Semiconductor production data supervision system and method based on Internet of Things

Abstract

The invention relates to the technical field of supervision data transmission management, in particular to a semiconductor production data supervision system and method based on the Internet of Things, and the system comprises a production data collection module, a time transition prediction module, a production equipment data analysis module, and a supervision data transmission management module. Historical semiconductor production information and historical equipment inspection information for semiconductor production are acquired through a production data acquisition module, and a production time prediction model is established through a time transition prediction module to predict time required for completing a semiconductor production process in a current production stage. Before the next production stage of the current production stage starts, the production equipment data analysis module analyzes the equipment fault probability when the equipment is used after the operation state of the equipment is judged by referring to equipment inspection data stored in the production supervision terminal; equipment re-inspection and re-inspection data transmission management are carried out through the supervision data transmission management module, so that the equipment fault probability of the used equipment after reference inspection data is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of supervisory data transmission management, and in particular to a semiconductor production data supervision system and method based on the Internet of Things. Background Art

[0002] In the semiconductor production process, different production equipment is used in different production stages. These production equipment need to be regularly inspected and maintained during the semiconductor production process to ensure the safe operation of the equipment and thus the safe production of semiconductors.

[0003] However, since the number of semiconductors produced each time may vary, the time for transitioning to the next stage after the previous production stage completes the production process is also different. When entering the next production stage, the production equipment of the corresponding stage is needed, and the last inspection time of the corresponding equipment may be a long time ago. Before using the equipment, it is necessary to check the equipment inspection data to determine whether the equipment can operate safely. For inspection data uploaded to the supervision terminal with a long interval, referring to it to determine whether the equipment can operate safely can easily reduce the accuracy of the judgment result. The existing technology does not, on the basis of the original equipment inspection cycle, intervene in the equipment inspection time, that is, the time to upload the inspection data when necessary to maintain the accuracy of the result of the reference inspection data judgment, and cannot reduce the probability of equipment failure when the equipment is used after the reference inspection data.

[0004] Therefore, people need a semiconductor production data supervision system and method based on the Internet of Things to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a semiconductor production data monitoring system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a semiconductor production data supervision system based on the Internet of Things, the system comprising: a production data acquisition module, a time transition prediction module, a production equipment data analysis module and a supervision data transmission management module;

[0007] The output end of the production data acquisition module is connected to the input end of the time transition prediction module and the production equipment data analysis module, the output end of the time transition prediction module is connected to the input end of the production equipment data analysis module, and the output end of the production equipment data analysis module is connected to the input end of the supervision data transmission management module;

[0008] The production data acquisition module is used to collect historical semiconductor production information and historical inspection information of equipment used for semiconductor production;

[0009] The time transition prediction module is used to establish a production time prediction model according to the historical production information of semiconductors for different production stages, and predict the time required to complete the production process of the current number of semiconductors in the current production stage;

[0010] The production equipment data analysis module is used to analyze the probability of equipment failure when the equipment is used after judging the operating status of the equipment with reference to the equipment inspection data stored in the production supervision terminal before the start of the next production stage of the current production stage;

[0011] The supervisory data transmission management module is used to perform equipment re-inspection and re-inspection data transmission management based on the probability analysis results.

[0012] Further, the production data collection module includes a production quantity collection unit, a completion time collection unit and a production equipment information collection unit;

[0013] The production quantity collection unit is used to collect the historical production quantity of semiconductors;

[0014] The completion time collection unit is used to collect the time information taken by each production stage to complete the production process of a corresponding number of semiconductors when producing a corresponding number of semiconductors in the past. Semiconductors include multiple production stages, including wafer preparation, photolithography, thin film deposition, ion implantation, annealing and packaging;

[0015] The production equipment information collection unit is used to collect inspection information of equipment used for semiconductor production in each production stage, including the current default equipment inspection cycle, the previously set equipment inspection cycle, and the number of times the equipment fails when used in semiconductor production due to direct reference to the equipment inspection data transmitted to the production supervision terminal under the corresponding inspection cycle. Each production stage has corresponding equipment used for semiconductor production, such as a photolithography machine, a thin film deposition device, etc.

