Method for allocating device resources of a photoresist coating machine, storage medium and terminal

Abstract

A method for allocating equipment resources, a storage medium, and a terminal for a photoresist coating machine are disclosed. The method includes: constructing a multi-level structural model based on photolithography processes; constructing a judgment matrix for the multi-level structural model; obtaining the subjective and objective weights of the criterion layer on the target layer based on the judgment matrix; obtaining the comprehensive weight of the criterion layer on the target layer; and performing gradient classification of the photolithography processes. Each product consists of hundreds or even thousands of processes, and a semiconductor factory has dozens of products, resulting in tens of thousands of processes. Each process has different requirements for the board temperature of the photoresist coating machine. Moreover, the board temperature in the photoresist coating machine has a significant impact on equipment efficiency, and the board temperature is related to the photolithography process. Therefore, this method uses the photolithography process as a classification object and performs gradient classification control of the photolithography process. Based on the supply and demand of the board temperature of the photoresist coating machine, the resource allocation of the photoresist coating machine among different products and processes is guided to achieve the lowest loss and highest efficiency.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing technology, and in particular to a method for allocating equipment resources for a photoresist coating machine, a storage medium, and a terminal. Background Technology

[0002] Photolithography equipment is one of the core pieces of equipment in wafer manufacturing, representing the most expensive and indispensable investment in the entire wafer equipment investment. Furthermore, the photolithography process demands extremely high levels of cleanliness and stability in the environment, resulting in high maintenance costs for the cleanrooms used in its fabrication. Therefore, continuously improving the efficiency of photolithography equipment is crucial in the semiconductor industry.

[0003] Photolithography equipment includes photoresist coating machines and photolithography machines. The photoresist coating machine is mainly used to coat photoresist, while the photolithography machine transfers the pattern image onto the silicon wafer. The two are used together to complete the key photolithography process in semiconductor manufacturing.

[0004] However, existing photoresist coating machines still have many problems in terms of equipment resource allocation. Summary of the Invention

[0005] The technical problem solved by this invention is to provide a method for allocating equipment resources, a storage medium, and a terminal for a photoresist coating machine, so as to scientifically and rationally allocate the equipment resources of the photoresist coating machine.

[0006] To address the aforementioned problems, this invention provides a method for allocating equipment resources for a photoresist coating machine, comprising: constructing a multi-level structural model based on the photolithography process, wherein the multi-level structural model includes a target layer, a criterion layer, and a scheme layer; constructing a judgment matrix for the multi-level structural model based on the analytic hierarchy process (AHP); obtaining the subjective and objective weights of the criterion layer to the target layer based on the judgment matrix; obtaining the comprehensive weight of the criterion layer to the target layer based on the subjective and objective weights; and performing gradient classification of the photolithography process based on the comprehensive weight to guide the allocation of equipment resources for the photoresist coating machine.

[0007] Optionally, the method for constructing a judgment matrix for the multi-layered structural model based on the analytic hierarchy process includes: using a 1-9 comparison scale and a pairwise comparison method to construct a pairwise comparison array for each factor index in the scheme layer.

[0008] Optionally, the method for obtaining the subjective weight of the criterion layer to the target layer based on the judgment matrix includes: comparing the indicators of the scheme layer in the judgment matrix pairwise; normalizing the eigenvalues ​​of each feature vector in the judgment matrix to obtain the subjective weight of the criterion layer to the target layer.

[0009] Optionally, after obtaining the subjective weights, a consistency check is performed on the subjective weights to determine whether the results are reasonable.

[0010] Optionally, if the consistency ratio is less than or equal to 0.1, the obtained subjective weight is deemed reasonable.

[0011] Optionally, the method for obtaining the objective weight of the criterion layer to the target layer based on the judgment matrix includes: standardizing the judgment matrix using the entropy weight method to obtain the score ratio of each indicator in the judgment matrix; obtaining the information entropy of each indicator based on the score ratio of each indicator in the judgment matrix; and obtaining the objective weight of each indicator based on the information entropy of each indicator.

[0012] Optionally, the method for obtaining the comprehensive weight of the criterion layer to the target layer includes: redistributing the subjective weight and the objective weight respectively to obtain the comprehensive weight.

