Process control system and process control method

Abstract

The invention provides a process control system and a process control method. In the process control system, a prediction module is used for forming a prediction value for a controlled parameter before a current batch of wafers execute a controlled process, and a prediction value control module is used for judging whether the prediction value is larger than an upper limit value or smaller than a lower limit value and controlling the current batch of wafers when the prediction value exceeds the range. The card control boundary module at least comprises a dynamic boundary unit, and the dynamic boundary unit is used for calculating an EWMA mean value mu and a Gaussian distribution standard deviation sigma according to predicted values corresponding to a plurality of batches of wafers in recent periods and actual measurement value data, taking mu + n sigma as an upper limit value, taking mu-n sigma as a lower limit value, and calculating the EWMA mean value mu and the Gaussian distribution standard deviation sigma; the dynamic upper limit value and the dynamic lower limit value can change according to the predicted values corresponding to multiple batches of wafers recently and the actual measured value data, and the problems that standards are not uniform and flexibility and stability are poor when whether the predicted values are abnormal or not is judged only through a manually-set card control boundary can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor, and in particular to a process control system and a process control method. BACKGROUND

[0002] In semiconductor manufacturing, in order to improve chip yield and production efficiency, a prediction value is formed according to historical data before a current batch of wafers is processed (for example, an exposure energy prediction value is formed before an exposure process is performed), when the prediction value does not exceed a preset control boundary, the prediction value can be used as the value of the corresponding condition parameter (i.e. the delivery value) when the corresponding process is performed on the current batch of wafers, when the prediction value exceeds the preset control boundary, it indicates that the prediction value (i.e. the current delivery value) is abnormal, at this time, the corresponding process is suspended for the current batch of wafers, and the parameter conditions and measurement processes before the process are checked by the staff, and are corrected as needed.

[0003] The prior art usually uses an APC (Advanced Process Control) system to perform the above prediction and control on its control object. For example, the APC system can provide an exposure energy prediction value (i.e. a delivery energy value) for the exposure process to be performed on a batch of wafers currently arrived, if the prediction value exceeds the corresponding control boundary, it is determined that the prediction value is abnormal, thereby controlling the current batch of wafers to suspend the exposure process. For another example, the overlay error (OVL) test usually uses a multi-parameter model, the APC system can provide a prediction value (i.e. an OVL compensation value) of at least part of the parameters for the overlay error (OVL) test to be performed on a batch of wafers currently arrived after exposure, before the exposure process is performed, if the prediction value exceeds the corresponding control boundary, it is determined that the prediction value is abnormal, thereby controlling the current batch of wafers to suspend the exposure process. It can be understood that the control boundary used to determine whether the prediction value is abnormal cannot be too loose, otherwise it will not play a role in determining abnormality and controlling, and it cannot be too strict, otherwise it will easily cause frequent and unreasonable control.

[0004] However, the control boundary currently used by the APC system is set by engineers according to experience, and lacks unified standards, and the control boundary often needs to be adjusted according to the running of the goods, for example, after the exposure machine has been running for a period of time, the exposure energy will drift but will not affect the CD (critical dimension) after exposure, at this time, the prediction value provided by the APC system will also have a certain deviation, if the control boundary is not adjusted in time, unnecessary control will be caused, for another example, when the OVL measurement value is normal, the prediction value deviation does not need to be considered abnormal to cause control. However, the control boundary is frequently modified by engineers, which consumes manpower. It can be seen that only manually setting the control boundary to monitor whether the prediction value is abnormal lacks unified standards, and the flexibility and stability of the adjustment of the control boundary are poor. Summary of the Invention

[0005] To avoid the above problems and improve the flexibility and stability of the control boundary adjustment, this invention provides a process control system and a process control method.

[0006] On one hand, the present invention provides a process control system, the process control system comprising:

[0007] The prediction module is used to generate predicted values ​​for the controlled parameters before the controlled process is executed on the current batch of wafers;

[0008] A prediction value control module is used to determine whether the predicted value is greater than an upper limit or less than a lower limit. If so, it controls the current batch of wafers, pausing the execution of the controlled process; otherwise, it allows the current batch of wafers to be shipped to execute the controlled process, using the predicted value as the value of the controlled parameter.

[0009] A boundary control module is used to provide the upper limit value and the lower limit value. The boundary control module includes at least a dynamic boundary unit. The dynamic boundary unit is used to calculate the EWMA mean, Gaussian standard deviation, and μ ± nσ based on the predicted values ​​and actual measurement values ​​corresponding to multiple recent batches of wafers. μ + nσ is used as the dynamic upper limit value, and μ - nσ is used as the dynamic lower limit value. μ represents the EWMA mean, σ represents the Gaussian standard deviation, and n is a positive integer.

[0010] Optionally, the card control boundary module further includes a fixed boundary unit, which is used to obtain a custom upper limit and a custom lower limit from external input, and use them as the custom upper limit value and the custom lower limit value, respectively.

[0011] Optionally, the card control boundary module includes:

[0012] A mode selection unit is configured to acquire external instructions and accordingly employ at least one of the dynamic boundary unit and the fixed boundary unit to provide the upper limit value and the lower limit value; and

[0013] The multiplier selection unit is used to obtain external instructions to get the specific value of n, so that the dynamic boundary unit can calculate the specific value of μ ± nσ.

