System, program, information processing method and construction method of system

Abstract

To provide a system, a program, an information processing method, a construction method of the system, etc. capable of estimating a characteristic of an object to be processed with higher accuracy than before, and preventing proliferation and duplication of a model to reflect essential physics.SOLUTION: A system is provided. The system includes one or more classes in which a model which executes calculation related to the characteristic of an object to be processed in a certain manufacturing process is defined. A fluctuation factor model, and a characteristic fluctuation model are defined in the one or more classes. The fluctuation factor model is defined so as to calculate the value of a fluctuation factor, by using an atmosphere generated when the object to be processed is processed by a processing device. The fluctuation factor is a factor which varies the characteristic of the object to be processed. The characteristic fluctuation model is defined so as to calculate a variation value of the characteristic of the object to be processed, by using the value of the fluctuation factor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a system, a program, an information processing method, and a system construction method. [Background technology]

[0002] Patent Document 1 discloses a product information management method.

[0003] This product information management method determines that configuration-related information and manufacturing-related information are managed as the same type of management objects, and then creates an object model by extracting the relationships between all of these management objects and classifying them into multiple classes CL1, 2, 3, .... Under this model, each piece of information is stored in memory along with its relationships with other information, so there is no need to change the contents of that memory when other information changes. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 9-267239 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technique of Patent Document 1 does not adequately represent the behavior of the manufacturing process, and therefore it is not possible to estimate the characteristics of the workpiece.

[0006] In view of the above circumstances, the present invention provides a system, a program, an information processing method, a system construction method, etc., which are capable of estimating the characteristics of a workpiece with higher accuracy than conventional methods, and which are capable of reflecting essential physics by preventing the proliferation and duplication of models. [Means for solving the problem]

[0007] According to one aspect of the present invention, a system is provided. The system includes one or more classes in which models are defined for performing calculations related to the characteristics of a workpiece in a manufacturing process. The one or more classes define a variation factor model and a characteristic variation model. The variation factor model is defined to calculate the value of the variation factor using an atmosphere generated when the workpiece is processed by a processing device. The variation factor is a factor that causes the characteristics of the workpiece to vary. The characteristic variation model is defined to calculate the variation value of the characteristic of the workpiece using the value of the variation factor.

[0008] According to this embodiment, it is possible to estimate the characteristics of the object to be processed with higher accuracy than before, and to prevent the proliferation and duplication of models and to reflect essential physics. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a configuration diagram illustrating an information processing system 100. [Figure 2] FIG. 2 is a block diagram showing the hardware configuration of an information processing device 200. [Figure 3] FIG. 2 is a block diagram showing the hardware configuration of a terminal 300. [Figure 4] FIG. 4 is a block diagram showing the software configuration of the system 400. [Figure 5] 1 is an activity diagram showing the flow of a method for constructing a system 400, which is executed by an information processing device 200. FIG. [Figure 6] 5 is a diagram showing the relationship between a thermal oxidation furnace model 510, a plasma etching apparatus model 520, a semiconductor wafer model 580, and the system 400. [Figure 7] 5 is an activity diagram illustrating information processing between a thermal oxidation furnace model 510 and a semiconductor wafer model 580 using the modeled system 400. FIG. [Figure 8] FIG. 10 is a diagram illustrating an example of data processing using a thermal energy model. [Figure 9] FIG. 10 is a diagram illustrating an example of data processing by a thermal fluctuation model 421. [Figure 10] 5 is a diagram showing another example of the relationship between a thermal oxidation furnace model 510, a plasma etcher model 520, a semiconductor wafer model 580, and the system 400. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0011] Incidentally, the program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0012] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously trained the correlation between input and output, or a large-scale language model that can output a desired result by inputting a prompt.

[0013] In one embodiment, a "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage and current, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on a circuit in the broad sense.

[0014] Furthermore, a circuit in a broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, a processor, a memory, etc. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0015] 1. Hardware Configuration In Section 1, the hardware configuration of this embodiment will be described.