[0013] Optionally, the method for gradient classification of photolithography processes based on the comprehensive weights includes: processing the comprehensive weights using the Pareto classification method to obtain the gradient level of the photolithography process.

[0014] Optionally, the indicators of the target layer include: process rational classification indicators.

[0015] Optionally, the indicators of the criteria layer include: machine-related indicators, production-related indicators, and product-related indicators; wherein, the reasonable process classification indicators include: machine-related indicators, production-related indicators, and product-related indicators.

[0016] Optionally, the indicators of the solution layer include: the number of photoresist coating machines at different board temperatures, the status of the photoresist coating machines, the required board temperature for the process, the number of processes under the process, the process sequence, the temperature transition process loss, market demand, product delivery time, and product priority; among which, the machine-related indicators include: the number of photoresist coating machines at different board temperatures and the status of the photoresist coating machines; the production-related indicators include: the required board temperature for the process, the number of processes under the process, the process sequence, and the temperature transition process loss; the product-related indicators include: market demand, product delivery time, and product priority.

[0017] Accordingly, the present invention also provides a storage medium storing computer instructions, which, when executed, perform the steps of the method described in any of the above technical solutions.

[0018] Accordingly, the present invention also provides a terminal, including a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and the processor executes the steps of the method described in any of the above technical solutions when executing the computer instructions.

[0019] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0020] In the equipment resource allocation method of the photoresist coating machine in this invention, each product consists of hundreds or even thousands of processes. A semiconductor factory has dozens of products, resulting in tens of thousands of processes. Each process has different requirements for the board temperature of the photoresist coating machine. Moreover, the board temperature in the photoresist coating machine has a significant impact on the equipment efficiency, and the board temperature is related to the photolithography process. Therefore, the photolithography process is used as the classification object, and by implementing gradient hierarchical control of the photolithography process, the resource allocation of the photoresist coating machine among different products and processes is rationally guided according to the supply and demand of the board temperature of the photoresist coating machine, so as to achieve the lowest loss and highest efficiency. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the equipment resource allocation method for the photoresist coating machine in an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of a multi-level structure model in an embodiment of the present invention. Detailed Implementation

[0023] As described in the background section, existing photoresist coating machines still have many problems in terms of equipment resource allocation. These will be explained in detail below.

[0024] The performance of a photoresist coating machine directly affects the quality and production efficiency of the photolithography process, and board temperature is a key parameter. During photoresist coating, the board temperature directly affects the adhesion, drying time, and uniformity of the photoresist. Excessive temperature may increase the fluidity of the photoresist, affecting coating uniformity, while excessively low temperature may increase the viscosity of the photoresist, resulting in uneven coating or the formation of bubbles. Precise board temperature control can ensure the stability of the photolithography process, significantly improving the efficiency and wear of the photoresist coating machine by minimizing the impact of temperature variations on the process.

[0025] The board temperature for photoresist coating machines is related to the photolithography process. Generally, different processes require processing at specific board temperatures, and different board temperatures result in different production losses. Due to changes in market demand, product capacity fluctuates, and different products have different processes, leading to varying demands for photoresist coating machines at different temperatures. Currently, semiconductor factories allocate photoresist coating machines solely based on product delivery cycles, resulting in uneven machine utilization, with some machines idle or fully loaded. In the event of urgent orders, production may be sacrificed in terms of yield, leading to temperature-related losses and further uneven utilization of photoresist coating machines, resulting in significant losses.

[0026] Based on this, the present invention provides a method for allocating equipment resources for a photoresist coating machine, a storage medium, and a terminal. Each product consists of hundreds or even thousands of processes, and a semiconductor factory has dozens of products, resulting in tens of thousands of processes. Each process has different requirements for the board temperature of the photoresist coating machine. Moreover, the board temperature in the photoresist coating machine has a significant impact on the equipment efficiency, and the board temperature is related to the photolithography process. Therefore, the photolithography process is used as the classification object, and by implementing gradient hierarchical control of the photolithography process, the resource allocation of the photoresist coating machine among different products and processes is rationally guided according to the supply and demand of the board temperature of the photoresist coating machine, so as to achieve the lowest loss and highest efficiency.

[0027] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 This is a flowchart illustrating the equipment resource allocation method for a photoresist coating machine in an embodiment of the present invention, including:

[0029] Step S101: Construct a multi-level structural model based on the photolithography process. The multi-level structural model includes a target layer, a criterion layer, and a scheme layer.