[0014] Optionally, the card control boundary module includes:

[0015] The first parameter setting unit is used to acquire the first time period and the number of first batches from external input, so that the dynamic boundary unit can determine whether the number of batches successfully delivered and executed by the controlled process within the first time period before the current batch of wafers has reached the number of first batches before calculating the EWMA mean and the Gaussian standard deviation; and only when the number of first batches is reached will the calculation of the EWMA mean and the Gaussian standard deviation begin; and

[0016] The second parameter setting unit is used to set the second time period and the second batch number, so that when the dynamic boundary unit calculates the EWMA mean and the Gaussian distribution standard deviation, it selects the predicted value and actual measurement value data corresponding to the second batch number of wafers in the second time period before the current batch of wafers for calculation.

[0017] Optionally, the controlled process is an exposure process, and the controlled parameter is exposure energy; or, the controlled process is an exposure process, and the controlled parameter is a model parameter used for overlay error testing performed after the exposure process.

[0018] On the other hand, the present invention provides a process control method, employing the above-mentioned process control system, the process control method comprising:

[0019] After the current batch of wafers arrives at the station and before the controlled process is executed, predictive values ​​are generated for the controlled parameters.

[0020] Select at least one of the dynamic boundary unit and the fixed boundary unit, and provide an upper limit value and a lower limit value; and

[0021] If the predicted value is greater than the upper limit or less than the lower limit, the current batch of wafers is stopped from executing the controlled process. If not, the current batch of wafers is shipped to execute the controlled process, and the predicted value is used as the value of the controlled parameter.

[0022] Optionally, when the dynamic boundary unit and the fixed boundary unit are selected to provide the upper limit value and the lower limit value, the fixed boundary unit is first used to provide the custom upper limit value and the lower limit value. If the predicted value does not exceed the range formed by the custom upper limit value and the lower limit value, the dynamic boundary unit is then used to provide the dynamic lower limit value and the lower limit value. It is then determined whether the predicted value exceeds the range formed by the dynamic upper limit value and the lower limit value. If the predicted value exceeds the range formed by the custom upper limit value and the lower limit value provided by the fixed boundary unit, the current batch of wafers is controlled, and the dynamic boundary unit is no longer used to provide the dynamic lower limit value and the lower limit value.

[0023] Optionally, when the dynamic boundary unit is used to provide the dynamic lower limit value and the lower limit value, the process control method includes:

[0024] Obtain the first time period and the first batch of counts from the external input; and

[0025] Determine whether the number of batches successfully shipped and executed by the controlled process within the first time period prior to the current batch of wafers has reached the number of the first batch. If yes, the conditions for using the dynamic upper and lower limits are met, and the EWMA mean, Gaussian distribution standard deviation, and μ ± nσ are calculated based on the predicted values ​​and actual measurement data corresponding to multiple recent batches of wafers. If no, it indicates that the conditions for using the dynamic upper and lower limits are not met, and relevant information is output.

[0026] Optionally, calculating the EWMA mean, the Gaussian distribution standard deviation, and μ ± nσ includes:

[0027] Obtain the second time period, the second batch number, and the specific value of n from the external input; and

[0028] The predicted value and actual measurement value data of the second batch of wafers within the second time period before the current batch of wafers are selected to calculate the EWMA mean, the Gaussian distribution standard deviation, and μ ± nσ. μ + nσ is used as the dynamic upper limit value, and μ - nσ is used as the dynamic lower limit value.

[0029] Optionally, if the number of batches successfully shipped and executed with the controlled process within the second time period prior to the current batch of wafers does not reach the second batch number, the corresponding predicted value and actual measurement value data are obtained from all batches of wafers successfully shipped and executed with the controlled process within the second time period, and the EWMA mean μ, Gaussian distribution standard deviation σ, and μ ± nσ are calculated. μ + nσ is used as the dynamic upper limit value, and μ - nσ is used as the dynamic lower limit value.

[0030] Optionally, the controlled process is an exposure process, and the controlled parameter is exposure energy; or, the controlled process is an exposure process, and the controlled parameter is a model parameter used for overlay error testing performed after the exposure process.

[0031] In the process control system provided by this invention, the control boundary module includes at least a dynamic boundary unit. The dynamic boundary unit can calculate the EWMA mean (μ), Gaussian standard deviation (σ), and μ ± nσ based on the predicted values ​​and actual measurement data corresponding to multiple recent batches of wafers. The upper limit value is set as μ + nσ, and the lower limit value is set as μ - nσ. The dynamic upper and lower limits change according to the changes in the predicted values ​​and actual measurement data corresponding to multiple recent batches of wafers. This avoids the problems of inconsistent standards, poor flexibility and stability when judging whether the predicted value is abnormal by relying solely on manually set control boundaries. It facilitates flexible and stable monitoring of the predicted value, helps reduce unnecessary control, and reduces manpower consumption.

[0032] The process control method provided by this invention employs the aforementioned process control system, wherein at least one of the dynamic boundary unit and the fixed boundary unit is selected to provide upper and lower limits. When the predicted value is greater than the upper limit or less than the lower limit, the current batch of wafers is controlled, causing it to suspend the execution of the controlled process. This allows the user to check and correct the relevant settings, helping to avoid the problems of inconsistent standards, poor flexibility and stability when judging whether the predicted value is abnormal solely by manually set control boundaries, thereby improving the flexibility and stability of the control process. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the structure of a process control system according to an embodiment of the present invention.

