CN117762069A - Control chip automatic data processing system and method based on Internet of things - Google Patents
Control chip automatic data processing system and method based on Internet of things Download PDFInfo
- Publication number
- CN117762069A CN117762069A CN202311717267.8A CN202311717267A CN117762069A CN 117762069 A CN117762069 A CN 117762069A CN 202311717267 A CN202311717267 A CN 202311717267A CN 117762069 A CN117762069 A CN 117762069A
- Authority
- CN
- China
- Prior art keywords
- functional defect
- control chip
- functional
- real
- defect
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012545 processing Methods 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000007849 functional defect Effects 0.000 claims abstract description 281
- 238000012360 testing method Methods 0.000 claims abstract description 132
- 238000007405 data analysis Methods 0.000 claims abstract description 33
- 230000007547 defect Effects 0.000 claims description 53
- 238000004458 analytical method Methods 0.000 claims description 35
- 238000012544 monitoring process Methods 0.000 claims description 30
- 230000008859 change Effects 0.000 claims description 16
- 230000008030 elimination Effects 0.000 claims description 8
- 238000003379 elimination reaction Methods 0.000 claims description 8
- 238000003672 processing method Methods 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 230000009467 reduction Effects 0.000 claims description 6
- 230000008439 repair process Effects 0.000 claims description 6
- 230000009471 action Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 229940079593 drug Drugs 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000004377 microelectronic Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Test And Diagnosis Of Digital Computers (AREA)
Abstract
The invention discloses an automatic data processing system and method of a control chip based on the Internet of things, and belongs to the technical field of data processing. The invention comprises the following steps: s10: the data analysis terminal determines the functional defect position of the control chip according to the received real-time test data; s20: automatically associating the real-time test data of the control chip at each functional defect position, and determining the functional defect root position of the control chip according to the automatic association result; s30: analyzing the functional defect reasons of the control chip according to the priority level of the functional defect source positions of the control chip; s40: and analyzing the fault conditions of other control chips. According to the invention, the functional defect root positions of the functional modules are determined through the association degree among the functional defect positions in the functional modules, so that the data processing capacity of the system on the functional defect positions is reduced, and the data processing rate of the system is further improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an automatic data processing system and method of a control chip based on the Internet of things.
Background
A chip, also known as an integrated circuit, is a microelectronic system fabricated from a plurality of electronic components (e.g., transistors, resistors, capacitors, etc.) on a single semiconductor material. The manufacturing process of chips requires highly accurate equipment and processes to ensure that the electronic components are properly connected and functioning.
Today, when acquiring test data of a chip, the test data is usually collected and analyzed manually from the chip under test, and is not performed in real time, so when existing defects are observed at a time that is usually too late to be prevented before the chip enters the streaming link, when the manual analysis is adopted, test files containing various data may be intercepted, thereby causing safety problems, and the existing system cannot accurately analyze the root cause of the chip, so that the produced chip is affected by the same problem, and the error correction cost of the chip is increased.
Disclosure of Invention
The invention aims to provide an automatic data processing system and method of a control chip based on the Internet of things, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an automated data processing method of a control chip based on the Internet of things, the method comprising:
s10: connecting the control chip with an application programming interface, connecting the application programming interface with a data analysis terminal, transmitting real-time test data of the control chip to the data analysis terminal through the application programming interface, and determining the functional defect position of the control chip by the data analysis terminal according to the received real-time test data;
s20: automatically associating the real-time test data of the control chip at each functional defect position, and determining the functional defect root position of the control chip according to the automatic association result;
s30: analyzing the functional defect reasons of the control chip according to the priority level of the functional defect source positions of the control chip;
s40: and (3) performing functional repair processing on the control chip according to the analysis result in the step (S30), taking real-time test data of the repaired control chip as monitoring data, and analyzing fault conditions of other control chips through comparison results of the monitoring data and the real-time test data of the other control chips under the condition that the test environment temperatures of the control chips are the same.
Further, the specific method for predicting the functional defect position of the control chip by the data analysis terminal according to the received real-time test data in S10 is as follows:
acquiring the real-time test environment temperature of the control chip, judging whether the acquired test environment temperature is within a set test environment temperature range, setting the range value of the test environment temperature to be T 'to T', if so, indicating that the delay time of the real-time test data generated by the control chip is 0, and if not, constructing a prediction model to predict the delay time T of the real-time test data generated by the control chip;
dividing the acquired real-time test environment temperature according to the variation condition of the acquired real-time test environment temperature of the control chip, wherein the real-time test environment temperature values in each dividing section are identical, and the real-time test environment temperature values corresponding to adjacent dividing points are different;
the specific prediction model is as follows:
wherein u=1, 2, …, n represents the number corresponding to each division point, n represents the total number of division points, V represents the average running speed of the control chip in the set test environment temperature range, α represents the average running speed reduction value corresponding to every 1 ℃ decrease in the test environment temperature of the control chip, β represents the average running speed reduction value corresponding to every 1 ℃ increase in the test environment temperature of the control chip, T u Representing the corresponding real-time test environment temperature value of the control chip in the ith division segment, S u Representing the total data processed by the control chip in the ith division segment, when T u When the value of T' > 0 is exceeded,when T is u When T is less than or equal to 0, the formula of the drug is-> When T' -T u When less than or equal to 0, the weight is increased>When T' -T u At > 0>
And adjusting the generation time of the standard monitoring data according to the predicted delay time, comparing the adjusted standard monitoring data with real-time test data received by the data analysis terminal, wherein the standard monitoring data refers to test data obtained by testing an application program when the control chip has no functional defects, determining the functional defect position of the control chip according to a comparison result, and outputting the output position corresponding to the real-time test data when the real-time test data received by the functional defect position data analysis terminal does not accord with the adjusted standard monitoring data.