[0030] Step S102: Construct a judgment matrix for the multi-layered structural model based on the analytic hierarchy process (AHP).

[0031] Step S103: Obtain the subjective weight and objective weight of the criterion layer on the target layer based on the judgment matrix;

[0032] Step S104: Based on the subjective weight and the objective weight, obtain the comprehensive weight of the criterion layer on the target layer;

[0033] Step S105: Perform gradient classification of the photolithography process based on the comprehensive weight to guide the allocation of equipment resources of the photoresist coating machine.

[0034] The following section, in conjunction with the accompanying drawings, provides a detailed explanation of each step in the equipment resource allocation method for the photoresist coating machine.

[0035] Figure 2 This is a schematic diagram of a multi-level structure model in an embodiment of the present invention.

[0036] Please continue to refer to this. Figure 1 and in conjunction with references Figure 2 In step S101, a multi-level structural model is constructed based on the photolithography process. The multi-level structural model includes a target layer, a criterion layer, and a scheme layer.

[0037] In this embodiment, the indicators of the target layer include: process rational classification indicators.

[0038] In this embodiment, the indicators of the criterion layer include: machine-related indicator A, production-related indicator B, and product-related indicator C; wherein, the process rational classification indicators include: machine-related indicator A, production-related indicator B, and product-related indicator C.

[0039] In this embodiment, the indicators of the scheme layer include: the number of photoresist coating machines at different board temperatures (A1), the machine status of the photoresist coating machine (A2), the required board temperature (B1), the number of processes under the process (B2), the process processing sequence (B3), the temperature transition process loss (B4), market demand (capacity) (C1), product delivery time (C2), and product priority (C3). Among them, the machine-related indicators A include: the number of photoresist coating machines at different board temperatures (A1) and the machine status of the photoresist coating machine (A2); the production-related indicators B include: the required board temperature (B1), the number of processes under the process (B2), the process processing sequence (B3), and the temperature transition process loss (B4); the product-related indicators C include: market demand (C1), product delivery time (C2), and product priority (C3).

[0040] Please continue to refer to this. Figure 1 In step S102, a judgment matrix is ​​constructed for the multi-layer structure model based on the analytic hierarchy process.

[0041] In this embodiment, the method for constructing a judgment matrix for the multi-layered structural model based on the analytic hierarchy process includes: using a 1-9 comparison scale and a pairwise comparison method to construct a pairwise comparison array for each factor index in the scheme layer. For details of the 1-9 comparison scale evaluation table, please refer to Table 1.

[0042] Factor i compared to factor j Quantized value Equally important 1 Slightly important 3 Stronger and more important 5 Strongly important 7 Extremely important 9 reciprocal <![CDATA[a ij =a ji ]]>

[0043] Table 1

[0044] Please refer to Table 2. In this embodiment, a pairwise comparison array is constructed using the various factor indicators in the scheme layer. The values ​​in Table 2 are based on human judgment and are filled in with reference to the 1-9 comparison scale evaluation table.

[0045] Solution layer A1 A2 B1 B2 B3 B4 C1 C2 C3 A1 1 5 1 / 9 3 1 / 5 1 / 5 1 / 3 3 5 A2 1 / 5 1 1 / 9 1 / 5 1 / 7 1 / 7 1 / 5 5 7 B1 9 9 1 3 9 1 / 3 1 / 3 5 7 B2 1 / 3 5 1 / 3 1 1 / 7 1 / 7 1 / 5 1 / 3 1 / 3 B3 5 7 1 / 9 7 1 3 7 5 5 B4 5 7 3 7 1 / 3 1 5 3 3 C1 3 5 3 5 1 / 7 1 / 5 1 3 1 / 5 C2 1 / 3 1 / 5 1 / 5 3 1 / 5 1 / 3 1 / 3 1 1 / 7 C3 1 / 5 1 / 7 1 / 7 3 1 / 5 1 / 3 5 7 1

[0046] Table 2

[0047] Please continue to refer to this. Figure 1 In step S103, the subjective weight F of the criterion layer to the target layer is obtained based on the judgment matrix. i And objective weight W.