[0034] Figure 2 This is a partial schematic diagram of the interface of the input / output module of a process control system according to an embodiment of the present invention.

[0035] Figure 3 This is a schematic diagram of the steps of a process control method according to an embodiment of the present invention.

[0036] Figure 4 This is a flowchart of a process control method using exposure process as the controlled process in one embodiment of the present invention.

[0037] Figure 5 This is a flowchart of a process control method using overlay error testing as the controlled process in another embodiment of the present invention. Detailed Implementation

[0038] The process control system and process control method of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the accompanying drawings are in a very simplified form and use non-precise scales, and are only used to facilitate clarity in illustrating the embodiments of the present invention. Terms such as "first," "second," etc., are used to distinguish between similar elements and are not necessarily used to describe a specific order or temporal sequence. It should be understood that these terms can be replaced where appropriate, for example, to allow the embodiments of the present invention described herein to operate in a different order than that described or shown herein. If the methods described herein comprise a series of steps, and the order of these steps presented herein is not necessarily the only possible order in which these steps can be performed, some of the described steps may be omitted and / or some other steps not described herein may be added to the method.

[0039] Reference Figure 1 Some embodiments of the present invention relate to a process control system 100. The process control system 100 can control at least one process in semiconductor manufacturing. The process control system 100 is, for example, an APC (Advanced Process Control) system, which is compatible with the functions of existing APC systems. In some embodiments, the process control system 100 can also be a standalone system or integrated with other process systems. The process control system 100 can be implemented using a computer program; however, all or part of it can also be implemented using hardware circuitry, for example, implemented as a computer program in conjunction with an apparatus or device, wherein the computer program can be stored in various readable storage media for processor execution.

[0040] In this embodiment of the invention, the process controlled by the process control system 100 (i.e., the controlled process) is, for example, a manufacturing process (such as an exposure process or a CMP process). The process control system 100 may have a database that stores predicted values ​​of different batches of wafers before the execution of the controlled process and historical data generated during the execution of the controlled process. Based on the historical data, the system can predict the monitored process conditions in the controlled process, generating corresponding predicted values. By determining whether the predicted values ​​are abnormal, the system decides whether to ship the current batch of wafers to execute the controlled process or to hold the current batch of wafers to suspend the execution of the controlled process. After being held, the process control system 100 can output relevant predicted values ​​and holding information to facilitate staff inspection and adjustment of relevant settings.

[0041] Reference Figure 1 According to an embodiment of the present invention, the process control system 100 includes a prediction module 110, a prediction value control module 120, and a control boundary module 130.

[0042] The prediction module 110 is used to generate predicted values ​​for the controlled parameters before the controlled process is performed on the current batch of wafers.

[0043] The current batch of wafers refers to a batch of wafers that are scheduled to undergo a controlled process but have not yet been executed, such as a current lot of wafers. In this embodiment, before executing the corresponding controlled process, the prediction module 110 first selects the process conditions used in the controlled process as the controlled parameters and forms corresponding predicted values. For example, the prediction module 110 can obtain historical shipment data from the database of multiple batches of wafers preceding the current batch (preferably multiple batches of wafers arriving at the station in time adjacent to the current batch) during the prediction and execution of the controlled process. This data is processed using a suitable method to form the values ​​of the controlled parameters preferably adopted for executing the controlled process for the current batch of wafers, and predictive feedback is performed to form the predicted values. The prediction module 110 can form the predicted values ​​according to known methods (such as weighted arithmetic average or other methods).

[0044] As an example, in one embodiment, the controlled process to be performed on the current batch of wafers is an exposure process. The prediction module 110 can predict the exposure energy used in the exposure process before the current batch of wafers is subjected to the exposure process, forming a corresponding predicted exposure energy value. In another embodiment, the controlled process to be performed on the current batch of wafers is an exposure process. The prediction module 110 can predict the values ​​of at least some parameters of the model used for the overlay error test performed after the exposure process before the current batch of wafers is subjected to the exposure process, forming a corresponding predicted model parameter value. As an example, the model used for the overlay error test is a 10-para model.

[0045] The prediction value control module 120 is used to determine whether the predicted value obtained by the prediction module 110 is greater than an upper limit or less than a lower limit. If so, it indicates that the predicted value is abnormal, and the current batch of wafers is controlled, suspending the execution of the controlled process (i.e., it cannot be shipped). If not, it indicates that the predicted value is normal, and the current batch of wafers is shipped to execute the controlled process, and the predicted value is used as the value of the controlled parameter. For example, when the above-mentioned exposure energy predicted value is not greater than the corresponding upper limit of energy value and not lower than the lower limit of energy value, it indicates that the exposure energy predicted value is normal, and the predicted value can be used as the exposure energy shipping value for the current batch of wafers to execute the current exposure process. When the above-mentioned exposure energy predicted value is greater than the corresponding upper limit of energy value or lower than the lower limit of energy value, it indicates that the exposure energy predicted value is abnormal, and control is required to suspend the current batch of wafers from executing the current exposure process.

[0046] The card control boundary module 130 is used to provide the upper limit value and the lower limit value.