[0016] Further, the time transition prediction module includes a production data retrieval unit, a time prediction model establishment unit and a completion time prediction unit;

[0017] The input end of the production data retrieval unit is connected to the output ends of the production quantity collection unit and the completion time collection unit, the output end of the production data retrieval unit is connected to the input end of the time prediction model establishment unit, and the output end of the time prediction model establishment unit is connected to the input end of the completion time prediction unit;

[0018] The production data retrieval unit is used to retrieve the historical production quantity information of semiconductors and the time information taken to complete the production process of the corresponding number of semiconductors in each production stage when the corresponding number of semiconductors were produced in the past to the time prediction model establishment unit;

[0019] The time prediction model building unit is used to build a production time prediction model for each production stage;

[0020] The completion time prediction unit is used to obtain the number of semiconductors that currently need to be produced, substitute the number of semiconductors that currently need to be produced into the production time prediction model, and predict the time required to complete the production process of the current number of semiconductors in the current production stage.

[0021] Further, the production equipment data analysis module includes a transmission data reference time analysis unit and an equipment failure probability analysis unit;

[0022] The input end of the transmission data reference time analysis unit is connected to the output end of the production equipment information collection unit, and the input end of the equipment failure probability analysis unit is connected to the output ends of the transmission data reference time analysis unit and the completion time prediction unit;

[0023] The transmission data reference time analysis unit is used to analyze the probability of failure of the equipment used for semiconductor production in the next production stage of the current production stage in the past under different equipment inspection cycles due to directly referring to the equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally, and establish a failure probability prediction model for the corresponding equipment, and analyze the transmission time of the corresponding equipment inspection data referenced before the start of the next production stage under the currently set corresponding equipment inspection cycle;

[0024] The equipment failure probability analysis unit is used to substitute the interval between the transmission time and the start time of the next production stage into the failure probability prediction model, and predict whether the corresponding equipment can operate normally by directly referring to the equipment inspection data transmitted to the production supervision terminal in the current situation, and the probability of failure of the corresponding equipment when it is used in semiconductor production after obtaining the judgment result.

[0025] Further, the supervision data transmission management module includes a re-inspection judgment unit and an inspection data re-transmission unit;

[0026] The input end of the re-inspection judgment unit is connected to the output end of the equipment failure probability analysis unit, and the output end of the re-inspection judgment unit is connected to the input end of the inspection data retransmission unit;

[0027] The re-inspection judgment unit is used to set a fault probability threshold and compare the predicted probability with the threshold: if the predicted probability exceeds the threshold, it is determined that the equipment used in the corresponding production stage needs to be re-inspected before the start of the next production stage; if the predicted probability does not exceed the threshold, it is determined that the equipment used in the corresponding production stage does not need to be re-inspected before the start of the next production stage, and it is selected to directly refer to the corresponding equipment inspection data stored in the production supervision terminal before the start of the next production stage to determine whether the corresponding equipment can operate normally;

[0028] The inspection data retransmission unit is used to transmit the equipment inspection data to the production supervision terminal after re-inspecting the equipment if it is determined that the equipment used in the corresponding production stage needs to be re-inspected before the start of the next production stage. Before the start of the next production stage, the equipment inspection data transmitted after the re-inspection is referred to to determine whether the corresponding equipment can operate normally. After determining that the corresponding equipment can operate normally, the corresponding equipment is used to complete the semiconductor production process of the corresponding stage at the start of the next production stage.

[0029] A semiconductor production data supervision method based on the Internet of Things comprises the following steps:

[0030] Z1: Collect historical semiconductor production information and historical inspection information of equipment used for semiconductor production;

[0031] Z2: For different production stages, a production time prediction model is established based on the historical production information of semiconductors to predict the time required to complete the current number of semiconductor production processes in the current production stage;

[0032] Z3: Analyze the probability of equipment failure when the equipment is used after judging the equipment's operating status by referring to the equipment inspection data stored in the production supervision terminal before the next production stage of the current production stage begins;

[0033] Z4: Conduct equipment re-inspection and re-inspection data transmission management based on probability analysis results.

[0034] Furthermore, in step Z1: the historical production quantity set of each previous production of the semiconductor is collected as M = {M1, M2, ..., M r}, r represents the historical production times of semiconductors. When the corresponding number of semiconductors were produced in the past, the time information spent on completing the corresponding number of semiconductor production processes in each production stage is collected. The inspection information of the equipment used for semiconductor production in each production stage is collected, including the current default equipment inspection cycle, the previously set equipment inspection cycle, and the number of times the equipment fails when used in semiconductor production due to direct reference to the equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally under the corresponding inspection cycle, and the use of the equipment after the judgment result is obtained.