Further, the step S20 includes:
s201: determining the function modules corresponding to the function defect positions, putting the function defect positions belonging to the same function module into the same set, randomly selecting one function defect position in the same set, judging whether the selected function defect position is the rest function defect positions in the same set, providing data support, if not, indicating that the association degree between the selected function defect position and the corresponding function defect position is 0, and if the data support is provided, indicating that the association degree between the selected function defect position and the corresponding function defect position is 1 until the function defect positions in the same set are all selected;
for example: the real-time test data corresponding to the selected functional defect position is 02.36.49, wherein the test data 36 needs to be transmitted to one of the remaining functional defect positions in the same set, and the selected functional defect position is considered to provide data support for the functional defect position in the same set, and at this time, the association degree between the selected functional defect position and the corresponding functional defect position is 1;
s202: when the association degree between the selected functional defect position and the corresponding functional defect position is 0, the selected functional defect position and the corresponding functional defect position are the functional defect root positions of the corresponding functional modules, and the functional defect root positions which are determined to be the corresponding functional modules are removed from the set;
when the association degree between the selected functional defect position and the corresponding functional defect position is 1, the number of times that each functional defect position in the set after the elimination processing appears when the association degree is 1 is obtained, if Ui > 1, whether the abnormal data amount existing in the corresponding functional defect position is equal to Ui or not is judged, if equal, the corresponding functional defect position is not the functional defect source position of the corresponding functional module, if not equal, the corresponding functional defect position is the functional defect source position of the corresponding functional module, the abnormal data amount existing in the corresponding functional defect position is equal to or more than Ui, if ui=1, the corresponding functional defect position is the functional defect source position of the corresponding functional module, i=1, 2, …, m represents the number corresponding to each functional defect position in the set after the elimination processing, m represents the total number of the functional defect positions, ui represents the number of times that the functional defect position with the number i appears when the association degree is 1 when the association degree analysis is performed.
Further, the step S30 includes:
s301: determining the priority level of each functional defect source position of the control chip according to the working time point of each functional defect source position in the control chip, wherein the closer the working time point is to the testing time point of the control chip, the higher the priority level of the corresponding functional defect source position is;
s302: acquiring real-time test data of a functional defect source position belonging to a first priority level, calculating a difference value between the acquired real-time test data and corresponding matched adjusted standard monitoring data, determining a real-time change rate of the difference value, determining whether the calculated difference value is in an error range or not, if the calculated difference value is in the error range, comparing the determined real-time change rate with a voltage real-time change rate of a control chip in a corresponding time period, wherein the voltage real-time change rate is equal to the voltage real-time change rate of the control chip, and if the determined real-time change rate is not in the error range, the calculated difference value is the test voltage fluctuation of the control chip, and if the calculated difference value is not in the error range, the calculated difference value is the test voltage fluctuation of the control chip;
when the functional defect reason of the functional defect source position belonging to the first priority level is the test error of the control chip, correcting real-time test data generated by the control chip at the functional defect position belonging to the first priority level;
when the functional defect reason of the functional defect source position belonging to the first priority level is external electromagnetic signal interference, shielding an interference electromagnetic signal existing at the test position of the control chip;
when the functional defect reason of the functional defect source position belonging to the first priority level is the defect of the chip, stopping testing the control chip at the moment;
s303: according to the analysis method of the functional defect cause of the functional defect source position belonging to the first priority level in S302, the functional defect causes of the functional defect source positions of other priority levels are automatically analyzed sequentially.
The control chip automatic data processing system based on the Internet of things comprises a functional defect position determining module, a functional defect root position determining module, a functional defect cause analyzing module and an automatic data analyzing and processing module;
the functional defect position determining module is used for determining the functional defect position of the control chip according to the real-time test data of the control chip;
the functional defect source position determining module is used for determining the functional defect source position of the control chip;
the functional defect reason analysis module is used for analyzing the functional defect reason of the control chip;
the automatic data analysis processing module is used for automatically analyzing fault conditions of other control chips.