[0047] In this embodiment, the control boundary module 130 includes at least a dynamic boundary unit 131. The dynamic boundary unit 131 is used to calculate the EWMA (Exponentially Weighted Moving Average) mean, Gaussian standard deviation, and μ ± nσ based on the predicted and actual measurement data corresponding to multiple recent batches of wafers. μ + nσ is used as the dynamic upper limit value, and μ - nσ is used as the dynamic lower limit value, where μ represents the EWMA mean, σ represents the Gaussian standard deviation, and n is a positive integer. The "multiple recent batches of wafers" refers to multiple batches of wafers that arrived before and are adjacent to the current batch of wafers. The predicted and actual measurement data corresponding to these multiple recent batches of wafers are stored, for example, in the database of the process control system 100. As the current batch of wafers changes, the selectable predicted and actual measurement data corresponding to the multiple recent batches of wafers will also change. Therefore, the upper and lower limits formed by the dynamic boundary unit 131 are dynamic, i.e., a dynamic boundary is formed.

[0048] EWMA is a method that calculates the average value by assigning different weights to historical data values, and uses this average value as the basis for determining the predicted value. Its calculation principle is shown in equation (1):

[0049] V t = V t−1 *(1-β) + θ t * β (1)

[0050] In equation (1), V t V represents the weighted average order value of the first t batches of wafers arranged in the order of execution of the controlled process within a series of consecutive batches. This weighted average order value can be used as the optimal order value for the first t batches of wafers; t−1 This represents the weighted average order value for the first t-1 batches of wafers arranged in the order of execution of the controlled process, among the multiple batches of wafers. This weighted average order value can be used as the optimal order value for the first t-1 batches of wafers; θ t θ represents the optimal shipment value of the t-th batch of wafers arranged in the order of execution of the controlled process among multiple batches of wafers. t The measurements were taken during the controlled process execution of the t-th batch of wafers. β represents the weighting parameter, and t is an integer greater than 1.

[0051] As an example, five batches were processed by the controlled process. According to the order in which the controlled process was executed, the five batches are denoted as A, B, C, D, and E, respectively. The optimal delivery values ​​for batches A to E are a, b, c, d, and e, respectively. Let β = 0.6. After batches A and B have been delivered and the actual delivery values ​​have been obtained, when obtaining the delivery parameters for batch C, the weighted average value V2 of the delivery values ​​of batches A and B needs to be calculated. When obtaining the delivery parameters for batch D, the weighted average value V3 of the delivery values ​​of batches A, B, and C needs to be calculated. When obtaining the delivery parameters for batch E, the weighted average value V4 of the delivery values ​​of batches A, B, C, and D needs to be calculated. V2, V3, and V4 are calculated by the following equations (2), (3), and (4), respectively:

[0052] V2 = 0.6*b + 0.4*a (2)

[0053] V3= 0.6*c + 0.4*(0.6*b + 0.4*a) (3)

[0054] V4= 0.6*d + 0.4*(0.6*c + 0.4*(0.6*b + 0.4*a)) (4)

[0055] In this embodiment of the invention, by calculating V t The EWMA mean μ is obtained, i.e., μ = V t The number of batches t used to calculate the EWMA mean μ can be set as needed.

[0056] The standard deviation σ of the Gaussian distribution can measure the dispersion of the data, and it can be calculated by the following equation (5):

[0057] (5)

[0058] In equation (5), xi represents the data point of the goods value, μ represents the EWMA mean, and N is the total number of data points.

[0059] Based on the EWMA mean μ and the Gaussian distribution standard deviation σ, μ is increased by an integer multiple of σ to obtain the upper limit value for controlling the current batch of wafers (i.e., μ + nσ), and the lower limit value for controlling the current batch of wafers is obtained by removing the integer multiple of σ (i.e., μ - nσ). The specific value of the multiple n can be set as needed.

[0060] Reference Figure 1In some embodiments, the card control boundary module 130 may further include a fixed boundary unit 132. The fixed boundary unit 132 is used to obtain a custom upper limit and a custom lower limit from the external input, and use them as the custom upper limit value and the custom lower limit value, respectively. The upper limit value and the lower limit value obtained by the fixed boundary unit 132 remain unchanged when the external input value does not change, that is, a fixed boundary is formed. When determining whether the predicted value formed by the prediction module 110 exceeds the range formed by the upper limit value and the lower limit value, at least one of the dynamic boundary unit 131 and the fixed boundary unit 132 can be selected as needed to provide a dynamic boundary or a fixed boundary, or a fixed boundary can be provided first and then a dynamic boundary can be provided.

[0061] Reference Figure 1 and Figure 2 As an example, the card control boundary module 130 may include at least one of the following: mode selection unit 133, multiplier selection unit 134, first parameter setting unit 135, and second parameter setting unit 136.

[0062] The mode selection unit 133 is used to acquire external instructions, and accordingly employs at least one of the dynamic boundary unit 131 and the fixed boundary unit 132 to provide the upper limit value and the lower limit value. For example... Figure 2 As shown, the input / output interface of the process control system 100 has, for example, a drop-down menu 10, so that the user can select "dynamic boundary mode" using dynamic boundary unit 131, "solid boundary mode" using fixed boundary unit 132, and "hybrid mode" using dynamic boundary unit 131 and fixed boundary unit 132.