[0016] 1-1. Information Processing System 100 FIG. 1 is a configuration diagram showing an information processing system 100. The information processing system 100 includes an information processing device 200 and a terminal 300, which are connected via a network. These components will be further described. Here, a system exemplified as the information processing system 100 is composed of one or more devices or components. Therefore, for example, even the information processing device 200 alone can become a system exemplified as the information processing system 100.

[0017] 1-2. Information processing device 200 2 is a block diagram showing the hardware configuration of information processing device 200. Information processing device 200 has a control unit 210, a storage unit 220, and a communication unit 250, and these components are electrically connected via a communication bus 260 inside information processing device 200. A system 400, which will be described later, will be described as being realized by information processing device 200. Each component will be further described.

[0018] The control unit 210 processes and controls the overall operations related to the information processing device 200. The control unit 210 is, for example, a central processing unit (CPU) not shown. The control unit 210 realizes various functions related to the information processing device 200 by reading out predetermined programs stored in the storage unit 220. In other words, information processing by software stored in the storage unit 220 is specifically realized by the control unit 210, which is an example of hardware, and can be executed as each functional unit included in the control unit 210. Note that the control unit 210 is not limited to being single, and multiple control units 210 may be provided for each function. Alternatively, a combination of these may be used.

[0019] The storage unit 220 stores various information necessary for information processing by the information processing device 200. This may be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs related to the information processing device 200 executed by the control unit 210, or as a memory such as a random access memory (RAM) that stores information (arguments, arrays, etc.) temporarily required for program calculations. Alternatively, it may be implemented as a combination of these.

[0020] The communication unit 250 is preferably a wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., but may also include wireless LAN network communication, mobile communication such as 5G / LTE / 3G, Bluetooth (registered trademark) communication, etc. as necessary. In other words, it is more preferable to implement it as a collection of multiple communication means. In other words, the information processing device 200 communicates various information with the terminal 300 via the communication unit 250 over a network.

[0021] 1-3. Terminal 300 3 is a block diagram showing the hardware configuration of terminal 300. Terminal 300 has a control unit 310, a storage unit 320, a display unit 330, an input unit 340, and a communication unit 350, and these components are electrically connected inside terminal 300 via a communication bus 360. Terminal 300 will be described as being used for building a system 400 (described below) and for quality control using system 400. Descriptions of control unit 310, storage unit 320, and communication unit 350 will be omitted because they are substantially the same as the descriptions of control unit 210, storage unit 220, and communication unit 250 in information processing device 200.

[0022] The display unit 330 may be included in the housing of the terminal 300 or may be externally attached. The display unit 330 displays a screen of a graphical user interface (GUI) that can be operated by the user. This is preferably implemented by selectively using display devices such as a CRT display, a liquid crystal display, an organic EL display, and a plasma display depending on the type of terminal 300. In the following description, the display unit 330 is assumed to be included in the housing of the terminal 300.

[0023] The input unit 340 may be included in the housing of the terminal 300 or may be externally attached. For example, the input unit 340 may be implemented as a touch panel integrated with the display unit 330. A touch panel allows a user to input tapping, swiping, and the like. Of course, a switch button, a mouse, a QWERT keyboard, or the like may be used instead of a touch panel. That is, the input unit 340 accepts an operation input made by the user. The input is transferred as a command signal to the control unit 310 via the communication bus 360. The control unit 310 can then execute predetermined control and calculation as necessary.

[0024] 2. Software Configuration In Section 2, the software configuration of this embodiment will be described.

[0025] FIG. 4 is a block diagram showing the software configuration of system 400. System 400 includes class 410 and class 420 (one or more classes) in which models for performing calculations related to the characteristics of a workpiece in a certain manufacturing process are defined. A variation factor model is defined in class 410. A characteristic variation model is defined in class 420. Class 410 and class 420 are concepts defined as classes for designing objects in order to capture the attributes and behavior of matters handled by the software as a single object. Furthermore, each model in this embodiment is a system described as an equation that mimics the behavior of measurable key indicators of a time-varying phenomenon. Each component will be further described.