Further, the functional defect position determining module comprises a module connecting unit, a delay time predicting unit and a functional defect position determining unit;
the module connecting unit connects the control chip with the application programming interface and connects the application programming interface with the data analysis terminal;
the delay time prediction unit acquires the real-time test environment temperature of the control chip, constructs a prediction model according to the acquired information, predicts the delay time of real-time test data generated by the control chip, and transmits a prediction result to the functional defect position determination unit;
the functional defect position determining unit receives the prediction result transmitted by the delay time predicting unit, adjusts the generation time of the standard monitoring data based on the received information, compares the adjusted standard monitoring data with real-time test data received by the data analysis terminal, determines the functional defect position of the control chip according to the comparison result, and transmits the determined functional defect position to the functional defect root position determining module.
Further, the functional defect source position determining module comprises a classifying unit, a relevance determining unit, a functional defect source position determining unit I and a functional defect source position determining unit II;
the classifying unit receives the function defect positions transmitted by the function defect position determining unit, determines the function modules corresponding to the function defect positions, puts the function defect positions belonging to the same function module into the same set, and transmits the classifying result to the association degree determining unit;
the association degree determining unit receives the classification result transmitted by the classifying unit, randomly selects one functional defect position in the same set, judges whether the selected functional defect position provides data support for each residual functional defect position in the same set, determines the association degree between the selected functional defect position and the corresponding functional defect position according to the judgment result, and transmits the determination result to the functional defect root position determining unit I or the functional defect root position determining unit II;
the first functional defect source position determining unit receives the determining result transmitted by the association degree determining unit when the association degree determined by the association degree determining unit is 0, determines the functional defect source position of the corresponding functional module based on the receiving information, performs rejection processing on the set according to the determining result, transmits the set after the rejection processing to the second functional defect source position determining unit, and transmits the determined functional defect source position to the functional defect cause analyzing module;
and the second functional defect source position determining unit receives the determining result transmitted by the association degree determining unit and the set transmitted by the first functional defect source position determining unit when the association degree determined by the association degree determining unit is 1, determines the functional defect source position of the corresponding functional module based on the receiving information, and transmits the determining result to the functional defect cause analyzing module.
Further, the functional defect cause analysis module comprises a priority level determination unit and a functional defect cause analysis unit;
the priority level determining unit receives the functional defect source positions transmitted by the first functional defect source position determining unit or the second functional defect source position determining unit, determines the priority level of each functional defect source position of the control chip according to the working time point of each functional defect source position in the control chip, and transmits the determination result to the functional defect cause analyzing unit;
the functional defect cause analysis unit receives the determination result transmitted by the priority level determination unit, analyzes the functional defect cause of the control chip based on the received information, and transmits the analysis result to the automatic data analysis processing module.
Further, the automatic data analysis processing module receives the analysis result transmitted by the functional defect cause analysis unit, performs functional repair processing on the control chip based on the received information, takes real-time test data of the repaired control chip as monitoring data, and performs automatic analysis on fault conditions of other control chips through comparison results of the monitoring data and the real-time test data of the other control chips under the condition that the test environment temperatures of the control chips are the same.
Compared with the prior art, the invention has the following beneficial effects:
1. the control chip is connected with the application programming interface, and the application programming interface is connected with the data analysis terminal, so that the real-time automatic collection of the test data of the control chip is realized, the control chip is prevented before the chip with problems enters the streaming link, and the safety of the test file is improved.
2. According to the invention, the delay time of data generated by the control chip is predicted in real time according to the real-time test environment temperature value of the control chip, so that the influence of environmental factors on the test result of the control chip is avoided, the rejection of good control chips is avoided, the yield loss of the control chip is further increased, and the use effect of the system is reduced.
3. According to the invention, the functional defect root positions of the functional modules are determined through the association degree among the functional defect positions in the functional modules, so that the data processing capacity of the system on the functional defect positions is reduced, and the data processing rate of the system is further improved.