[0063] The multiplier selection unit 134 is used to obtain the specific value of the external input n, so that the dynamic boundary unit 131 can calculate the specific value of μ ± nσ. For example... Figure 2 As shown, the input / output interface has, for example, a drop-down menu 20, so that the user can select one of 3 to 8 as n, and the corresponding μ ± nσ can be calculated.

[0064] The first parameter setting unit 135 is used to acquire the first time period and the number of first batches from external input. This allows the dynamic boundary unit 131 to determine, before calculating the EWMA mean μ and the Gaussian standard deviation σ, whether the number of batches successfully shipped and processed by the controlled process within the first time period preceding the current batch of wafers has reached the number of first batches. Only when the number of first batches has been reached will the calculation of the EWMA mean and the Gaussian standard deviation begin. This avoids the accuracy of the calculation results being affected by an insufficient sample size when calculating the EWMA mean μ and the Gaussian standard deviation σ. The first time period and the number of first batches can be set as needed. Figure 2 As shown, the input / output interface has, for example, an input box 30 for inputting the first time period and the number of times in the first batch.

[0065] As an example, if the first time period is 7 days and the first batch consists of 30 batches, then before calculating μ and σ, the dynamic boundary unit 131 first determines whether 30 batches of wafers have been successfully shipped and the controlled process has been completed within the last 7 days (successful shipment means passing the predicted value detection; completing the controlled process means, for example, executing the exposure process and measuring CD (Critical Dimension) after exposure and receiving feedback of qualification, or executing the exposure process and measuring OVL after exposure and receiving feedback of qualification). If this is insufficient, it indicates that there is insufficient data to calculate the EWMA mean μ and the Gaussian distribution standard deviation σ, and information about the failure to meet the activation conditions of the dynamic boundary unit 131 can be output.

[0066] The second parameter setting unit 136 is used to set the second time period and the second batch number, so that when the dynamic boundary unit 131 calculates the EWMA mean μ and the Gaussian distribution standard deviation σ, it selects the predicted value and actual measurement value data corresponding to the second batch number of wafers within the second time period before the current batch of wafers for calculation. Figure 2 As shown, the input / output interface, for example, has input boxes 40 for inputting the second time period and the number of second batches. The second time period and the number of second batches can be set as needed, for example, 7 days and 30 batches respectively. The first time period and the second time period can be the same or different, and the number of first batches and the number of second batches can be the same or different. Figure 2 As shown, in order to obtain custom upper and lower limits when using fixed boundary unit 132, the input / output interface may also have input boxes 50 for inputting custom upper and lower limits.

[0067] At least one of the modules and units in the process control system 100 described in the above embodiments is generally implemented in the form of software programs in conjunction with hardware. However, all or part of them may also be implemented using electronic hardware or software programs. The modules and units may be combined into one module, or any one of the modules or units may be split into multiple units. Alternatively, at least some of the functions of one or more of these units may be combined with at least some of the functions of other units and implemented in one unit.

[0068] In the process control system 100 described in the above embodiment, the dynamic boundary unit 131 can calculate the EWMA mean (μ), Gaussian standard deviation (σ), and μ ± nσ based on the predicted values ​​and actual measurement data corresponding to multiple recent batches of wafers. μ + nσ is used as the upper limit of the dynamic boundary unit 131, and μ - nσ is used as the lower limit of the dynamic boundary unit 131. The upper and lower limits of the dynamic boundary unit 131 will change according to the changes in the predicted values ​​and actual measurement data corresponding to multiple recent batches of wafers. This avoids the problems of inconsistent standards, poor flexibility and stability when judging whether the predicted value is abnormal by relying solely on manually set control boundaries. This facilitates flexible and stable monitoring of the predicted value, reduces unnecessary control, and reduces manpower consumption.

[0069] Some embodiments of the present invention relate to a process control method that employs the process control system 100 described in the above embodiments.

[0070] Reference Figure 3 The process control method according to an embodiment of the present invention includes the following steps:

[0071] Step S1: After the current batch of wafers arrives at the station and before the controlled process is executed, predictive values ​​are generated for the controlled parameters.

[0072] Step S2: Select at least one of the above dynamic boundary unit 131 and fixed boundary unit 132, and provide upper limit value and lower limit value;

[0073] Step S3: Determine whether the predicted value is greater than the upper limit or less than the lower limit. If yes, control the current batch of wafers to suspend the execution of the controlled process. If no, allow the current batch of wafers to be shipped to execute the controlled process, and use the predicted value as the value of the controlled parameter.

[0074] In some embodiments, when dynamic boundary unit 131 and fixed boundary unit 132 are selected to provide upper and lower limits, the fixed boundary unit 132 is first used to provide custom upper and lower limits. If the predicted value does not exceed the range formed by the custom upper and lower limits, then the dynamic boundary unit 131 is used to provide dynamic lower limits. It is then determined whether the predicted value exceeds the range formed by the dynamic upper and lower limits. If the predicted value exceeds the range formed by the custom upper and lower limits provided by the fixed boundary unit 132, the current batch of wafers is controlled, and the dynamic lower limits are no longer provided by the dynamic boundary unit 131.