[0035] Further, in step Z2: when the semiconductor to be produced is currently in the i-th production stage, and the corresponding number of semiconductors in the previous production set M are retrieved, the time set taken to complete the production process of the corresponding number of semiconductors in the i-th production stage is t = {t1, t2, ..., t r}, for data points {(M1, t1), (M2, t2), …, (M r , t r )} to perform straight line fitting and establish a production time prediction model for predicting the time required to complete the semiconductor production process in the current production stage: Y = a*X+b, where a and b represent the fitting coefficients of the production time prediction model, and solve a and b respectively:

[0036]

[0037] Wherein, e=1,2,…,r, e represents the e-th production of semiconductors, the current number of semiconductors to be produced is obtained as N, and N is substituted into the production time prediction model: let X=N, and the predicted time required to complete the production process of the current number of semiconductors in the current production stage is a*N+b.

[0038] Further, in step Z3: retrieve the previously set equipment inspection cycle set L = {L1, L2, ..., L n}, n represents the number of historical settings of the inspection cycle of the corresponding equipment, and the number of times the equipment fails when used in semiconductor production due to the direct reference to the corresponding equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally under different inspection cycles in the past, and the equipment is used after the judgment result is obtained is H = {H1, H2, ..., H n},according to The calculation shows that when the inspection cycle is set to L j When the corresponding equipment inspection data transmitted to the production monitoring terminal is directly referenced to determine whether the equipment can operate normally, the probability that the equipment will fail when used in semiconductor production after the judgment result is obtained is P j , the failure probability set is P = {P1, P2, ..., P j ,…,P n}, for data points {(H1, P1), (H2, P2), …, (H n , P n )} to perform straight line fitting and establish a failure probability prediction model for the equipment used in semiconductor production in the i+1th production stage:

[0039] in, and It represents the fitting coefficient of the failure probability prediction model. The start time of the semiconductor that needs to be produced is A1, the start time of the i-th production stage of the semiconductor that needs to be produced is A2, and the equipment inspection cycle used for the semiconductor production in the i+1-th production stage is B1. The equipment inspection cycle B1 means that the equipment is inspected once every B1 interval after the start of the first stage of semiconductor production. Under the currently set corresponding equipment inspection cycle, the transmission time of the corresponding equipment inspection data referenced before the start of the i+1-th production stage is the interval time from the start time of the i+1-th production stage to the start time of the i+1-th production stage. in, Express Round down, let It is predicted that the probability that the corresponding equipment will fail when used in semiconductor production by directly referring to the equipment inspection data transmitted to the production monitoring terminal to determine whether the corresponding equipment can operate normally is p.

[0040] Further, in step Z4: set the failure probability threshold to p ’ , compare p and p ’ :If p>p ’ , it is determined that before the start of the i+1th production stage, the equipment used in the corresponding production stage needs to be re-inspected, and after the equipment is re-inspected, the equipment inspection data is transmitted to the production supervision terminal. Before the start of the i+1th production stage, the equipment inspection data transmitted after the re-inspection is referred to to determine whether the corresponding equipment can operate normally; if p≤p ’ , it is determined that there is no need to re-inspect the equipment used in the corresponding production stage before the start of the i+1th production stage, and it is chosen to directly refer to the corresponding equipment inspection data stored in the production supervision terminal before the start of the i+1th production stage to determine whether the corresponding equipment can operate normally. After determining that the corresponding equipment can operate normally, the corresponding equipment is used at the beginning of the i+1th production stage to complete the semiconductor production process of the corresponding stage.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0042] The present invention uses big data technology to collect historical production data of semiconductors and the time information required to complete the corresponding production volume in each stage, uses the production volume and time data as training data to establish a production time prediction model, substitutes the current number of semiconductors to be produced into the production time prediction model, and predicts the time required to complete the production process of the current number of semiconductors in the current production stage, thereby improving the accuracy of the prediction results of the production completion time under different circumstances;

[0043] By collecting information on equipment failures under the premise of setting different inspection cycles for the equipment in the past, analyzing the probability of equipment failure based on the collected information, analyzing the equipment inspection time transmitted to the supervision terminal as a reference before starting production work in the next production stage under the currently set equipment inspection cycle, predicting the reference corresponding inspection time, that is, the inspection data corresponding to the inspection data transmission time to judge the equipment operation results, and using the equipment after determining that the equipment can operate normally. The failure probability of the equipment is determined in advance based on the probability prediction results whether it is necessary to re-inspect the semiconductor production equipment and retransmit the inspection data, and managing the equipment inspection data transmission, thereby reducing the probability of equipment failure when the equipment is used after referring to the inspection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a structural diagram of a semiconductor production data supervision system based on the Internet of Things of the present invention;

[0046] Figure 2 It is a flow chart of a semiconductor production data supervision method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0047] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0048] Combine the following Figure 1-Figure 2 The present invention is further described with reference to the accompanying drawings and specific embodiments.