4. According to the invention, the priority level of the functional defect source position is divided, the problem source of the control chip is accurately analyzed based on the division result, the produced chip is prevented from being influenced by the same problem, the error correction cost of the chip is reduced, and the automatic analysis of the fault condition of the control chip is realized under the same test environment.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic workflow diagram of an automated data processing system and method for a control chip based on the Internet of things of the present invention;
fig. 2 is a schematic structural diagram of the working principle of the control chip automated data processing system and method based on the internet of things.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 and 2, the present invention provides the following technical solutions: a control chip automatic data processing method based on the Internet of things comprises the following steps:
s10: connecting the control chip with an application programming interface, connecting the application programming interface with a data analysis terminal, transmitting real-time test data of the control chip to the data analysis terminal through the application programming interface, and determining the functional defect position of the control chip by the data analysis terminal according to the received real-time test data;
the specific method for predicting the functional defect position of the control chip by the data analysis terminal according to the received real-time test data in S10 is as follows:
acquiring the real-time test environment temperature of the control chip, judging whether the acquired test environment temperature is within a set test environment temperature range, setting the range value of the test environment temperature to be T 'to T', if so, indicating that the delay time of the real-time test data generated by the control chip is 0, and if not, constructing a prediction model to predict the delay time T of the real-time test data generated by the control chip;
dividing the acquired real-time test environment temperature according to the variation condition of the acquired real-time test environment temperature of the control chip, wherein the real-time test environment temperature values in each dividing section are identical, and the real-time test environment temperature values corresponding to adjacent dividing points are different;
the specific prediction model is as follows:
wherein u=1, 2, …, n represents the number corresponding to each division point, n represents the total number of division points, V represents the average running speed of the control chip in the set test environment temperature range, α represents the average running speed reduction value corresponding to every 1 ℃ decrease in the test environment temperature of the control chip, β represents the average running speed reduction value corresponding to every 1 ℃ increase in the test environment temperature of the control chip, T u Representing the corresponding real-time test environment temperature value of the control chip in the ith division segment, S u Representing the total data processed by the control chip in the ith division segment, when T u When the value of T' > 0 is exceeded,when T is u When T is less than or equal to 0, the formula of the drug is-> When T' -T u When less than or equal to 0, the weight is increased>When T' -T u At > 0>
Adjusting the generation time of standard monitoring data according to the predicted delay time, comparing the adjusted standard monitoring data with real-time test data received by a data analysis terminal, wherein the standard monitoring data refers to test data obtained by testing an application program when the control chip has no functional defects, and determining the functional defect positions of the control chip according to the comparison result;
s20: automatically associating the real-time test data of the control chip at each functional defect position, and determining the functional defect root position of the control chip according to the automatic association result;
s20 includes:
s201: determining the function modules corresponding to the function defect positions, putting the function defect positions belonging to the same function module into the same set, randomly selecting one function defect position in the same set, judging whether the selected function defect position is the rest function defect positions in the same set, providing data support, if not, indicating that the association degree between the selected function defect position and the corresponding function defect position is 0, and if the data support is provided, indicating that the association degree between the selected function defect position and the corresponding function defect position is 1 until the function defect positions in the same set are all selected;
s202: when the association degree between the selected functional defect position and the corresponding functional defect position is 0, the selected functional defect position and the corresponding functional defect position are the functional defect root positions of the corresponding functional modules, and the functional defect root positions which are determined to be the corresponding functional modules are removed from the set;
when the association degree between the selected functional defect position and the corresponding functional defect position is 1, acquiring the times of occurrence of each functional defect position in the set after the elimination processing when the association degree is 1, judging whether the abnormal data quantity existing in the corresponding functional defect position is equal to Ui or not if Ui is more than 1, if so, the corresponding functional defect position is not the functional defect source position of the corresponding functional module, if not, the corresponding functional defect position is the functional defect source position of the corresponding functional module, the abnormal data quantity existing in the corresponding functional defect position is more than or equal to Ui, if Ui=1, the corresponding functional defect position is the functional defect source position of the corresponding functional module, i=1, 2, …, m represents the numbers corresponding to the functional defect positions in the set after the elimination processing, m represents the total number of the functional defect positions, and Ui represents the times of occurrence of the functional defect position with the number i when the association degree is 1 when the association degree analysis is performed;
s30: analyzing the functional defect reasons of the control chip according to the priority level of the functional defect source positions of the control chip;
s30 includes:
s301: determining the priority level of each functional defect source position of the control chip according to the working time point of each functional defect source position in the control chip, wherein the closer the working time point is to the testing time point of the control chip, the higher the priority level of the corresponding functional defect source position is;
s302: acquiring real-time test data of a functional defect source position belonging to a first priority level, calculating a difference value between the acquired real-time test data and corresponding matched adjusted standard monitoring data, determining a real-time change rate of the difference value, determining whether the calculated difference value is in an error range or not, if the calculated difference value is in the error range, comparing the determined real-time change rate with a voltage real-time change rate of a control chip in a corresponding time period, wherein the voltage real-time change rate is equal to the voltage real-time change rate of the control chip, and if the determined real-time change rate is not in the error range, the calculated difference value is the test voltage fluctuation of the control chip, and if the calculated difference value is not in the error range, the calculated difference value is the test voltage fluctuation of the control chip;
when the functional defect reason of the functional defect source position belonging to the first priority level is the test error of the control chip, correcting real-time test data generated by the control chip at the functional defect position belonging to the first priority level;
when the functional defect reason of the functional defect source position belonging to the first priority level is external electromagnetic signal interference, shielding an interference electromagnetic signal existing at the test position of the control chip;
when the functional defect reason of the functional defect source position belonging to the first priority level is the defect of the chip, stopping testing the control chip at the moment;
s303: according to the analysis method of the functional defect reasons belonging to the functional defect source positions of the first priority level in S302, the functional defect reasons of the functional defect source positions of other priority levels are automatically analyzed sequentially;
s40: and (3) performing functional repair processing on the control chip according to the analysis result in the step (S30), taking real-time test data of the repaired control chip as monitoring data, and analyzing fault conditions of other control chips through comparison results of the monitoring data and the real-time test data of the other control chips under the condition that the test environment temperatures of the control chips are the same.