[0075] In some embodiments, step S2 selects to use only the fixed boundary unit 132 to form the upper and lower limits, or selects both the fixed boundary unit 132 and the dynamic boundary unit 131, but first uses the fixed boundary unit 132 to form a custom upper and lower limit. In this case, the custom upper and lower limits input from the outside are first obtained and used as the custom upper and lower limits, respectively. Then, the predicted value obtained in step S1 is compared with the custom set of upper and lower limits to determine whether the predicted value is greater than the custom upper limit or less than the custom lower limit. If so, the control is activated. For the current batch of wafers, pause the execution of the controlled process. If not, determine whether to use only the fixed boundary unit 132 mode (i.e., "fixed boundary mode") or a mode combining both fixed boundary unit 132 and dynamic boundary unit 131 (i.e., "hybrid mode"). If only the fixed boundary unit 132 mode is used, the current batch of wafers is shipped to execute the controlled process, and the predicted value is used as the value of the controlled parameter. If both the fixed boundary unit 132 and dynamic boundary unit 131 mode are used, the dynamic upper and lower limits formed by the dynamic boundary unit 131 are used for judgment.

[0076] Information about the current batch of wafers can be output to the output device to remind staff to check and modify the mode and process parameters used. After that, the control status can be released and the above steps can be repeated.

[0077] In some embodiments, step S2 selects only the dynamic boundary unit 131 to form dynamic upper and lower limits, or selects to first use the fixed boundary unit 132 and then the dynamic boundary unit 131 and is in the stage where the dynamic boundary unit 131 forms dynamic upper and lower limits. Forming dynamic upper and lower limits may include the following process: obtaining the first time period (e.g., day A) and the number of batches (e.g., batches B) from the external input. Then, historical data can be obtained from the system database to determine whether the number of batches that were successfully delivered (i.e., not blocked when monitoring the predicted value) and executed the controlled process in the first time period before the current batch of wafers has reached the number of batches. If so, the conditions for using dynamic upper and lower limits are met, and the EWMA mean μ, Gaussian distribution standard deviation σ, and μ ± nσ are calculated based on the predicted values ​​and actual measurement values ​​of multiple recent batches of wafers. If not, it indicates that the conditions for using dynamic upper and lower limits are not met and relevant information is output. Information can be output regarding the failure to meet the conditions for calculating the EWMA mean μ and the Gaussian distribution standard deviation σ to form the upper and lower limits, prompting the user to modify the mode or the values ​​of A and B. The method for calculating the EWMA mean μ and the Gaussian distribution standard deviation σ can be found in the description of the above embodiment.

[0078] Optionally, when the conditions for using the dynamic upper limit and lower limit are not met, a custom upper limit and a custom lower limit from external input are obtained and used as the custom upper limit and the custom lower limit, respectively.

[0079] As an example, calculating the EWMA mean μ, Gaussian standard deviation σ, and μ ± nσ may include the following process: obtaining the specific values ​​of the second time period (e.g., C days), the second batch number (e.g., D batches), and n from the external input; then, selecting the predicted and actual measurement data corresponding to the second batch number of wafers within the second time period before the current batch of wafers to calculate the EWMA mean μ, Gaussian standard deviation σ, and μ ± nσ, with μ + nσ as the dynamic upper limit and μ - nσ as the dynamic lower limit.

[0080] Optionally, if the number of batches successfully shipped and executed with the controlled process in the second time period prior to the current batch of wafers does not reach the second batch number, the corresponding predicted value and actual measurement value data are obtained from all batches of wafers successfully shipped and executed with the controlled process in the second time period, and the EWMA mean μ, Gaussian distribution standard deviation σ, and μ ± nσ are calculated. μ + nσ is used as the upper limit of the dynamic range, and μ - nσ is used as the lower limit of the dynamic range.

[0081] Figure 4 The flowchart illustrates a process control method using the exposure process as the controlled process. (Refer to...) Figure 4 As an example, when the current batch of wafers arrives and is about to undergo an exposure process, the process control system 100 is used to obtain and monitor the predicted exposure energy value before the exposure process is performed. Figure 5 The flowchart of a process control method with overlay error testing as the controlled process is shown, with reference to... Figure 5 As an example, when the current batch of wafers arrives and is about to undergo an exposure process, according to the process flow, an overlay error test must be performed after the exposure process. Before performing the exposure process, the process control system 100 obtains and monitors the predicted values ​​of at least some parameters of the model used for the overlay error test. Combined with... Figure 4 and Figure 5 As an example, the above process control method may specifically include the following procedures:

[0082] First, when the current batch of wafers arrives at the station, obtain the predicted exposure energy value or the predicted model parameter value;

[0083] Next, the pattern for obtaining monitoring and predicted values ​​is as follows: Figure 2As shown, the operator can select one of the following three modes to input on the initialization interface of the output end: "Dynamic Boundary Mode" (corresponding to dynamic boundary unit 131), "Fixed Boundary Mode" (corresponding to fixed boundary unit 132) and "Hybrid Mode" (corresponding to dynamic boundary unit 131 and fixed boundary unit 132).

[0084] When "Fixed Boundary Mode" or "Hybrid Mode" is selected, the system obtains the custom upper and lower energy limits from external input (which can be input by the engineer), or the custom upper and lower limits of the model parameters from external input (which can be input by the engineer). It then determines whether the predicted value is within the range formed by the corresponding upper and lower limits. If it is within the range and is not in hybrid mode, the current batch of wafers is not blocked, and the exposure process or overlay error test is performed, and the wafers are successfully shipped. If it is within the range and is in hybrid mode, the system switches to dynamic boundary unit 131 to form the upper and lower limits. If it is not within the range, the current batch of wafers is blocked, and the exposure process is paused. The system can then switch to dynamic boundary unit 131 to form the upper and lower limits.