[0049] Embodiment 1:

[0050] like Figure 1 As shown, this embodiment provides a semiconductor production data supervision system based on the Internet of Things, the system includes: a production data acquisition module, a time transition prediction module, a production equipment data analysis module and a supervision data transmission management module;

[0051] The output end of the production data acquisition module is connected to the input end of the time transition prediction module and the production equipment data analysis module, the output end of the time transition prediction module is connected to the input end of the production equipment data analysis module, and the output end of the production equipment data analysis module is connected to the input end of the supervision data transmission management module;

[0052] The production data collection module is used to collect historical semiconductor production information and historical inspection information of equipment used for semiconductor production;

[0053] The time transition prediction module is used to establish a production time prediction model based on the historical production information of semiconductors for different production stages, and predict the time required to complete the current number of semiconductor production processes in the current production stage;

[0054] The production equipment data analysis module is used to analyze the probability of equipment failure when the equipment is used after judging the operating status of the equipment with reference to the equipment inspection data stored in the production supervision terminal before the next production stage of the current production stage begins;

[0055] The supervisory data transmission management module is used to perform equipment re-inspection and re-inspection data transmission management based on the probability analysis results.

[0056] The production data collection module includes a production quantity collection unit, a completion time collection unit and a production equipment information collection unit;

[0057] The production quantity collection unit is used to collect the historical production quantity of semiconductors;

[0058] The completion time collection unit is used to collect the time information taken by each production stage to complete the production process of the corresponding number of semiconductors when the corresponding number of semiconductors were produced in the past. Semiconductors include multiple production stages, including wafer preparation, photolithography, thin film deposition, ion implantation, annealing and packaging;

[0059] The production equipment information collection unit is used to collect inspection information of equipment used for semiconductor production in each production stage, including the current default equipment inspection cycle, the previously set equipment inspection cycle, and the corresponding inspection cycle, which directly refers to the equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally, and the number of times the equipment fails when used in semiconductor production due to the use of the equipment after the judgment result is obtained. Each production stage has corresponding equipment used for semiconductor production, such as photolithography machines, thin film deposition devices, etc.

[0060] The time transition prediction module includes a production data retrieval unit, a time prediction model establishment unit and a completion time prediction unit;

[0061] The input end of the production data retrieval unit is connected to the output ends of the production quantity collection unit and the completion time collection unit, the output end of the production data retrieval unit is connected to the input end of the time prediction model establishment unit, and the output end of the time prediction model establishment unit is connected to the input end of the completion time prediction unit;

[0062] The production data retrieval unit is used to retrieve the historical production quantity information of semiconductors and the time information spent on completing the production process of the corresponding number of semiconductors in each production stage when the corresponding number of semiconductors were produced in the past to the time prediction model establishment unit;

[0063] The time prediction model building unit is used to build a production time prediction model for each production stage;

[0064] The completion time prediction unit is used to obtain the current number of semiconductors that need to be produced, substitute the current number of semiconductors that need to be produced into the production time prediction model, and predict the time required to complete the production process of the current number of semiconductors in the current production stage.

[0065] The production equipment data analysis module includes a transmission data reference time analysis unit and an equipment failure probability analysis unit;

[0066] The input end of the transmission data reference time analysis unit is connected to the output end of the production equipment information collection unit, and the input end of the equipment failure probability analysis unit is connected to the output ends of the transmission data reference time analysis unit and the completion time prediction unit;

[0067] The transmission data reference time analysis unit is used to analyze the probability of failure of the equipment used for semiconductor production in the next production stage of the current production stage in the past under different equipment inspection cycles due to directly referring to the equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally, and establish a failure probability prediction model for the corresponding equipment, and analyze the transmission time of the corresponding equipment inspection data referenced before the start of the next production stage under the currently set corresponding equipment inspection cycle;

[0068] The equipment failure probability analysis unit is used to substitute the interval between the transmission time and the start time of the next production stage into the failure probability prediction model, and predict whether the corresponding equipment can operate normally by directly referring to the equipment inspection data transmitted to the production supervision terminal in the current situation, and the probability of failure of the corresponding equipment when it is used in semiconductor production after obtaining the judgment result.

[0069] The supervision data transmission management module includes a re-inspection judgment unit and an inspection data re-transmission unit;

[0070] The input end of the re-inspection and judgment unit is connected to the output end of the equipment failure probability analysis unit, and the output end of the re-inspection and judgment unit is connected to the input end of the inspection data retransmission unit;

[0071] The re-inspection judgment unit is used to set a fault probability threshold and compare the predicted probability with the threshold: if the predicted probability exceeds the threshold, it is determined that the equipment used in the corresponding production stage needs to be re-inspected before the start of the next production stage; if the predicted probability does not exceed the threshold, it is determined that the equipment used in the corresponding production stage does not need to be re-inspected before the start of the next production stage, and it is selected to directly refer to the corresponding equipment inspection data stored in the production supervision terminal before the start of the next production stage to determine whether the corresponding equipment can operate normally;

[0072] The inspection data retransmission unit is used to transmit the equipment inspection data to the production supervision terminal after re-inspecting the equipment if it is determined that the equipment used in the corresponding production stage needs to be re-inspected before the start of the next production stage. Before the start of the next production stage, the equipment inspection data transmitted after the re-inspection is referred to to determine whether the corresponding equipment can operate normally. After determining that the corresponding equipment can operate normally, the corresponding equipment is used to complete the semiconductor production process of the corresponding stage at the start of the next production stage.

[0073] Embodiment 2:

[0074] like Figure 2 As shown, this embodiment provides a semiconductor production data supervision method based on the Internet of Things, which is implemented based on the data supervision system in the embodiment and specifically includes the following steps:

[0075] Z1: Collect historical semiconductor production information and historical inspection information of equipment used for semiconductor production. The historical production quantity set of each previous semiconductor production is M = {M1, M2, ..., M r}, r represents the historical production times of semiconductors, collects the time information taken to complete the production process of the corresponding number of semiconductors in each production stage when the corresponding number of semiconductors were produced in the past, and collects the inspection information of the equipment used for semiconductor production in each production stage, including the equipment inspection cycle currently set as the default, the equipment inspection cycle set in the past, and the number of times the equipment fails when it is used for semiconductor production due to the direct reference to the equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally under the corresponding inspection cycle, and the use of the equipment after the judgment result is obtained;

[0076] Z2: For different production stages, a production time prediction model is established based on the historical production information of semiconductors to predict the time required to complete the production process of the current number of semiconductors in the current production stage. When the semiconductors currently to be produced are in the i-th production stage and the corresponding number of semiconductors in the previous production set M are retrieved, the time set required to complete the production process of the corresponding number of semiconductors in the i-th production stage is t = {t1, t2, ..., t r}, for data points {(M1, t1), (M2, t2), …, (M r , t r )} to perform straight line fitting and establish a production time prediction model for predicting the time required to complete the semiconductor production process in the current production stage: Y = a*X+b, where a and b represent the fitting coefficients of the production time prediction model, and solve a and b respectively:

[0077]

[0078] Where, e=1,2,…,r, e represents the e-th production of semiconductors, and the number of semiconductors that need to be produced is N. Substitute N into the production time prediction model: Let X=N, and the time required to complete the production process of the current number of semiconductors in the current production stage is predicted to be a*N+b;

