarios, such as equipment status fluctuations or production batch differences. They cannot dynamically adjust rules or integrate real-time contextual data, resulting in low processing efficiency and excessive reliance on manual intervention. Therefore, the main problem with existing technologies is the poor flexibility of APC workflows in complex production scenarios, which limits the system's adaptability and overall performance optimization in semiconductor manufacturing processes. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an advanced process control system, method, equipment and medium for semiconductor manufacturing, which addresses the shortcomings of the prior art by integrating a modular architecture and an intelligent rule engine. This effectively solves the main problem of poor flexibility of APC workflow in the prior art when facing complex production scenarios.

[0004] The first aspect of this invention discloses an advanced process control system for semiconductor manufacturing, comprising:

[0005] The request receiving module is used to receive process parameter adjustment requests from the equipment automation program;

[0006] The protocol parsing module, connected to the request receiving module, is used to convert the request into the internal communication protocol format of the advanced process control system;

[0007] The workflow preprocessing module is connected to the protocol parsing module and is used to preprocess the parsed data and integrate context data, including device status and production batch information.

[0008] The rules engine module, connected to the workflow preprocessing module, is used to execute rule configuration;

[0009] The workflow judgment module is connected to the rule engine module and is used to determine whether the workflow has ended based on predefined conditions, including process parameter thresholds or time thresholds.

[0010] The result aggregation module is connected to the workflow judgment module and is used to aggregate the execution results of all rules.

[0011] The response return module is connected to the result aggregation module and is used to return the aggregation results to the device automation program.

[0012] The rule engine module in the above system further includes:

[0013] The translator submodule is used to translate user-configured rules into expression language expressions;

[0014] The builder submodule is used to build runtime instances of the rules engine;

[0015] The parser submodule is used to parse expression language expression instances;

[0016] The executor submodule is used to execute rule logic;

[0017] The context manager submodule is used to maintain data containers for requests and responses.

[0018] The system also includes a rule configuration module, which allows users to edit rules through a user interface and persist them in a database.

[0019] In the above system, the workflow judgment module includes an iterative control unit, which is used to search and process rules iteratively until the predefined conditions are met.

[0020] The rule engine module in the above system includes a rule lookup unit, a rule execution unit, and a script plugin unit;

[0021] The rule lookup unit is used to retrieve matching rules from the rule base;

[0022] The rule execution unit is used to calculate the rule logic;

[0023] The script plugin unit is used to dynamically load external scripts.

[0024] The above system's script plugin unit supports multiple scripting languages, including JavaScript or Python, for dynamically expanding logical functions at runtime.

[0025] In the above system, the rule execution unit includes a formula calculation module, which uses the formula R=f(P,C) for calculation, where:

[0026] R represents the result of rule execution;

[0027] P represents process parameters, including temperature, pressure, or voltage;

[0028] C represents a context variable, including equipment status or production batch information;

[0029] f represents a rule-based logic function.

[0030] The actuator submodule of the above system includes:

[0031] The logical judgment unit is used to determine whether a rule contains custom logic.

[0032] Custom logic execution unit, used to execute via script plugin unit when custom logic exists;

[0033] The post-logic execution unit is used to directly execute post-logic when there is no user-defined logic.

[0034] A second aspect of this invention discloses an advanced process control method for semiconductor manufacturing, comprising the following steps:

[0035] S1: Receive process parameter adjustment requests from the equipment automation program;

[0036] S2: Convert the request into the internal communication protocol format of the advanced process control system;

[0037] S3: Preprocess the converted data and integrate the context data, which includes equipment status and production batch information;

[0038] S4: Execution rule configuration, which includes finding rules, executing rules, and processing custom logic through script plugins;

[0039] S5: Determine whether the workflow has ended based on predefined conditions, including process parameter thresholds or time thresholds; if the workflow has not ended, repeat S4 and S5 until the workflow ends; if the workflow ends, proceed to S6.