The control chip automatic data processing system based on the Internet of things comprises a functional defect position determining module, a functional defect root position determining module, a functional defect cause analyzing module and an automatic data analyzing and processing module;
the functional defect position determining module is used for determining the functional defect position of the control chip according to the real-time test data of the control chip;
the functional defect position determining module comprises a module connecting unit, a delay time predicting unit and a functional defect position determining unit;
the module connecting unit connects the control chip with the application programming interface and connects the application programming interface with the data analysis terminal;
the delay time prediction unit acquires the real-time test environment temperature of the control chip, constructs a prediction model according to the acquired information, predicts the delay time of real-time test data generated by the control chip, and transmits a prediction result to the functional defect position determination unit;
the function defect position determining unit receives the prediction result transmitted by the delay time predicting unit, adjusts the generation time of the standard monitoring data based on the received information, compares the adjusted standard monitoring data with real-time test data received by the data analysis terminal, determines the function defect position of the control chip according to the comparison result, and transmits the determined function defect position to the function defect root position determining module;
the functional defect root position determining module is used for determining the functional defect root position of the control chip;
the functional defect root position determining module comprises a classifying unit, a relevance determining unit, a functional defect root position determining unit I and a functional defect root position determining unit II;
the classification unit receives the function defect positions transmitted by the function defect position determination unit, determines the function modules corresponding to the function defect positions, puts the function defect positions belonging to the same function module into the same set, and transmits the classification result to the association degree determination unit;
the association degree determining unit receives the classification result transmitted by the classifying unit, randomly selects one functional defect position in the same set, judges whether the selected functional defect position provides data support for each residual functional defect position in the same set, determines the association degree between the selected functional defect position and the corresponding functional defect position according to the judgment result, and transmits the determination result to the functional defect root position determining unit I or the functional defect root position determining unit II;
the first functional defect source position determining unit receives the determining result transmitted by the association degree determining unit when the association degree determined by the association degree determining unit is 0, determines the functional defect source position of the corresponding functional module based on the receiving information, performs elimination processing on the set according to the determining result, transmits the set after the elimination processing to the second functional defect source position determining unit, and transmits the determined functional defect source position to the functional defect cause analyzing module;
the second functional defect source position determining unit receives the determining result transmitted by the association degree determining unit and the set transmitted by the first functional defect source position determining unit when the association degree determined by the association degree determining unit is 1, determines the functional defect source position of the corresponding functional module based on the receiving information, and transmits the determining result to the functional defect cause analyzing module;
the functional defect reason analysis module is used for analyzing the functional defect reason of the control chip;
the functional defect cause analysis module comprises a priority level determination unit and a functional defect cause analysis unit;
the priority level determining unit receives the functional defect source positions transmitted by the first functional defect source position determining unit or the second functional defect source position determining unit, determines the priority level of each functional defect source position of the control chip according to the working time point of each functional defect source position in the control chip, and transmits the determination result to the functional defect cause analyzing unit;
the function defect cause analysis unit receives the determination result transmitted by the priority level determination unit, analyzes the function defect cause of the control chip based on the received information, and transmits the analysis result to the automatic data analysis processing module;
and the automatic data analysis processing module is used for automatically analyzing the fault conditions of other control chips.
The automatic data analysis processing module receives the analysis result transmitted by the functional defect cause analysis unit, performs functional repair processing on the control chip based on the received information, takes real-time test data of the repaired control chip as monitoring data, and performs automatic analysis on fault conditions of other control chips through comparison results of the monitoring data and the real-time test data of the other control chips under the condition that the test environment temperatures of the control chips are the same.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A control chip automatic data processing method based on the Internet of things is characterized in that: the method comprises the following steps:
s10: connecting the control chip with an application programming interface, connecting the application programming interface with a data analysis terminal, transmitting real-time test data of the control chip to the data analysis terminal through the application programming interface, and determining the functional defect position of the control chip by the data analysis terminal according to the received real-time test data;
s20: automatically associating the real-time test data of the control chip at each functional defect position, and determining the functional defect root position of the control chip according to the automatic association result;
s30: analyzing the functional defect reasons of the control chip according to the priority level of the functional defect source positions of the control chip;
s40: and (3) performing functional repair processing on the control chip according to the analysis result in the step (S30), taking real-time test data of the repaired control chip as monitoring data, and analyzing fault conditions of other control chips through comparison results of the monitoring data and the real-time test data of the other control chips under the condition that the test environment temperatures of the control chips are the same.