[0079] Z3: Analyze the probability of equipment failure when using the equipment after judging the equipment's operating status by referring to the equipment inspection data stored in the production supervision terminal before the next production stage of the current production stage begins, and retrieve the equipment inspection cycle set L = {L1, L2, ..., L n}, n represents the number of historical settings of the inspection cycle of the corresponding equipment, and the number of times the equipment fails when used in semiconductor production due to the direct reference to the corresponding equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally under different inspection cycles in the past, and the equipment is used after the judgment result is obtained is H = {H1, H2, ..., H n},according to The calculation shows that when the inspection cycle is set to L j When the corresponding equipment inspection data transmitted to the production monitoring terminal is directly referenced to determine whether the equipment can operate normally, the probability that the equipment will fail when used in semiconductor production after the judgment result is obtained is P j , the failure probability set is P = {P1, P2, ..., P j ,…,P n}, for data points {(H1, P1), (H2, P2), …, (H n , P n )} to perform straight line fitting and establish a failure probability prediction model for the equipment used in semiconductor production in the i+1th production stage: in, and It represents the fitting coefficient of the failure probability prediction model. The start time of the semiconductor that needs to be produced is A1, the start time of the i-th production stage of the semiconductor that needs to be produced is A2, and the equipment inspection cycle used for the semiconductor production in the i+1-th production stage is B1. The equipment inspection cycle B1 means that the equipment is inspected once every B1 interval after the start of the first stage of semiconductor production. Under the currently set corresponding equipment inspection cycle, the transmission time of the corresponding equipment inspection data referenced before the start of the i+1-th production stage is the interval time from the start time of the i+1-th production stage to the start time of the i+1-th production stage. in, Express Round down, let It is predicted that the probability that the corresponding equipment will fail when used in semiconductor production by directly referring to the equipment inspection data transmitted to the production monitoring terminal to determine whether the corresponding equipment can operate normally is p.

[0080] Z4: Perform equipment re-inspection and re-inspection data transmission management based on the probability analysis results, and set the fault probability threshold to p ’ , compare p and p ’ :If p>p ’ , it is determined that before the start of the i+1th production stage, the equipment used in the corresponding production stage needs to be re-inspected, and after the equipment is re-inspected, the equipment inspection data is transmitted to the production supervision terminal. Before the start of the i+1th production stage, the equipment inspection data transmitted after the re-inspection is referred to to determine whether the corresponding equipment can operate normally; if p≤p ’ , it is determined that it is not necessary to re-inspect the equipment used in the corresponding production stage before the start of the i+1th production stage, and it is chosen to directly refer to the corresponding equipment inspection data stored in the production supervision terminal before the start of the i+1th production stage to determine whether the corresponding equipment can operate normally. After determining that the corresponding equipment can operate normally, the corresponding equipment is used to complete the semiconductor production process of the corresponding stage at the start of the i+1th production stage;

[0081] For example: If the current production stage is photolithography, set the failure probability threshold to p ’ =0.4, and the predicted probability is p=0.6 that the corresponding equipment will fail when it is used in semiconductor production if the equipment inspection data transmitted to the production monitoring terminal is directly used to judge whether the corresponding equipment can operate normally. ’ , it is necessary to re-inspect the thin film deposition device before the start of the thin film deposition production phase, and after re-inspecting the thin film deposition device, transmit the equipment inspection data to the production supervision terminal, and before the start of the production phase of the thin film deposition device, refer to the equipment inspection data transmitted after the re-inspection to determine whether the thin film deposition device can operate normally, and after determining that the thin film deposition device can operate normally, use the thin film deposition device to complete the semiconductor production process of the corresponding phase at the beginning of the production phase of the thin film deposition device.

[0082] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A semiconductor production data supervision system based on the Internet of Things, characterized by: The system includes: a production data acquisition module, a time transition prediction module, a production equipment data analysis module and a supervision data transmission management module; The output end of the production data acquisition module is connected to the input end of the time transition prediction module and the production equipment data analysis module, the output end of the time transition prediction module is connected to the input end of the production equipment data analysis module, and the output end of the production equipment data analysis module is connected to the input end of the supervision data transmission management module; The production data acquisition module is used to collect historical semiconductor production information and historical inspection information of equipment used for semiconductor production; The time transition prediction module is used to establish a production time prediction model according to the historical production information of semiconductors for different production stages, and predict the time required to complete the production process of the current number of semiconductors in the current production stage; The production equipment data analysis module is used to analyze the probability of equipment failure when the equipment is used after judging the operating status of the equipment with reference to the equipment inspection data stored in the production supervision terminal before the start of the next production stage of the current production stage; The supervisory data transmission management module is used to perform equipment re-inspection and re-inspection data transmission management based on the probability analysis results.

2. The semiconductor production data monitoring system based on the Internet of Things according to claim 1 is characterized in that: The production data collection module includes a production quantity collection unit, a completion time collection unit and a production equipment information collection unit; The production quantity collection unit is used to collect the historical production quantity of semiconductors; The completion time collection unit is used to collect the time information taken to complete the production process of the corresponding number of semiconductors in each production stage when the corresponding number of semiconductors were produced in the past; The production equipment information collection unit is used to collect inspection information of equipment used for semiconductor production in each production stage, including the current default equipment inspection cycle, the previously set equipment inspection cycle, and the number of times the equipment fails when used in semiconductor production due to direct reference to the equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally under the corresponding inspection cycle, and the use of the equipment after obtaining the judgment result.

3. The semiconductor production data monitoring system based on the Internet of Things according to claim 2 is characterized in that: The time transition prediction module includes a production data retrieval unit, a time prediction model establishment unit and a completion time prediction unit; The input end of the production data retrieval unit is connected to the output ends of the production quantity collection unit and the completion time collection unit, the output end of the production data retrieval unit is connected to the input end of the time prediction model establishment unit, and the output end of the time prediction model establishment unit is connected to the input end of the completion time prediction unit; The production data retrieval unit is used to retrieve the historical production quantity information of semiconductors and the time information taken to complete the production process of the corresponding number of semiconductors in each production stage when the corresponding number of semiconductors were produced in the past to the time prediction model establishment unit; The time prediction model establishment unit is used to establish a production time prediction model for each production stage; The completion time prediction unit is used to obtain the number of semiconductors that currently need to be produced, substitute the number of semiconductors that currently need to be produced into the production time prediction model, and predict the time required to complete the production process of the current number of semiconductors in the current production stage.

4. The semiconductor production data monitoring system based on the Internet of Things according to claim 3 is characterized in that: The production equipment data analysis module includes a transmission data reference time analysis unit and an equipment failure probability analysis unit; The input end of the transmission data reference time analysis unit is connected to the output end of the production equipment information collection unit, and the input end of the equipment failure probability analysis unit is connected to the output ends of the transmission data reference time analysis unit and the completion time prediction unit; The transmission data reference time analysis unit is used to analyze the probability of failure of the equipment used for semiconductor production in the next production stage of the current production stage in the past under different equipment inspection cycles due to directly referring to the equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally, and establish a failure probability prediction model for the corresponding equipment, and analyze the transmission time of the corresponding equipment inspection data referenced before the start of the next production stage under the currently set corresponding equipment inspection cycle; The equipment failure probability analysis unit is used to substitute the interval between the transmission time and the start time of the next production stage into the failure probability prediction model, and predict whether the corresponding equipment can operate normally by directly referring to the equipment inspection data transmitted to the production supervision terminal in the current situation, and the probability of failure of the corresponding equipment when it is used in semiconductor production after obtaining the judgment result.

5. The semiconductor production data monitoring system based on the Internet of Things according to claim 4 is characterized in that: The supervision data transmission management module includes a re-inspection judgment unit and an inspection data re-transmission unit; The input end of the re-inspection judgment unit is connected to the output end of the equipment failure probability analysis unit, and the output end of the re-inspection judgment unit is connected to the input end of the inspection data retransmission unit; The re-inspection judgment unit is used to set a fault probability threshold and compare the predicted probability with the threshold: if the predicted probability exceeds the threshold, it is determined that the equipment used in the corresponding production stage needs to be re-inspected before the start of the next production stage; if the predicted probability does not exceed the threshold, it is determined that the equipment used in the corresponding production stage does not need to be re-inspected before the start of the next production stage, and it is selected to directly refer to the corresponding equipment inspection data stored in the production supervision terminal before the start of the next production stage to determine whether the corresponding equipment can operate normally; The inspection data retransmission unit is used to transmit the equipment inspection data to the production supervision terminal after re-inspecting the equipment if it is determined that the equipment used in the corresponding production stage needs to be re-inspected before the start of the next production stage. Before the start of the next production stage, the equipment inspection data transmitted after the re-inspection is referred to to determine whether the corresponding equipment can operate normally. After determining that the corresponding equipment can operate normally, the corresponding equipment is used to complete the semiconductor production process of the corresponding stage at the start of the next production stage.

6. A semiconductor production data supervision method based on the Internet of Things, characterized in that: The following steps are involved: Z1: Collect historical semiconductor production information and historical inspection information of equipment used for semiconductor production; Z2: For different production stages, a production time prediction model is established based on the historical production information of semiconductors to predict the time required to complete the current number of semiconductor production processes in the current production stage; Z3: Analyze the probability of equipment failure when the equipment is used after judging the equipment's operating status by referring to the equipment inspection data stored in the production supervision terminal before the next production stage of the current production stage begins; Z4: Conduct equipment re-inspection and re-inspection data transmission management based on probability analysis results.