[0026] The variation factor model is defined to calculate the value of the variation factor using the atmosphere that occurs when the workpiece is processed by the processing device, where the variation factor is a factor that changes the characteristics of the workpiece.

[0027] The characteristic variation model is defined to calculate the variation value of the characteristic of the object to be treated using the calculated values ​​of the variation factors.

[0028] In this way, the variation factor model and the characteristic variation model are defined in different classes, so that the scalability of the system 400 can be ensured.

[0029] 3. Construction method Section 3 describes the flow of a method for constructing system 400. This construction method is a method for constructing system 400. System 400 includes classes 410 and 420 (one or more classes) in which models for performing calculations related to the characteristics of a workpiece in a certain manufacturing process are defined. This construction method includes a first definition step and a second definition step. In the first definition step, a variation factor model for calculating the value of a variation factor using the atmosphere generated when the workpiece is processed by a processing device is defined in class 410. Here, the variation factor is a factor that causes the characteristics of the workpiece to fluctuate. In the second definition step, a characteristic variation model for calculating the variation value of the characteristic of the workpiece using the value of the variation factor is defined in class 420.

[0030] 5 is an activity diagram showing the flow of a method for constructing the system 400, which is executed by the information processing device 200. Section 3 will be explained along with each activity in this activity diagram.

[0031] First, the control unit 310 in the terminal 300 transmits information (hereinafter also referred to as "first definition information") for defining a variation factor model in the class 410 that calculates the value of a variation factor using the atmosphere that occurs when the processing object is processed by the processing device to the information processing device 200 (activity A110). In activity A110, for example, the following two-stage information processing is executed: (1) The control unit 310 reads out the first definition information from the storage unit 320. (2) The control unit 310 transmits the first definition information to the information processing device 200 via the communication unit 350.

[0032] Next, the control unit 210 in the information processing device 200 receives the first definition information transmitted from the terminal 300 (activity A120). In activity A120, for example, the following two-stage information processing is executed: (1) The communication unit 250 receives the first definition information transmitted from the terminal 300. (2) The control unit 210 stores the received first definition information in the storage unit 220.

[0033] Next, the control unit 210 in the information processing device 200 defines a variation factor model for the class 410 based on the received first definition information (activity A130). Activity A130 corresponds to the first definition step. In activity A130, for example, the following three stages of information processing are executed: (1) The control unit 210 reads out the first definition information from the storage unit 220. (2) The control unit 210 executes the definition process and defines a variation factor model for the class 410. (3) The control unit 210 stores the class 410 in the storage unit 220.

[0034] Next, the control unit 310 in the terminal 300 transmits information (hereinafter also referred to as "second definition information") for defining a characteristic variation model in the class 420 that calculates the variation value of the characteristic of the workpiece using the value of the variation factor calculated by the variation factor model to the information processing device 200 (activity A140). In activity A140, for example, the following two-stage information processing is executed: (1) The control unit 310 reads out the second definition information from the storage unit 320. (2) The control unit 310 transmits the second definition information to the information processing device 200 via the communication unit 350.

[0035] Next, the control unit 210 in the information processing device 200 receives the second definition information transmitted from the terminal 300 (activity A150). In activity A150, for example, the following two-stage information processing is executed: (1) The communication unit 250 receives the second definition information transmitted from the terminal 300. (2) The control unit 210 stores the received second definition information in the storage unit 220.

[0036] Next, the control unit 210 in the information processing device 200 defines a characteristic variation model for the class 420 based on the received second definition information (activity A160). Activity A160 corresponds to the second definition step. In activity A160, for example, the following three stages of information processing are executed: (1) The control unit 210 reads the second definition information from the storage unit 220. (2) The control unit 210 executes the definition process and defines a characteristic variation model for the class 420. (3) The control unit 210 stores the class 420 in the storage unit 220.

[0037] Next, the control unit 310 in the terminal 300 transmits a command signal (hereinafter also referred to as "command signal") for modeling the system 400 to the information processing device 200 (activity A170). In activity A170, for example, the following three stages of information processing are executed: (1) The control unit 310 reads the command signal from the storage unit 320. (2) The control unit 310 transmits the command signal to the information processing device 200 via the communication unit 350.

[0038] Next, the control unit 210 in the information processing device 200 receives the command signal transmitted from the terminal 300 (activity A180). In activity A180, for example, the following two-stage information processing is executed: (1) The communication unit 250 receives the command signal transmitted from the terminal 300. (2) The control unit 210 stores the received command signal in the storage unit 220.

[0039] Next, the control unit 210 in the information processing device 200 models the class 410 and the class 420 (activity A190). In activity A190, for example, the following three stages of information processing are executed: (1) The control unit 210 reads a command signal from the storage unit 220. (2) The control unit 210 reads the class 410 and the class 420 from the storage unit 220. (3) The control unit 210 executes a modeling process to model the system 400.

[0040] The activities A110 and A140 may be executed in any order or simultaneously. That is, the control unit 210 in the information processing device 200 may execute activities A120 to A130 after executing activity A110, and may execute activities A150 to A160 after executing activity A140.

[0041] 4. Information Processing Method The information processing method of this embodiment will be described in Section 4. This information processing method executes each model defined in the class 410 and the class 420 of the system 400.

[0042] 6 is a diagram showing the relationship between thermal oxidation furnace model 510, plasma etching equipment model 520, semiconductor wafer model 580, and system 400. FIG. 7 is an activity diagram showing information processing between thermal oxidation furnace model 510 and semiconductor wafer model 580 using modeled system 400. Section 4 will be explained along with each activity in the activity diagram of FIG. 7, with reference to FIG. 6.

[0043] Here, the workpiece is a semiconductor wafer, and the processing equipment is a thermal oxidation furnace and a plasma etching equipment. Therefore, system 400 is used to estimate the variation in the characteristics of semiconductor wafers processed in a thermal oxidation furnace and the variation in the characteristics of semiconductor wafers processed in a plasma etching equipment.

[0044] Here, it is assumed that thermal oxidation furnace model 510, plasma etching equipment model 520, semiconductor wafer model 580, and system 400 are realized by information processing device 200. In other words, it is assumed that information processing by software (thermal oxidation furnace model 510, plasma etching equipment model 520, semiconductor wafer model 580, and system 400) realized by information processing device 200 is specifically realized by hardware (control unit 210, storage unit 220, and communication unit 250) in information processing device 200. Therefore, in the following description of each activity, for convenience of explanation, the software will be used as the subject, but in reality, it is assumed that the software is specifically realized by the hardware configuration of information processing device 200.

[0045] The system 400 includes a thermal oxidation process model 411, a plasma etching process model 412, a thermal energy model 413, and a thermal fluctuation model 421. The thermal fluctuation model 421 corresponds to the property fluctuation model in the claims.

[0046] The thermal oxidation process model 411 is a model corresponding to the thermal oxidation process in the semiconductor manufacturing process. The thermal oxidation process model 411 includes an oxide film generation model and a thermal energy model. The thermal energy model corresponds to the variable factor model in the claims. The oxide film generation model calculates the degree of oxide film generation, which is the main processing condition in the thermal oxidation furnace. The thermal energy model calculates the value of thermal energy, which is an influencing factor on secondary characteristics in the thermal oxidation furnace.

[0047] The plasma etching process model 412 corresponds to the plasma etching process in the semiconductor manufacturing process. The plasma etching process model 412 includes an etching model and a thermal energy model. The thermal energy model corresponds to the characteristic variation model in the claims. The etching model calculates the degree of etching, which is the main processing condition of the plasma etching equipment. The thermal energy model calculates the value of thermal energy, which is an influencing factor on secondary characteristics of the plasma etching equipment.

[0048] The thermal energy calculated by thermal oxidation process model 411 and plasma etching process model 412 is input to thermal fluctuation model 421 via thermal energy 413. For convenience of explanation, in each of the following activities, thermal oxidation furnace model 510 is exemplified as a processing device that outputs an atmosphere, but plasma etching device model 520 also performs processing similar to that of thermal oxidation furnace model 510. For example, in activity A340, plasma etching process model 412 receives data related to the plasma atmosphere via atmosphere 550 and plasma atmosphere 570.

[0049] In the thermal oxidation process model 411, the main processing conditions in the thermal oxidation furnace are calculated using an oxide film generation model, and in the plasma etching process model 412, the main processing conditions in the plasma etching device are calculated using an etching model. In this embodiment, the processing of these main processing conditions is omitted, and the explanation focuses on thermal energy, which is a factor affecting secondary characteristics.

[0050] First, the thermal oxidation furnace model 510 outputs data related to the thermal oxidation atmosphere (hereinafter also referred to as "thermal oxidation atmosphere data") to the interface 540 via the interface 530 (activity A310). The thermal oxidation atmosphere refers to the atmosphere that occurs when a semiconductor wafer is processed in a thermal oxidation furnace. In activity A310, for example, the following three stages of information processing are executed: (1) The control unit 210 reads out the thermal oxidation furnace model 510 from the memory unit 220. (2) The control unit 210 executes the thermal oxidation furnace model 510 and causes it to output the thermal oxidation atmosphere data. (3) The control unit 210 causes the memory unit 220 to store the thermal oxidation atmosphere data.

[0051] Next, the semiconductor wafer model 580 receives thermal oxidation atmosphere data expressed by atmosphere 550 and thermal oxidation atmosphere 560 via interface 540 (activity A320) and outputs the data to the thermal oxidation process model 411, which is an appropriate model in the system 400 (activity A330). If the received data is plasma atmosphere data expressed by atmosphere 550 and plasma atmosphere 570, the semiconductor wafer model 580 outputs the data to the plasma etching process model 412, not to the thermal oxidation process model 411. In activities A320 to A330, for example, the following four stages of information processing are performed: (1) The control unit 210 reads the thermal oxidation atmosphere data, the interface 540, and the semiconductor wafer model 580 from the storage unit 220. (2) The control unit 210 inputs the thermal oxidation atmosphere data to the semiconductor wafer model 580 via the interface 540. (3) The control unit 210 reads out the atmosphere 550, the thermal oxidation atmosphere 560, and the thermal oxidation process model 411 from the storage unit 220. (4) The control unit 210 inputs the thermal oxidation atmosphere data into the thermal oxidation process model 411.

[0052] Next, the thermal oxidation process model 411 receives the thermal oxidation ambient data (activity A340) and calculates process-specific structural changes using the input thermal oxidation ambient data and the characteristics of the semiconductor wafer in the cleaning process (preceding the thermal oxidation process) (activity A350). Process-specific structural changes, for example, in the thermal oxidation process, correspond to changes in the thickness and quality of the oxide film formed. Furthermore, the thermal oxidation process model 411 calculates the value of thermal energy generated by the thermal oxidation process (activity A360). That is, the variation factor model is defined to reference the characteristics of the workpiece in the preceding process of the target manufacturing process and to calculate the value of thermal energy. Activities A340 to A360, for example, perform the following three-stage information processing: (1) The control unit 210 reads the characteristics of the semiconductor wafer in the cleaning process from the storage unit 220. (2) The control unit 210 executes the thermal oxidation process model 411 and calculates the process-specific structural changes and thermal energy based on the thermal oxidation ambient data, the characteristics of the semiconductor wafer in the cleaning process, and the thermal energy model. (3) The control unit 210 stores the calculated process-specific structural change value and thermal energy value (hereinafter, "data on the thermal energy value" will also be referred to as "thermal energy data") in the storage unit 220.

[0053] Figure 8 shows an example of data processing using a thermal energy model. The thermal energy generated by a thermal oxidation atmosphere i (i corresponds to a semiconductor manufacturing process and is expressed as an integer equal to or greater than 1) in a thermal oxidation furnace is classified into heat transfer energy resulting from substrate heating, etc., chemical energy received by the semiconductor wafer, mechanical energy, and electromagnetic energy. The thermal energy model then calculates the total thermal energy as the sum of these individual energies.

[0054] Using the above calculations, the thermal oxidation process model 411 outputs thermal energy data, which is a fluctuation factor, to the semiconductor wafer model 580 (activity A370). In activity A370, for example, the following two-stage information processing is executed: (1) The control unit 210 reads out the thermal energy data and the semiconductor wafer model 580 from the storage unit 220. (2) The control unit 210 inputs the thermal energy data to the semiconductor wafer model 580.

[0055] Next, the semiconductor wafer model 580 receives the thermal energy data calculated by the thermal oxidation process model 411 (activity A380) and outputs it to the thermal fluctuation model 421 in the form of thermal energy 413 (activity A390). Even if the received thermal energy data is obtained from the plasma etching process model 412, it is still output to the thermal fluctuation model 421. Therefore, the thermal fluctuation model 421 is defined to be a model that describes thermal fluctuation phenomena common to any process. In this manner, by sharing processing related to fluctuation factors common to any process, the roles of each model are clearly divided, making it easy to expand and update each model due to the addition of processes or the elucidation of physical phenomena. Activities A380 to A390, for example, perform the following three-stage information processing. (1) The control unit 210 executes the semiconductor wafer model 580 and causes it to output the thermal energy data in the form of thermal energy 413. (2) The control unit 210 reads the thermal fluctuation model 421 from the storage unit 220. (3) The control unit 210 inputs the thermal energy data expressed by the thermal energy 413 into the thermal fluctuation model 421 .

[0056] Next, the thermal fluctuation model 421 receives the thermal energy data represented by the thermal energy 413 (activity A400). The thermal fluctuation model 421 calculates the thermal fluctuation value of the semiconductor wafer characteristic (hereinafter also referred to as the "thermal fluctuation value") using the calculated total value of the thermal energy and the characteristics of the semiconductor wafer in the cleaning process (activity A410). That is, the characteristic fluctuation model is defined to refer to the characteristics of the workpiece in the previous process of the target manufacturing process, and is further defined to calculate the fluctuation value of the workpiece characteristic using the thermal energy value. In activities A400 to A410, for example, the following three stages of information processing are executed: (1) The control unit 210 reads the total value of the thermal energy from the storage unit 220. (2) The control unit 210 executes the thermal fluctuation model 421 to calculate the thermal fluctuation value. (3) The control unit 210 stores the calculated thermal fluctuation value in the storage unit 220. By referring to the characteristics of the workpiece in the previous process of the target manufacturing process, the characteristics of the workpiece can be estimated with higher accuracy.

[0057] 9 is a diagram showing an example of data processing by the thermal fluctuation model 421. The characteristics of a semiconductor wafer are classified into Z-axis position, crystallinity, impurity concentration, surface roughness, mass density, etc. Then, the thermal fluctuation model 421 integrates each of these characteristics to calculate the influence on the semiconductor wafer characteristics.

[0058] Next, the thermal variation model 421 outputs the calculated thermal variation value to the semiconductor wafer model 580 (activity A420). In activity A420, for example, the following two-stage information processing is executed: (1) The control unit 210 reads the calculated thermal variation value from the storage unit 220. (2) The control unit 210 executes the thermal variation model 421 and causes it to output the thermal variation value.

[0059] Next, the semiconductor wafer model 580 receives the output thermal variation value (activity A430). In activity A430, for example, the following information processing is executed: The control unit 210 inputs the output thermal variation value to the semiconductor wafer model 580.

[0060] Next, semiconductor wafer model 580 uses the thermal fluctuation value to estimate a change in state occurring in the semiconductor wafer (activity A440). In activity A440, for example, the following information processing is executed. Control unit 210 executes semiconductor wafer model 580 and estimates a change in state occurring in the semiconductor wafer based on the thermal fluctuation value. The estimated result of the change in state occurring in the semiconductor wafer is displayed on display unit 330 of terminal 300.

[0061] According to this aspect, the characteristics of the workpiece can be estimated with high accuracy by focusing on thermal energy, which is the main variable factor.

[0062] According to this embodiment, since not only the main processing conditions but also factors affecting secondary characteristics are taken into consideration, the characteristics of the workpiece can be estimated with higher accuracy than in the past. Furthermore, due to the simple configuration, the saved resources can be used for other core functions.

[0063] Although the embodiment of the present invention has been described above, the present invention is not limited to this and can be modified as appropriate within the scope of the technical idea of ​​the invention.

[0064] 5. Variations In Section 5, a modification of this embodiment will be described.

[0065] An aspect of this embodiment may be a program configured to cause a computer to execute each model defined in the class of the system 400.

[0066] The control unit 210 performs write processing (storage processing) and read processing of various data and information to the memory unit 220, but this is not limited to this, and for example, the information processing of each activity may be performed using a register or cache memory within the control unit 210.

[0067] In this embodiment, the variation factor model and the characteristic variation model are defined in different classes, but the present invention is not limited to this. Fig. 10 is a diagram showing another example of the relationship between thermal oxidation furnace model 510, plasma etching equipment model 520, semiconductor wafer model 580, and system 400. As shown in Fig. 10, the variation factor model and the characteristic variation model may be defined in thermal oxidation process model 414 and plasma etching process model 415, respectively.

[0068] In this embodiment, the fluctuation factor is described as thermal energy, but is not limited to this. The fluctuation factor may be any factor that fluctuates the properties of the workpiece, such as a gas atmosphere such as an oxidizing gas or a reducing gas, or an atmospheric pressure such as high pressure or low pressure.

[0069] The processing equipment represented as a model is not limited to a thermal oxidation furnace or a plasma etching equipment, but may be any equipment that processes semiconductor wafers. Other examples of equipment that processes semiconductor wafers include a sputtering equipment. This embodiment is suitable for use in representing semiconductor manufacturing processes.

[0070] 6.Other It may be provided in the following manner.

[0071] (1) A system including one or more classes in which models are defined that perform calculations related to the characteristics of a workpiece in a manufacturing process, wherein the one or more classes define a variation factor model and a characteristic variation model, the variation factor model is defined to calculate the value of a variation factor using an atmosphere generated when the workpiece is processed by a processing device, the variation factor being a factor that varies the characteristics of the workpiece, and the characteristic variation model is defined to calculate the variation value of the characteristic of the workpiece using the value of the variation factor.

[0072] According to this aspect, it is possible to estimate the properties of the object to be processed with higher accuracy than before, and to prevent the proliferation and duplication of models and reflect the essential physics. In addition, because of the simple configuration, the saved resources can be used for other core functions.

[0073] (2) In the system described in (1) above, the variation factor model and the characteristic variation model are defined in different classes.

[0074] According to this embodiment, the expandability of the system can be ensured.

[0075] (3) In the system described in (1) or (2) above, the fluctuation factor model is defined to calculate a value of thermal energy, and the characteristic fluctuation model is defined to calculate the fluctuation value using the value of thermal energy.

[0076] According to this aspect, the characteristics of the workpiece can be estimated with high accuracy by focusing on thermal energy, which is the main variable factor.

[0077] (4) A system according to any one of (1) to (3) above, wherein the fluctuation factor model and the characteristic fluctuation model are defined to refer to the characteristics of the workpiece in a previous process of the manufacturing process.

[0078] According to this aspect, the characteristics of the workpiece can be estimated with higher accuracy.

[0079] (5) In the system according to any one of (1) to (4) above, the object to be processed is a semiconductor wafer, and the processing device is a device that processes the semiconductor wafer.

[0080] According to this aspect, it can be suitably used when expressing the manufacturing process of a semiconductor.

[0081] (6) A program configured to cause a computer to execute each model defined in the class of systems described in any one of (1) to (5) above.

[0082] According to this aspect, it is possible to estimate the properties of the object to be processed with higher accuracy than before, and to prevent the proliferation and duplication of models and reflect the essential physics. In addition, because of the simple configuration, the saved resources can be used for other core functions.

[0083] (7) An information processing method, which executes each model defined in a class of systems described in any one of (1) to (5) above.

[0084] According to this aspect, it is possible to estimate the properties of the object to be processed with higher accuracy than before, and to prevent the proliferation and duplication of models and reflect the essential physics. In addition, because of the simple configuration, the saved resources can be used for other core functions.

[0085] (8) A method for constructing a system, the system including one or more classes in which models for performing calculations regarding the characteristics of a workpiece in a manufacturing process are defined, the method comprising a first definition step and a second definition step, in which in the first definition step, a variation factor model is defined for the class that calculates the value of a variation factor using the atmosphere generated when the workpiece is processed by a processing device, the variation factor being a factor that causes the characteristics of the workpiece to fluctuate, and in the second definition step, a characteristic variation model is defined for the class that calculates the variation value of the characteristics of the workpiece using the value of the variation factor.

[0086] According to this aspect, it is possible to estimate the properties of the object to be processed with higher accuracy than before, and to prevent the proliferation and duplication of models and reflect the essential physics. In addition, because of the simple configuration, the saved resources can be used for other core functions. Of course, this is not the case. [Explanation of symbols]

[0087] 100: Information Processing Systems 200: Information processing device 210: Control unit 220: Storage section 250: Communications Department 260: Communication bus 300: Terminal 310: Control unit 320: Storage section 330: Display section 340: Input section 350: Communications Department 360: Communication bus 400: System 410: Class 411: Thermal oxidation process model 412: Plasma etching process model 413: Thermal Energy 414: Thermal oxidation process model 415: Plasma etching process model 420: Class 421: Thermal Variation Model 510: Thermal oxidation furnace model 520: Plasma etching equipment model 530: Interface 540: Interface 550: Atmosphere 560: Thermal oxidation atmosphere 570: Plasma atmosphere 580: Semiconductor wafer model

Claims

1. 1. A system comprising: The model includes one or more classes for performing calculations on the properties of a workpiece in a manufacturing process; A variation factor model and a characteristic variation model are defined for the one or more classes; the fluctuation factor model is defined to calculate a value of the fluctuation factor using an atmosphere generated when the object to be treated is treated by a treatment device; the fluctuation factor is a factor that fluctuates the properties of the object to be treated, The characteristic variation model is defined to calculate a variation value of the characteristic of the object to be treated using the value of the variation factor. system.

2. 10. The system of claim 1, The variation factor model and the characteristic variation model are defined in different classes. system.

3. 10. The system of claim 1, the variation factor model is defined to calculate a value of thermal energy; The characteristic fluctuation model is defined to calculate the fluctuation value using the value of the thermal energy. system.

4. 10. The system of claim 1, the fluctuation factor model and the characteristic fluctuation model are defined so as to refer to the characteristics of the workpiece in a previous process of the manufacturing process. system.

5. 10. The system of claim 1, the workpiece is a semiconductor wafer, the processing device is a device for processing the semiconductor wafer; system.

6. A program, A system configured to cause a computer to execute each model defined in a class of systems according to any one of claims 1 to 5, program.

7. An information processing method, comprising: Executing each model defined in a class of systems according to any one of claims 1 to 5, Information processing methods.

8. A system construction method, comprising: The system includes one or more classes in which models are defined to perform calculations related to properties of a workpiece in a manufacturing process; The method includes a first definition step and a second definition step, In the first definition step, a variation factor model is defined for the class, which calculates a value of a variation factor using an atmosphere generated when the workpiece is processed by a processing device; the fluctuation factor is a factor that fluctuates the properties of the object to be treated, In the second definition step, a characteristic variation model is defined for the class, which calculates a variation value of the characteristic of the object to be processed using the value of the variation factor. How to build it.

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