2. The control chip automation data processing method based on the internet of things according to claim 1, wherein the method comprises the following steps: the specific method for predicting the functional defect position of the control chip by the data analysis terminal according to the received real-time test data in the S10 is as follows:
acquiring the real-time test environment temperature of the control chip, judging whether the acquired test environment temperature is within a set test environment temperature range, setting the range value of the test environment temperature to be T 'to T', if so, indicating that the delay time of the real-time test data generated by the control chip is 0, and if not, constructing a prediction model to predict the delay time T of the real-time test data generated by the control chip;
dividing the acquired real-time test environment temperature according to the variation condition of the acquired real-time test environment temperature of the control chip, wherein the real-time test environment temperature values in each dividing section are identical, and the real-time test environment temperature values corresponding to adjacent dividing points are different;
the specific prediction model is as follows:
wherein u=1, 2, …, n represents the number corresponding to each division point, n represents the total number of division points, V represents the average running speed of the control chip in the set test environment temperature range, α represents the average running speed reduction value corresponding to every 1 ℃ decrease in the test environment temperature of the control chip, β represents the average running speed reduction value corresponding to every 1 ℃ increase in the test environment temperature of the control chip, T u Representing the corresponding real-time test environment temperature value of the control chip in the ith division segment, S u Indicating that the control chip is processing in the ith division segmentWhen T is u When the value of T' > 0 is exceeded,when T is u When T is less than or equal to 0,when T' -T u When less than or equal to 0, the weight is increased>When T' -T u At > 0>
And adjusting the generation time of the standard monitoring data according to the predicted delay time, comparing the adjusted standard monitoring data with real-time test data received by the data analysis terminal, and determining the functional defect position of the control chip according to the comparison result.
3. The control chip automation data processing method based on the internet of things according to claim 2, wherein the method comprises the following steps: the S20 includes:
s201: determining the function modules corresponding to the function defect positions, putting the function defect positions belonging to the same function module into the same set, randomly selecting one function defect position in the same set, judging whether the selected function defect position is the rest function defect positions in the same set, providing data support, if not, indicating that the association degree between the selected function defect position and the corresponding function defect position is 0, and if the data support is provided, indicating that the association degree between the selected function defect position and the corresponding function defect position is 1 until the function defect positions in the same set are all selected;
s202: when the association degree between the selected functional defect position and the corresponding functional defect position is 0, the selected functional defect position and the corresponding functional defect position are the functional defect root positions of the corresponding functional modules, and the functional defect root positions which are determined to be the corresponding functional modules are removed from the set;
when the association degree between the selected functional defect position and the corresponding functional defect position is 1, the number of times that each functional defect position in the set after the elimination processing appears when the association degree is 1 is obtained, if Ui > 1, whether the abnormal data amount existing in the corresponding functional defect position is equal to Ui or not is judged, if equal, the corresponding functional defect position is not the functional defect source position of the corresponding functional module, if not equal, the corresponding functional defect position is the functional defect source position of the corresponding functional module, the abnormal data amount existing in the corresponding functional defect position is equal to or more than Ui, if ui=1, the corresponding functional defect position is the functional defect source position of the corresponding functional module, i=1, 2, …, m represents the number corresponding to each functional defect position in the set after the elimination processing, m represents the total number of the functional defect positions, ui represents the number of times that the functional defect position with the number i appears when the association degree is 1 when the association degree analysis is performed.
4. The control chip automation data processing method based on the internet of things according to claim 3, wherein the method comprises the following steps: the S30 includes:
s301: determining the priority level of each functional defect source position of the control chip according to the working time point of each functional defect source position in the control chip, wherein the closer the working time point is to the testing time point of the control chip, the higher the priority level of the corresponding functional defect source position is;
s302: acquiring real-time test data of the functional defect source position belonging to the first priority level, calculating a difference value between the acquired real-time test data and the corresponding matched adjusted standard monitoring data, determining a real-time change rate of the difference value, comparing the determined real-time change rate with a real-time change rate of voltage of a control chip in a corresponding time period, if the two change rates are the same, judging whether the calculated difference value is in an error range or not, if the calculated difference value is in the error range, indicating that the functional defect source position belonging to the first priority level is a test error of the control chip, and if the calculated difference value is not in the error range, indicating that the functional defect source position belonging to the first priority level is external electromagnetic signal interference or chip self defect;
when the functional defect reason of the functional defect source position belonging to the first priority level is the test error of the control chip, correcting real-time test data generated by the control chip at the functional defect position belonging to the first priority level;
when the functional defect reason of the functional defect source position belonging to the first priority level is external electromagnetic signal interference, shielding an interference electromagnetic signal existing at the test position of the control chip;
when the functional defect reason of the functional defect source position belonging to the first priority level is the defect of the chip, stopping testing the control chip at the moment;
s303: according to the analysis method of the functional defect cause of the functional defect source position belonging to the first priority level in S302, the functional defect causes of the functional defect source positions of other priority levels are automatically analyzed sequentially.
5. An automated data processing system of control chip based on internet of things applied to the automated data processing method of control chip based on internet of things of any one of claims 1-4, characterized in that: the system comprises a functional defect position determining module, a functional defect root position determining module, a functional defect cause analyzing module and an automatic data analyzing and processing module;
the functional defect position determining module is used for determining the functional defect position of the control chip according to the real-time test data of the control chip;
the functional defect source position determining module is used for determining the functional defect source position of the control chip;
the functional defect reason analysis module is used for analyzing the functional defect reason of the control chip;
the automatic data analysis processing module is used for automatically analyzing fault conditions of other control chips.