7. The semiconductor production data supervision method based on the Internet of Things according to claim 6 is characterized in that: In step Z1: the historical production quantity set of each previous production of semiconductors is collected as M = {M1, M2, ..., M r }, r represents the historical production times of semiconductors. When the corresponding number of semiconductors were produced in the past, the time information spent on completing the corresponding number of semiconductor production processes in each production stage is collected. The inspection information of the equipment used for semiconductor production in each production stage is collected, including the current default equipment inspection cycle, the previously set equipment inspection cycle, and the number of times the equipment fails when used in semiconductor production due to direct reference to the equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally under the corresponding inspection cycle, and the use of the equipment after the judgment result is obtained.

8. The semiconductor production data supervision method based on the Internet of Things according to claim 7 is characterized in that: In step Z2: the semiconductor to be produced is currently in the i-th production stage. When the corresponding number of semiconductors in the previous production set M is retrieved, the time set taken to complete the production process of the corresponding number of semiconductors in the i-th production stage is t = {t1, t2, ..., t r }, for data points {(M1, t1), (M2, t2), …, (M r , t r )} to perform straight line fitting and establish a production time prediction model for predicting the time required to complete the semiconductor production process in the current production stage: Y = a*X+b, where a and b represent the fitting coefficients of the production time prediction model, and solve a and b respectively: Wherein, e=1,2,…,r, e represents the e-th production of semiconductors, the current number of semiconductors to be produced is obtained as N, and N is substituted into the production time prediction model: let X=N, and the predicted time required to complete the production process of the current number of semiconductors in the current production stage is a*N+b.

9. The semiconductor production data supervision method based on the Internet of Things according to claim 8 is characterized in that: In step Z3: retrieve the previously set equipment inspection cycle set L = {L1, L2, ..., L n }, n represents the number of historical settings of the inspection cycle of the corresponding equipment, and the number of times the equipment fails when used in semiconductor production due to the direct reference to the corresponding equipment inspection data transmitted to the production supervision terminal to determine whether the equipment can operate normally under different inspection cycles in the past, and the equipment is used after the judgment result is obtained is H = {H1, H2, ..., H n },according to The calculation shows that when the inspection cycle is set to L j When the corresponding equipment inspection data transmitted to the production monitoring terminal is directly referenced to determine whether the equipment can operate normally, the probability that the equipment will fail when used in semiconductor production after the judgment result is obtained is P j , the failure probability set is P = {P1, P2, ..., P j ,…,P n }, for data points {(H1, P1), (H2, P2), ..., (H n , P n )} to perform straight line fitting and establish a failure probability prediction model for the equipment used in semiconductor production in the i+1th production stage: in, and It represents the fitting coefficient of the failure probability prediction model. The start time of the semiconductor that needs to be produced is A1, the start time of the i-th production stage of the semiconductor that needs to be produced is A2, and the equipment inspection cycle used for semiconductor production in the i+1-th production stage is B1. Under the currently set corresponding equipment inspection cycle, the transmission time of the corresponding equipment inspection data referenced before the start of the i+1-th production stage is the interval time from the start time of the i+1-th production stage to the start time of the i+1-th production stage. in, Express Round down, let x = A1 + It is predicted that the probability that the corresponding equipment will fail when used in semiconductor production by directly referring to the equipment inspection data transmitted to the production monitoring terminal to determine whether the corresponding equipment can operate normally is p.

10. The semiconductor production data supervision method based on the Internet of Things according to claim 9, characterized in that: In step Z4: set the failure probability threshold to p ’ , compare p and p ’ :If p>p ’ , it is determined that before the start of the i+1th production stage, the equipment used in the corresponding production stage needs to be re-inspected, and after the equipment is re-inspected, the equipment inspection data is transmitted to the production supervision terminal. Before the start of the i+1th production stage, the equipment inspection data transmitted after the re-inspection is referred to to determine whether the corresponding equipment can operate normally; if p≤p ’ , it is determined that there is no need to re-inspect the equipment used in the corresponding production stage before the start of the i+1th production stage, and it is chosen to directly refer to the corresponding equipment inspection data stored in the production supervision terminal before the start of the i+1th production stage to determine whether the corresponding equipment can operate normally. After determining that the corresponding equipment can operate normally, the corresponding equipment is used at the beginning of the i+1th production stage to complete the semiconductor production process of the corresponding stage.

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