[0085] When "Dynamic Boundary Mode" is selected or when "Dynamic Boundary Mode" is selected and the process redirects to Dynamic Boundary Unit 131 to form upper and lower limits, the system first obtains the first time period (e.g., A days) and the number of batches (e.g., B batches) from the external input. It then retrieves the total number of batches successfully delivered within the first time period from the database and determines whether the exposure process or overlay error test has successfully delivered B batches and completed the controlled process within the most recent A days (e.g., exposure completed and CD measured and returned as qualified after exposure, or exposure completed and OVL measured and returned as qualified after exposure). If not, the system controls the current batch of wafers and outputs information indicating that the dynamic boundary calculation enable condition is not met. The user can modify the mode or parameter conditions for forming the upper and lower limits and return to the mode selection step. If it is determined that the exposure process has successfully delivered B batches and completed the controlled process within the most recent A days, then the enable condition for calculating the dynamic upper and lower limits is met. The information indicating that the enable condition is met, along with the values ​​of A and B, can be recorded and stored.

[0086] After determining that the exposure process has successfully shipped B batches within the most recent A days, the system further obtains the second time period (e.g., C days) and the second batch number (e.g., D batches) from external input. It then retrieves and summarizes the number of batches successfully shipped within the most recent C days prior to the current batch of wafers from the database. The system determines whether D batches have been successfully shipped and the controlled process completed within the most recent C days. If so, it obtains the historical data corresponding to the D batches within C days and calculates the EWMA mean μ, Gaussian standard deviation σ, and μ ± nσ. If not, it obtains the historical data corresponding to all batches successfully shipped and the controlled process completed within C days and calculates the EWMA mean μ, Gaussian standard deviation σ, and μ ± nσ.

[0087] Next, it is determined whether the predicted exposure energy value or the predicted model parameter value is within the corresponding μ ± nσ range. If so, the current batch of wafers is shipped to perform the exposure process, i.e., the shipment is successful. The shipment success information, as well as the data of C days and D batches, can be stored. The EWMA mean μ, Gaussian distribution standard deviation σ, and μ ± nσ can also be output and displayed. If not, the current batch of wafers is paused from performing the exposure process, and relevant information is output, such as the predicted exposure energy value exceeding the energy range under the dynamic strategy.

[0088] use Figure 4 and Figure 5 The process shown allows for the acquisition of predicted exposure energy and / or predicted model parameters as needed before performing the exposure process. Accordingly, it determines whether the predicted exposure energy is greater than a corresponding upper limit or less than a corresponding lower limit, and / or whether the predicted model parameters are greater than a corresponding upper limit or less than a corresponding lower limit. If the predicted exposure energy is greater than the corresponding upper limit or less than the corresponding lower limit, or the predicted model parameters are greater than the corresponding upper limit or less than the corresponding lower limit, the current batch of wafers is stopped, pausing the exposure process.

[0089] It should be noted that the process control method described in the above embodiments is not only used for exposure processes, but can also be used for other suitable semiconductor processes. For example, in one embodiment, the controlled process is a CMP process, and the corresponding yield value and predicted value are for, for example, grinding time or grinding rate. Using the process control method, a grinding time prediction value or a grinding rate prediction value can be generated and it can be determined whether it exceeds the corresponding upper or lower limit value, thereby monitoring whether the grinding time prediction value or the grinding rate prediction value is abnormal, and controlling the current batch of wafers when an abnormality is determined.

[0090] The process control method described in the above embodiments can be converted into computer-executable instructions and stored in a memory (such as a hard disk, random access memory (RAM), external storage medium, storage device with communication lines, registers, etc.). When the executable instructions are executed by a processor (such as a central processing unit (CPU)), the process control method can be executed.

[0091] Using the process control method described in the above embodiments, at least one of the dynamic boundary unit 131 and the fixed boundary unit 132 can be selected as needed to form an upper limit value and a lower limit value. When the predicted value is greater than the upper limit value or less than the lower limit value, the current batch of wafers is controlled, causing it to suspend the execution of the controlled process. This allows the user to check the relevant settings and make corrections. It helps to avoid the problems of inconsistent standards, poor flexibility and stability when judging whether the predicted value is abnormal by relying solely on manually set control boundaries. It improves the flexibility and stability of the control process, helps to reduce unnecessary control, and reduces manpower consumption.

[0092] 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 and similar parts between the various embodiments can be referred to each other.

[0093] The above description is merely a description of preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A process control system, characterized in that, include: The prediction module is used to generate predicted values ​​for the controlled parameters before the controlled process is executed on the current batch of wafers; The prediction value control module is used to determine whether the predicted value is greater than an upper limit or less than a lower limit. If so, the current batch of wafers is controlled to suspend the execution of the controlled process. Otherwise, the current batch of wafers is shipped to execute the controlled process, and the predicted value is used as the value of the controlled parameter. as well as A boundary control module is used to provide the upper limit value and the lower limit value. The boundary control module includes at least a dynamic boundary unit. The dynamic boundary unit is used to calculate the EWMA mean, Gaussian standard deviation, and μ ± nσ based on the predicted values ​​and actual measurement values ​​corresponding to multiple recent batches of wafers. μ + nσ is used as the dynamic upper limit value, and μ - nσ is used as the dynamic lower limit value. μ represents the EWMA mean, σ represents the Gaussian standard deviation, and n is a positive integer.

2. The process control system as described in claim 1, characterized in that, The card control boundary module also includes: A fixed boundary unit is used to obtain a custom upper limit and a custom lower limit of the external input, and use them as the custom upper limit value and the custom lower limit value, respectively.

3. The process control system as described in claim 1, characterized in that, The card control boundary module includes: A mode selection unit is configured to acquire external instructions and accordingly employ at least one of the dynamic boundary unit and the fixed boundary unit to provide the upper limit value and the lower limit value; and The multiplier selection unit is used to obtain external instructions to get the specific value of n, so that the dynamic boundary unit can calculate the specific value of μ±nσ.

4. The process control system as described in claim 1, characterized in that, The card control boundary module includes: The first parameter setting unit is used to acquire the first time period and the number of first batches from external input, so that the dynamic boundary unit can determine whether the number of batches successfully delivered and executed by the controlled process within the first time period before the current batch of wafers has reached the number of first batches before calculating the EWMA mean and the Gaussian standard deviation; and only when the number of first batches is reached will the calculation of the EWMA mean and the Gaussian standard deviation begin; and The second parameter setting unit is used to set the second time period and the second batch number, so that when the dynamic boundary unit calculates the EWMA mean and the Gaussian distribution standard deviation, it selects the predicted value and actual measurement value data corresponding to the second batch number of wafers in the second time period before the current batch of wafers for calculation.

5. The process control system according to any one of claims 1 to 5, characterized in that, The controlled process is an exposure process, and the controlled parameter is the exposure energy; or, the controlled process is an exposure process, and the controlled parameter is the model parameter used for the overlay error test performed after the exposure process.

6. A process control method, characterized in that, The process control system described in any one of claims 2 to 5, wherein the process control method comprises: After the current batch of wafers arrives at the station and before the controlled process is executed, predictive values ​​are generated for the controlled parameters. Select at least one of the dynamic boundary unit and the fixed boundary unit, and provide an upper limit value and a lower limit value; and If the predicted value is greater than the upper limit or less than the lower limit, the current batch of wafers is stopped from executing the controlled process. If not, the current batch of wafers is shipped to execute the controlled process, and the predicted value is used as the value of the controlled parameter.

7. The process control method as described in claim 6, characterized in that, When the dynamic boundary unit and the fixed boundary unit are selected to provide the upper limit value and the lower limit value, the fixed boundary unit is first used to provide the custom upper limit value and the lower limit value. If the predicted value does not exceed the range formed by the custom upper limit value and the lower limit value, the dynamic boundary unit is then used to provide the dynamic lower limit value and the lower limit value. It is then determined whether the predicted value exceeds the range formed by the dynamic upper limit value and the lower limit value. If the predicted value exceeds the range formed by the custom upper limit value and the lower limit value provided by the fixed boundary unit, the current batch of wafers is controlled, and the dynamic boundary unit is no longer used to provide the dynamic lower limit value and the lower limit value.

8. The process control method as described in claim 6, characterized in that, When the dynamic boundary unit is used to provide the dynamic lower limit value and the lower limit value, the process control method includes: Obtain the first time period and the first batch of counts from the external input; and Determine whether the number of batches successfully shipped and executed by the controlled process within the first time period prior to the current batch of wafers has reached the number of the first batch. If yes, the conditions for using the dynamic upper and lower limits are met, and the EWMA mean, Gaussian distribution standard deviation, and μ ± nσ are calculated based on the predicted values ​​and actual measurement data corresponding to multiple recent batches of wafers. If no, it indicates that the conditions for using the dynamic upper and lower limits are not met, and relevant information is output.

9. The process control method as described in claim 8, characterized in that, When the conditions for using the dynamic upper and lower limits are not met, obtain the custom upper and lower limits from external input, and use them as the custom upper and lower limits, respectively.

10. The process control method as described in claim 6, characterized in that, The calculation of the EWMA mean, the Gaussian distribution standard deviation, and μ ± nσ includes: Obtain the second time period, the second batch number, and the specific value of n from the external input; and The predicted value and actual measurement value data corresponding to the number of wafers in the second batch within the second time period before the current batch of wafers are selected to calculate the EWMA mean, the Gaussian distribution standard deviation, and μ ± nσ. μ + nσ is used as the dynamic upper limit value, and μ - nσ is used as the dynamic lower limit value.

11. The process control method as described in claim 10, characterized in that, If the number of batches successfully shipped and processed by the controlled process within the second time period prior to the current batch of wafers does not reach the second batch number, then all batches of wafers successfully shipped and processed by the controlled process within the second time period are used to obtain the corresponding predicted value and actual measurement value data, and the EWMA mean μ, Gaussian distribution standard deviation σ, and μ ± nσ are calculated, with μ + nσ as the dynamic upper limit and μ - nσ as the dynamic lower limit.

12. The process control method according to any one of claims 6 to 11, characterized in that, The controlled process is an exposure process, and the controlled parameter is the exposure energy; or, the controlled process is an overlay error test, and the controlled parameter is the model parameter used in the overlay error test.

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