[0040] S6: Aggregate the execution results of all rules;

[0041] S7: Return the aggregation results to the device automation program.

[0042] A third aspect of the present invention discloses an electronic device, comprising: a memory and a processor, wherein the processor and the memory are connected;

[0043] The memory is used to store programs;

[0044] The processor invokes a program stored in the memory to execute the method provided in the second aspect embodiment described above.

[0045] The fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a computer to perform the method provided in the second aspect embodiment.

[0046] Compared with the prior art, the present invention has the following advantages: By introducing core innovations such as the protocol parsing module and the workflow preprocessing module, the present invention effectively improves the system's adaptability, data processing efficiency and overall stability, thereby overcoming the shortcomings of traditional fixed algorithm dependence and excessive manual intervention.

[0047] Specifically, the protocol parsing module, as a crucial link in the connection request reception and internal processing, ensures rapid compatibility and standardized processing of different data sources by converting requests from the equipment automation program into the internal communication protocol format of the advanced process control system. This feature directly solves the adaptation delay problem caused by rigid protocols in existing technologies, enabling the system to respond to changing production requests in real time without relying on preset hard-coded processes, thereby enhancing the flexibility and scalability of the workflow. For example, in the semiconductor manufacturing process, when equipment status or production batches change, the protocol parsing module can dynamically adjust the data format, avoiding the hassle of manual reconfiguration required by traditional systems due to protocol incompatibility.

[0048] The workflow preprocessing module further integrates contextual data (such as equipment status and production batch information) to provide a dynamic data foundation for subsequent rule execution. This module preprocesses the parsed data, achieving the fusion of real-time environmental data, enabling the workflow to automatically adjust its processing logic based on specific production scenarios. Compared to the lack of intelligent data integration in existing technologies, the workflow preprocessing module significantly improves the system's context awareness and reduces flexibility bottlenecks caused by isolated data or processing delays. For example, during production batch switching, this module can quickly integrate information from the old and new batches, ensuring that the rule engine module executes rules based on accurate context, avoiding the frequent manual intervention required by traditional systems due to data disconnect.

[0049] Furthermore, the rule engine module (including the rule lookup unit, rule execution unit, and script plugin unit) works in conjunction with the aforementioned modules, further enhancing flexibility through custom logic and dynamic rule execution. The workflow judgment module automatically determines the workflow status based on predefined conditions (such as process parameter thresholds or time thresholds), while the result aggregation module and response return module ensure efficient result feedback, forming a closed-loop optimization. The combination of these features not only improves the response speed and reliability of the APC system but also reduces the frequency of main program changes through a modular architecture, indirectly enhancing system stability. Overall, the features of this invention, through core innovations such as protocol parsing and workflow preprocessing, realize the transformation of APC workflow from static to dynamic, thereby significantly improving chip yield and production efficiency in the semiconductor manufacturing process.

[0050] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0051] Figure 1 This is a system module diagram of Example 1.

[0052] Figure 2 This is a diagram showing the submodule composition of the rules engine module.

[0053] Figure 3 This is a flowchart of the method in Example 2. Detailed Implementation

[0054] Example 1

[0055] like Figure 1 As shown, an advanced process control system for semiconductor manufacturing includes:

[0056] The request receiving module is used to receive process parameter adjustment requests from the equipment automation program;

[0057] The protocol parsing module, connected to the request receiving module, is used to convert the request into the internal communication protocol format of the advanced process control system;

[0058] The workflow preprocessing module is connected to the protocol parsing module and is used to preprocess the parsed data and integrate context data, including device status and production batch information.

[0059] The rule engine module is connected to the workflow preprocessing module and is used to execute rule configuration. The rule engine module includes a rule lookup unit, a rule execution unit, and a script plugin unit.

[0060] The workflow judgment module is connected to the rule engine module and is used to determine whether the workflow has ended based on predefined conditions, including process parameter thresholds or time thresholds.

[0061] The result aggregation module is connected to the workflow judgment module and is used to aggregate the execution results of all rules.

[0062] The response return module is connected to the result aggregation module and is used to return the aggregation results to the device automation program.

[0063] It should be noted that during semiconductor manufacturing, the equipment automation program detects that the current temperature of the etching equipment deviates from the standard value. For example, excessively high temperatures may lead to chip defects. Therefore, it sends a process parameter adjustment request to the advanced process control system. The system processes this request through a modular workflow and ultimately returns the optimized result.

[0064] The request receiving module receives process parameter adjustment requests from the equipment automation program. In implementation, this module is deployed as a network service endpoint, continuously listening for data packets sent by the equipment automation program. When a request arrives, the module first verifies the integrity of the data format, such as checking if the JSON request contains necessary fields like the device identifier, parameter type, and value. By capturing and parsing requests in real time, the module ensures no data loss or corruption before forwarding it to the protocol parsing module. This proactive listening and verification mechanism achieves high real-time performance and reliability in request processing, avoiding production interruptions caused by dropped requests in traditional systems.

[0065] The protocol parsing module connects to the request receiving module and is used to convert requests into the internal communication protocol format of the advanced process control system. In implementation, the module integrates a parsing algorithm to identify external protocols such as JSON or XML and map them to an internal standard structure. For example, a temperature adjustment request sent by the equipment automation program contains temperature values ​​and timestamps. The protocol parsing module extracts these fields and converts them into internal data objects, adding protocol version and priority tags. The conversion process ensures data standardization and eliminates incompatibility issues between multiple protocols. Through unified protocol processing, the system integration is smoother and data processing efficiency is improved, reducing the parsing burden on subsequent modules.

[0066] The workflow preprocessing module connects to the protocol parsing module to preprocess the parsed data and integrate contextual data such as equipment status and production batch information. In practice, the module accesses the system database or cache to obtain real-time equipment operating status (e.g., idle or faulty) and production batch details (e.g., batch number and process specifications). Then, the module merges the parsed data with the contextual data into a unified dataset. For example, in a temperature adjustment scenario, it combines the equipment status "normal" with the batch's allowable temperature range to form a complete context. Through data integration and preprocessing, data completeness before workflow execution is achieved, reducing the risk of rule misjudgment and enhancing the system's adaptability to changing production environments.

[0067] The rules engine module connects to the workflow preprocessing module to execute rule configurations. It includes a rule lookup unit, a rule execution unit, and a script plugin unit. In implementation, the rule lookup unit retrieves matching rules from the rule library, such as querying predefined rule sets based on parameter types like temperature and device status. The rule execution unit calculates the rule logic, such as using formulas to determine if temperature deviation exceeds a threshold. The script plugin unit supports dynamically loading external scripts, such as Python scripts, to extend custom logic.

[0068] For example, the rule engine retrieves the rule "If the temperature deviation is greater than 5% and the equipment is normal, adjust the temperature to the standard value," the execution unit calculates the deviation value, and the script plugin unit can call the calibration script for fine-tuning. Through dynamic rule execution and script expansion, flexible workflow adjustments are achieved, allowing users to optimize processes without modifying the main program, significantly improving the system's automation and intelligence.

[0069] The workflow judgment module connects to the rule engine module to determine whether the workflow has ended based on predefined conditions such as process parameter thresholds or time thresholds. In implementation, the module monitors the rule execution results and compares them with thresholds; for example, it checks whether the temperature has stabilized within ±2% of the standard value for 10 seconds. If the condition is not met, the module triggers the rule engine to re-execute; if it is met, the workflow is marked as ended. Through iterative judgment and condition monitoring, closed-loop control of the workflow is achieved, avoiding over-adjustment or premature termination, and improving process accuracy and stability.

[0070] The results aggregation module connects to the workflow judgment module to aggregate the results of all rule executions. In implementation, the module collects output data from multiple rule executions, such as temperature adjustment records, execution counts, and status logs, and merges them into a structured summary report. For example, during temperature optimization, the aggregation module summarizes multiple fine-tuning results into the final adjustment value, total time, and success rate. Through data aggregation and summary generation, a global execution view is achieved, facilitating monitoring and subsequent optimization, and reducing the analytical difficulties caused by data dispersion.

[0071] The response return module connects to the result aggregation module and is used to return the aggregated results to the device automation program. In implementation, the module encapsulates the aggregated data into a device-recognizable response format, such as JSON, and sends it back to the device automation program via a network protocol. For example, the return packet includes the adjusted temperature value, execution status code, and suggested operation instructions. This standardized response return enables end-to-end automated communication, ensuring timely receipt of instructions by the device and improving production efficiency and system response speed.

[0072] like Figure 2 As shown, in one embodiment, the rule engine module further includes:

[0073] The translator submodule is used to translate user-configured rules into expression language expressions;

[0074] The builder submodule is used to build runtime instances of the rules engine;

[0075] The parser submodule is used to parse expression language expression instances;

[0076] The executor submodule is used to execute rule logic;

[0077] The context manager submodule is used to maintain data containers for requests and responses.

[0078] In implementation, for example in a temperature adjustment scenario, after the rule engine module receives data integrated by the workflow preprocessing module, the translator submodule first converts the user-configured rule text into an expression language expression. For instance, it translates the natural language rule "If the temperature exceeds the threshold, then reduce it by 5%" into a standard EL expression such as "if(temp>threshold) {temp = temp * 0.95}". The builder submodule then builds a running instance of the rule engine, dynamically loading expressions and dependency libraries to provide an executable environment for the parser submodule. The parser submodule parses the EL expression instance, checks its syntax correctness, and generates intermediate code. The executor submodule then executes the rule logic, calling script plugin units to handle complex calculations. The context manager submodule maintains a data container for requests and responses throughout the process, storing temporary variables such as the current temperature value and device status to ensure data consistency. Through the collaborative work of these submodules, efficient parsing and execution of the rule engine are achieved. Users can dynamically adjust rules without coding, improving the system's flexibility and maintainability. For example, in temperature optimization, the translator quickly converts rules, the builder instantiates the environment, the parser verifies the logic, the executor calculates the adjustment value, and the context manager tracks the data flow. Ultimately, this modular division of labor reduces processing latency.

[0079] In one embodiment, a rule configuration module is also included, which is used to edit rules through a user interface and persistently store them in a database.

[0080] During implementation, this module provides a graphical web interface, allowing users to directly input, debug, and save rules. For example, in a temperature control scenario, a user can add a rule through the interface: "When the batch uses a high-precision process, tighten the temperature threshold to ±1%." The rule configuration module converts the rule into structured data and stores it in the database. A persistence mechanism ensures that rules remain available after a system restart, preventing the loss of temporary configurations. Through visual editing and automatic saving, the module achieves both convenience and security in rule management, lowers the operational threshold, and enables non-technical personnel to quickly optimize workflows.

[0081] In one embodiment, the workflow judgment module includes an iterative control unit for iteratively executing rules to search and process until the predefined conditions are met.

[0082] During implementation, for example, during temperature adjustment, the iterative control unit monitors the rule execution results, such as checking whether the temperature has stabilized within the standard range after each adjustment. If the conditions are not met (e.g., temperature fluctuations still exceed the threshold), the unit automatically triggers the rule engine to re-search for and execute rules, looping until the temperature stabilizes for 10 seconds. For example, if the temperature is still too high after the first adjustment, the iterative control unit starts a second round of rule processing, gradually approaching the target value. Through the iterative mechanism, adaptive optimization of the workflow is achieved, avoiding accuracy issues caused by insufficient execution in a single run, and improving the system's robustness in complex scenarios.

[0083] In one embodiment, the script plugin unit supports multiple scripting languages, including JavaScript or Python, for dynamically expanding logical functions at runtime.

[0084] During implementation, the script plugin unit integrates an interpreter, allowing the loading of external script files. In temperature adjustment, if the rule requires complex calibration, the script plugin unit can call a Python script to calculate the temperature compensation value, based on historical data or a machine learning model. For example, a user could write a Python script like "def calibrate_temp(temp): return temp *0.98", and the rule engine could dynamically execute this logic through the plugin unit. Multi-language support enables flexible expansion of logical functions, enhances system compatibility and customization capabilities, and overcomes the limitations of fixed algorithms.

[0085] In one embodiment, the rule execution unit includes a formula calculation module, which uses the formula R=f(P,C) for calculation, wherein:

[0086] R represents the result of rule execution;

[0087] P represents process parameters, including temperature, pressure, or voltage;

[0088] C represents a context variable, including equipment status or production batch information;

[0089] f represents a rule-based logic function.

[0090] In implementation, for example in a temperature optimization scenario, the formula calculation module applies a function f, such as f(P,C) = P * (1 + C.adjustment), where P is the current temperature and C.adjustment is an adjustment coefficient based on the equipment status. The module calculates the R value in real time as the basis for temperature adjustment. Through standardized formula calculation, the accuracy and consistency of rule execution are achieved, human error is reduced, and the reliability of process control is improved.

[0091] In one embodiment, the actuator submodule includes:

[0092] The logical judgment unit is used to determine whether a rule contains custom logic.

[0093] Custom logic execution unit, used to execute via script plugin unit when custom logic exists;

[0094] The post-logic execution unit is used to directly execute post-logic when there is no user-defined logic.

[0095] During implementation, the logic judgment unit first analyzes whether the rule contains custom logic, such as checking if the rule expression has the "custom_script" tag. If it exists, the custom logic execution unit calls the external script through the script plugin unit; if it does not exist, the post-logic execution unit directly executes the post-rule part, such as logging or data cleanup. In temperature adjustment, if the logic judgment unit finds that the rule requires custom calibration, it triggers the Python script to perform fine-tuning; otherwise, it directly applies the standard post-logic. Through this layered execution mechanism, efficient processing and error isolation of rule logic are achieved, improving system security and execution efficiency.

[0096] Example 2

[0097] like Figure 3 As shown, an advanced process control method for semiconductor manufacturing includes the following steps:

[0098] S1: Receive process parameter adjustment requests from the equipment automation program;

[0099] S2: Convert the request into the internal communication protocol format of the advanced process control system;

[0100] S3: Preprocess the converted data and integrate the context data, which includes equipment status and production batch information;

[0101] S4: Execution rule configuration, which includes finding rules, executing rules, and processing custom logic through script plugins;

[0102] S5: Determine whether the workflow has ended based on predefined conditions, including process parameter thresholds or time thresholds; if the workflow has not ended, repeat S4 and S5 until the workflow ends; if the workflow ends, proceed to S6.

[0103] S6: Aggregate the execution results of all rules;

[0104] S7: Return the aggregation results to the device automation program.

[0105] The advanced process control method for semiconductor manufacturing provided in this embodiment has the same implementation principle and technical effects as the system embodiment in Embodiment 1. For the sake of brevity, any parts not mentioned in the method embodiment can be referred to the corresponding content in Embodiment 1.

[0106] Example 3

[0107] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a computer to perform the advanced process control method for semiconductor manufacturing described in Embodiment 2 above.

[0108] Example 4

[0109] An electronic device includes: a memory and a processor, wherein the processor and the memory are connected;

[0110] The memory is used to store programs;

[0111] The processor invokes a program stored in the memory to execute an advanced process control method for semiconductor manufacturing as described in Embodiment 2.

[0112] It should be noted that the electronic device mentioned may be, but is not limited to, personal computers (PCs), tablet computers, mobile internet devices (MIDs), etc.

[0113] It should be noted that processors, memory, and other components that may be present in electronic devices are electrically connected to each other, directly or indirectly, to enable data transmission or interaction. For example, processors, memory, and other components may be electrically connected to each other via one or more communication buses or signal lines.

[0114] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0116] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0117] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, laptop, server, mobile phone, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An advanced process control system for semiconductor manufacturing, characterized in that, include: The request receiving module is used to receive process parameter adjustment requests from the equipment automation program; The protocol parsing module, connected to the request receiving module, is used to convert the request into the internal communication protocol format of the advanced process control system; The workflow preprocessing module is connected to the protocol parsing module and is used to preprocess the parsed data and integrate context data, including device status and production batch information. The rules engine module, connected to the workflow preprocessing module, is used to execute rule configuration; The workflow judgment module is connected to the rule engine module and is used to determine whether the workflow has ended based on predefined conditions, including process parameter thresholds or time thresholds. The result aggregation module is connected to the workflow judgment module and is used to aggregate the execution results of all rules. A response return module, connected to the result aggregation module, is used to return the aggregation results to the device automation program; The workflow judgment module includes an iterative control unit, which is used to search and process rules iteratively until the predefined conditions are met. The rule engine module includes a rule lookup unit, a rule execution unit, and a script plugin unit; The rule lookup unit is used to retrieve matching rules from the rule base; The rule execution unit is used to calculate the rule logic; The script plugin unit is used to dynamically load external scripts; The rule execution unit includes a formula calculation module, which uses the formula R=f(P,C) for calculation, where: R represents the result of rule execution; P represents process parameters, including temperature, pressure, or voltage; C represents a context variable, including equipment status or production batch information; f represents a rule-based logic function.

2. The system according to claim 1, characterized in that, The rule engine module also includes: The translator submodule is used to translate user-configured rules into expression language expressions; The builder submodule is used to build runtime instances of the rules engine; The parser submodule is used to parse expression language expression instances; The executor submodule is used to execute rule logic; The context manager submodule is used to maintain data containers for requests and responses.

3. The system according to claim 2, characterized in that, It also includes a rule configuration module, which allows users to edit rules through a user interface and persist them to a database.

4. The system according to claim 2, characterized in that, The actuator submodule includes: The logical judgment unit is used to determine whether a rule contains custom logic. Custom logic execution unit, used to execute via script plugin unit when custom logic exists; The post-logic execution unit is used to directly execute post-logic when there is no user-defined logic.

5. An advanced process control method for semiconductor manufacturing, characterized in that, Includes the following steps: S1: Receive process parameter adjustment requests from the equipment automation program; S2: Convert the request into the internal communication protocol format of the advanced process control system; S3: Preprocess the converted data and integrate the context data, which includes equipment status and production batch information; S4: Execution rule configuration, which includes finding rules, executing rules, and processing custom logic through script plugins; the execution rule is calculated using the formula R=f(P,C), where R represents the rule execution result, P represents process parameters, including temperature, pressure, or voltage, C represents context variables, including equipment status or production batch information, and f represents the rule logic function; S5: Determine whether the workflow has ended based on predefined conditions, including process parameter thresholds or time thresholds; iterate through execution rules to find and process until the predefined conditions are met. If the workflow is not finished, repeat steps S4 and S5 until the workflow is finished; if the workflow is finished, proceed to step S6. S6: Aggregate the execution results of all rules; S7: Return the aggregation results to the device automation program.

6. An electronic device, characterized in that, include: A memory and a processor, wherein the processor and the memory are connected; The memory is used to store programs; The processor invokes a program stored in the memory to execute the method of claim 5.

7. A computer-readable storage medium, characterized in that, It stores a computer program, which is executed by a computer to perform the method of claim 5.

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