[0048] In this embodiment, the method for obtaining the subjective weight of the criterion layer to the target layer based on the judgment matrix includes: comparing the indicators of the scheme layer in the judgment matrix pairwise; normalizing the eigenvalues ​​of each feature vector in the judgment matrix to obtain the subjective weight of the criterion layer to the target layer.

[0049] Please refer to Table 3. In this embodiment, the subjective weight F of each indicator is obtained based on Table 2. i .

[0050]

[0051]

[0052] Table 3

[0053] In this embodiment, when obtaining the subjective weight F i Then, the subjective weight F i A consistency check is performed to determine the reasonableness of the results. Specifically, if the consistency ratio is less than or equal to 0.1, then the obtained subjective weight F is considered acceptable. i Reasonable.

[0054] In this embodiment, the method for obtaining the objective weight W of the criterion layer to the target layer based on the judgment matrix includes: standardizing the judgment matrix using the entropy weight method to obtain the score proportion of each indicator in the judgment matrix; obtaining the information entropy of each indicator based on the score proportion of each indicator in the judgment matrix; and obtaining the objective weight of each indicator based on the information entropy of each indicator.

[0055] Obtain the score percentage P for each indicator in the judgment matrix. ij For: P ij =R ij / ∑R; where P ij Let represent the proportion of the i-th evaluation indicator for the j-th evaluation site to the total weight of that indicator.

[0056] Obtain the information entropy E for each indicator i for:

[0057] The objective weight W for each indicator is obtained as follows:

[0058] Please refer to Table 4 for the objective weights of each indicator obtained from Table 2 in this embodiment.

[0059]

[0060]

[0061] Table 4

[0062] Please continue to refer to this. Figure 1 In step S104, based on the subjective weight F i Together with the objective weight W, obtain the comprehensive weight Z of the criterion layer on the target layer.

[0063] In this embodiment, the method for obtaining the comprehensive weight Z of the criterion layer to the target layer includes: respectively assigning weights F to the subjective weights F. i The objective weight W is used to reassign weights, thus obtaining the comprehensive weight. That is, the comprehensive weight Z = k * F. i +(1-k)*W; where k depends on the decision-maker's preference for subjective experience and objective weights. The data for subjective decision-making comes from the factory's historical experience data, which has a high degree of credibility. Usually, k is set to 0.7.

[0064] Please refer to Table 5. In this embodiment, the comprehensive weight of each indicator is obtained based on Tables 3 and 4.

[0065]

[0066]

[0067] Table 5

[0068] Please continue to refer to this. Figure 1 In step S105, the photolithography process is graded based on the comprehensive weight to guide the allocation of equipment resources of the photoresist coating machine.

[0069] Each product consists of hundreds or even thousands of processes. A semiconductor factory may have dozens of products, resulting in tens of thousands of processes. Each process has different requirements for the board temperature of the photoresist coating machine. Moreover, the board temperature in the photoresist coating machine has a significant impact on the equipment efficiency, and the board temperature is related to the photolithography process. Therefore, this paper uses the photolithography process as a classification object and implements gradient-level control of the photolithography process. Based on the supply and demand of the board temperature of the photoresist coating machine, the resource allocation of the photoresist coating machine among different products and processes is rationally guided to achieve the lowest loss and highest efficiency.

[0070] In this embodiment, the method for gradient classification of lithography processes based on the comprehensive weights includes: processing the comprehensive weights using the Pareto classification method to obtain the gradient level of the lithography processes.

[0071] Based on the comprehensive weights calculated in Table 5, and considering multiple influencing factors in actual photolithography production, the complex processes are categorized according to factors such as board temperature requirements, processing sequence, and market capacity, with weights decreasing progressively. Furthermore, based on the proportion of processes and following the Pareto classification principle, tens of thousands of processes are divided into three categories according to their weights: Key Category A, Normal Category B, and General Category C, for tiered management. Category A (approximately 20%) consists of products with high temperature loss, those in the early stages of the process, and those with high capacity requirements; Category B (approximately 70%) consists of products with moderate temperature loss and moderate capacity requirements; and Category C (approximately 10%) consists of blank wafers or R&D wafers. This categorization, while ensuring that processes with the same board temperature are grouped together, further categorizes processes according to capacity requirements, delivery cycle, and temperature loss to rationally guide the allocation of photolithography coating machine resources and ensure full utilization of machine resources.

[0072] Accordingly, this embodiment of the invention also provides a storage medium storing computer instructions, which, when executed, perform the steps of the method described in any of the above embodiments.

[0073] Accordingly, this embodiment of the invention also provides a terminal, including a memory and a processor, wherein the memory stores computer instructions that can run on the processor, and the processor executes the steps of the method described in any of the above embodiments when running the computer instructions.

[0074] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for allocating equipment resources for a photoresist coating machine, characterized in that, include: A multi-level structural model is constructed based on the photolithography process. The multi-level structural model includes a target layer, a criterion layer, and a scheme layer. A judgment matrix is ​​constructed for the multi-layered structural model based on the analytic hierarchy process. Based on the judgment matrix, obtain the subjective and objective weights of the criterion layer on the target layer; Based on the subjective weights and the objective weights, the comprehensive weight of the criterion layer on the target layer is obtained; The photolithography process is graded based on the comprehensive weight to guide the allocation of equipment resources for the photoresist coating machine.

2. The equipment resource allocation method for the photoresist coating machine as described in claim 1, characterized in that, The method for constructing a judgment matrix for the multi-layered structural model based on the analytic hierarchy process includes: using a 1-9 comparison scale and a pairwise comparison method to construct a pairwise comparison array for each factor index in the scheme layer.

3. The equipment resource allocation method for the photoresist coating machine as described in claim 1, characterized in that, The method for obtaining the subjective weight of the criterion layer to the target layer based on the judgment matrix includes: comparing the indicators of the scheme layer in the judgment matrix pairwise; normalizing the eigenvalues ​​of each feature vector in the judgment matrix to obtain the subjective weight of the criterion layer to the target layer.

4. The equipment resource allocation method for the photoresist coating machine as described in claim 3, characterized in that, After obtaining the subjective weights, a consistency check is performed on the subjective weights to determine whether the results are reasonable.

5. The equipment resource allocation method for the photoresist coating machine as described in claim 4, characterized in that, When the consistency ratio is less than or equal to 0.1, the obtained subjective weight is considered reasonable.

6. The equipment resource allocation method for the photoresist coating machine as described in claim 1, characterized in that, The method for obtaining the objective weight of the criterion layer to the target layer based on the judgment matrix includes: standardizing the judgment matrix using the entropy weight method to obtain the score ratio of each indicator in the judgment matrix; obtaining the information entropy of each indicator based on the score ratio of each indicator in the judgment matrix; and obtaining the objective weight of each indicator based on the information entropy of each indicator.

7. The equipment resource allocation method for the photoresist coating machine as described in claim 1, characterized in that, The method for obtaining the comprehensive weight of the criterion layer to the target layer includes: redistributing the subjective weight and the objective weight respectively to obtain the comprehensive weight.

8. The equipment resource allocation method for the photoresist coating machine as described in claim 1, characterized in that, The method for gradient classification of photolithography processes based on the comprehensive weights includes: processing the comprehensive weights using the Pareto classification method to obtain the gradient levels of the photolithography processes.

9. The equipment resource allocation method for the photoresist coating machine as described in claim 1, characterized in that, The indicators of the target layer include: process rational classification indicators.

10. The equipment resource allocation method for the photoresist coating machine as described in claim 9, characterized in that, The criteria layer includes: machine-related indicators, production-related indicators, and product-related indicators; among which, the reasonable process classification indicators include: machine-related indicators, production-related indicators, and product-related indicators.

11. The equipment resource allocation method for the photoresist coating machine as described in claim 10, characterized in that, The indicators at the solution layer include: the number of photoresist coating machines at different board temperatures, the machine status of the photoresist coating machines, the required board temperature for the process, the number of processes under the process, the process sequence, the temperature transition process loss, market demand, product delivery time, and product priority. Among these, the machine-related indicators include: the number of photoresist coating machines at different board temperatures and the machine status of the photoresist coating machines; the production-related indicators include: the required board temperature for the process, the number of processes under the process, the process sequence, and the temperature transition process loss; and the product-related indicators include: market demand, product delivery time, and product priority.

12. A storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed, they perform the steps of the method according to any one of claims 1 to 11.

13. A terminal comprising a memory and a processor, wherein the memory stores computer instructions capable of running on the processor, characterized in that, When the processor executes the computer instructions, it performs the steps of the method according to any one of claims 1 to 11.

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