6. The control chip automation data processing system based on the internet of things according to claim 5, wherein: the functional defect position determining module comprises a module connecting unit, a delay time predicting unit and a functional defect position determining unit;
the module connecting unit connects the control chip with the application programming interface and connects the application programming interface with the data analysis terminal;
the delay time prediction unit acquires the real-time test environment temperature of the control chip, constructs a prediction model according to the acquired information, predicts the delay time of real-time test data generated by the control chip, and transmits a prediction result to the functional defect position determination unit;
the functional defect position determining unit receives the prediction result transmitted by the delay time predicting unit, adjusts the generation time of the standard monitoring data based on the received information, compares the adjusted standard monitoring data with real-time test data received by the data analysis terminal, determines the functional defect position of the control chip according to the comparison result, and transmits the determined functional defect position to the functional defect root position determining module.
7. The control chip automation data processing system based on the internet of things according to claim 6, wherein: the functional defect root position determining module comprises a classifying unit, a relevance determining unit, a functional defect root position determining unit I and a functional defect root position determining unit II;
the classifying unit receives the function defect positions transmitted by the function defect position determining unit, determines the function modules corresponding to the function defect positions, puts the function defect positions belonging to the same function module into the same set, and transmits the classifying result to the association degree determining unit;
the association degree determining unit receives the classification result transmitted by the classifying unit, randomly selects one functional defect position in the same set, judges whether the selected functional defect position provides data support for each residual functional defect position in the same set, determines the association degree between the selected functional defect position and the corresponding functional defect position according to the judgment result, and transmits the determination result to the functional defect root position determining unit I or the functional defect root position determining unit II;
the first functional defect source position determining unit receives the determining result transmitted by the association degree determining unit when the association degree determined by the association degree determining unit is 0, determines the functional defect source position of the corresponding functional module based on the receiving information, performs rejection processing on the set according to the determining result, transmits the set after the rejection processing to the second functional defect source position determining unit, and transmits the determined functional defect source position to the functional defect cause analyzing module;
and the second functional defect source position determining unit receives the determining result transmitted by the association degree determining unit and the set transmitted by the first functional defect source position determining unit when the association degree determined by the association degree determining unit is 1, determines the functional defect source position of the corresponding functional module based on the receiving information, and transmits the determining result to the functional defect cause analyzing module.
8. The control chip automation data processing system based on the internet of things of claim 7, wherein: the functional defect cause analysis module comprises a priority level determination unit and a functional defect cause analysis unit;
the priority level determining unit receives the functional defect source positions transmitted by the first functional defect source position determining unit or the second functional defect source position determining unit, determines the priority level of each functional defect source position of the control chip according to the working time point of each functional defect source position in the control chip, and transmits the determination result to the functional defect cause analyzing unit;
the functional defect cause analysis unit receives the determination result transmitted by the priority level determination unit, analyzes the functional defect cause of the control chip based on the received information, and transmits the analysis result to the automatic data analysis processing module.
9. The control chip automation data processing system based on the internet of things of claim 8, wherein: the automatic data analysis processing module receives the analysis result transmitted by the functional defect cause analysis unit, performs functional repair processing on the control chip based on the received information, takes real-time test data of the repaired control chip as monitoring data, and performs automatic analysis on fault conditions of other control chips through comparison results of the monitoring data and the real-time test data of the other control chips under the condition that the test environment temperatures of the control chips are the same.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311717267.8A CN117762069B (en) | 2023-12-14 | 2023-12-14 | Control chip automatic data processing system and method based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311717267.8A CN117762069B (en) | 2023-12-14 | 2023-12-14 | Control chip automatic data processing system and method based on Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117762069A true CN117762069A (en) | 2024-03-26 |
| CN117762069B CN117762069B (en) | 2024-06-18 |
Family
ID=90319183
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311717267.8A Active CN117762069B (en) | 2023-12-14 | 2023-12-14 | Control chip automatic data processing system and method based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117762069B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5787190A (en) * | 1995-06-07 | 1998-07-28 | Advanced Micro Devices, Inc. | Method and apparatus for pattern recognition of wafer test bins |
| KR20180086865A (en) * | 2017-01-24 | 2018-08-01 | 삼성에스디에스 주식회사 | Fault detecting apparatus and method for determining optimal fault detecting rule thereof |
| CN110376430A (en) * | 2019-07-17 | 2019-10-25 | 广州市伟粤通讯设备有限公司 | A kind of communication component evaluation system based on big data |
| CN110442517A (en) * | 2019-07-18 | 2019-11-12 | 暨南大学 | A kind of auto-programming obtains the method for security patch in repairing |
| CN110797072A (en) * | 2019-10-31 | 2020-02-14 | 西安紫光国芯半导体有限公司 | DRAM chip repairing method |
| CN113807046A (en) * | 2021-10-09 | 2021-12-17 | 中国人民解放军国防科技大学 | Test excitation optimization regression verification method, system and medium |
| CN116068378A (en) * | 2023-02-17 | 2023-05-05 | 光子集成(温州)创新研究院 | Optical chip production detection system and method based on Internet of things |
| CN116596510A (en) * | 2023-05-15 | 2023-08-15 | 浙江创智科技股份有限公司 | Operation and maintenance fault management method, system, terminal equipment and storage medium |
| CN116955955A (en) * | 2023-07-28 | 2023-10-27 | 河南博锐流体设备有限公司 | Pipeline defect model prediction method, system, terminal equipment and storage medium |
-
2023
- 2023-12-14 CN CN202311717267.8A patent/CN117762069B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5787190A (en) * | 1995-06-07 | 1998-07-28 | Advanced Micro Devices, Inc. | Method and apparatus for pattern recognition of wafer test bins |
| KR20180086865A (en) * | 2017-01-24 | 2018-08-01 | 삼성에스디에스 주식회사 | Fault detecting apparatus and method for determining optimal fault detecting rule thereof |
| CN110376430A (en) * | 2019-07-17 | 2019-10-25 | 广州市伟粤通讯设备有限公司 | A kind of communication component evaluation system based on big data |
| CN110442517A (en) * | 2019-07-18 | 2019-11-12 | 暨南大学 | A kind of auto-programming obtains the method for security patch in repairing |
| CN110797072A (en) * | 2019-10-31 | 2020-02-14 | 西安紫光国芯半导体有限公司 | DRAM chip repairing method |
| CN113807046A (en) * | 2021-10-09 | 2021-12-17 | 中国人民解放军国防科技大学 | Test excitation optimization regression verification method, system and medium |
| CN116068378A (en) * | 2023-02-17 | 2023-05-05 | 光子集成(温州)创新研究院 | Optical chip production detection system and method based on Internet of things |
| CN116596510A (en) * | 2023-05-15 | 2023-08-15 | 浙江创智科技股份有限公司 | Operation and maintenance fault management method, system, terminal equipment and storage medium |
| CN116955955A (en) * | 2023-07-28 | 2023-10-27 | 河南博锐流体设备有限公司 | Pipeline defect model prediction method, system, terminal equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| SSU-HAN CHEN: "SMD LED chips defect detection using a YOLOV3-dense model", 《ADVANCED ENGINEERING INFORMATICS》, 6 February 2021 (2021-02-06) * |
| 刘东;邹波;李刚;骆凯波;何蓓;: "基于数据挖掘的电网检测数据分析", 舰船电子工程, no. 07, 20 July 2018 (2018-07-20) * |
| 白云;章鹿华;: "基于大数据的电网输电线路缺陷与故障关联分析研究", 电工文摘, no. 05, 20 October 2017 (2017-10-20) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117762069B (en) | 2024-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20170060664A1 (en) | Method for verifying bad pattern in time series sensing data and apparatus thereof | |
| CN115308562A (en) | Chip testing method and related equipment | |
| CN104425300B (en) | The method of sampling and device are measured in product | |
| KR20060006723A (en) | Methods and apparatus for test process enhancement | |
| US11404331B2 (en) | System and method for determining cause of abnormality in semiconductor manufacturing processes | |
| US20140278234A1 (en) | Method and a system for a statistical equivalence test | |
| CN116008790A (en) | Chip aging test system and method | |
| CN113448298A (en) | Data acquisition system for automatic production equipment | |
| CN116125242A (en) | Object detection method and system | |
| US6850811B1 (en) | Analyzing error signals based on fault detection | |
| CN116483623A (en) | Data storage system and storage equipment based on cloud computing | |
| CN109816191A (en) | The qualitative forecasting method and its system of Multi-workstation System | |
| CN117762069B (en) | Control chip automatic data processing system and method based on Internet of things | |
| CN110274844B (en) | Method and device for diagnosing drying process in sintered fuel grain composition detection system | |
| US20080004829A1 (en) | Method and apparatus for automatic test equipment | |
| CN113655370A (en) | Method, device and system for determining abnormal test working condition of chip and related equipment | |
| US6697691B1 (en) | Method and apparatus for fault model analysis in manufacturing tools | |
| CN116167604A (en) | Intelligent management system for power product production based on production full-flow tracking | |
| CN115792583A (en) | Test method, device, equipment and medium for vehicle gauge chip | |
| CN115684869A (en) | Chip testing system and method based on power management | |
| US20050075835A1 (en) | System and method of real-time statistical bin control | |
| CN112445632A (en) | HPC reliability evaluation method based on fault data modeling | |
| US20090018788A1 (en) | Normalization of process variables in a manufacturing process | |
| CN110749813B (en) | Test system and method for generating adaptive test recipe | |
| CN110489805B (en) | Method for predicting BIT false alarm rate of airborne electronic information equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |