CN113421065B - Intelligent sorting system for semiconductor production based on Internet of things - Google Patents
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- 239000004065 semiconductor Substances 0.000 title claims abstract description 132
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 77
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- 238000004364 calculation method Methods 0.000 claims abstract description 16
- 238000004140 cleaning Methods 0.000 claims abstract description 16
- 238000010606 normalization Methods 0.000 claims description 6
- 108010076504 Protein Sorting Signals Proteins 0.000 claims description 5
- 239000000654 additive Substances 0.000 claims description 3
- 230000000996 additive effect Effects 0.000 claims description 3
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Abstract
The invention relates to an intelligent sorting system for semiconductor production based on the Internet of things, which comprises a processor, a semiconductor module, a detection module, a processing statistics module, a calculation analysis module and a sorting module; the semiconductor module collects data information of the semiconductor, wherein the data information comprises type data, size data and processing data of the semiconductor; cleaning the data information; the detection module is used for detecting the sorted semiconductors to obtain detection information, and the detection information comprises crack data and duration data; cleaning the detection information; the processing statistics module is used for calculating the cleaned data information and the cleaned detection information to obtain an ontology value and a detection value; the invention solves the technical problems that the existing scheme can not comprehensively analyze the processing condition and the detection condition of the semiconductors and sort and transport the semiconductors which do not meet the quality standard in the transportation process.
Description
Technical Field
The invention relates to the technical field of semiconductor production, in particular to an intelligent sorting system for semiconductor production based on the Internet of things.
Background
Semiconductors refer to materials with conductivity properties between conductors and insulators at normal temperatures, and are used in integrated circuits, consumer electronics, communication systems, photovoltaic power generation, lighting, high-power conversion, and other fields.
In the existing semiconductor production process, comprehensive analysis cannot be performed on the processing condition and the detection condition of the semiconductor, and the semiconductor which does not meet the quality standard is sorted and transported in the transportation process.
Disclosure of Invention
The invention aims to provide an intelligent sorting system for semiconductor production based on the Internet of things, which mainly aims to solve the technical problems that the processing condition and the detection condition of semiconductors cannot be comprehensively analyzed in the existing scheme, and semiconductors which do not meet quality standards are sorted and transported in the transportation process.
The aim of the invention can be achieved by the following technical method:
an intelligent sorting system for semiconductor production based on the Internet of things comprises a processor, a semiconductor module, a detection module, a processing statistics module, a calculation analysis module and a sorting module;
the semiconductor module collects data information of the semiconductor, wherein the data information comprises type data, size data and processing data of the semiconductor; cleaning the data information to obtain type processing data, size processing data and processing data;
the detection module is used for detecting the sorted semiconductors to obtain detection information, and the detection information comprises crack data and duration data; cleaning the detection information to obtain detection cleaning information;
the processing statistics module is used for calculating the cleaned data information and the cleaned detection information to obtain an ontology value and a detection value;
the calculation and analysis module is used for carrying out calculation and analysis according to the body value and the detection value to obtain sorting data of the semiconductor;
the sorting module sorts the semiconductors according to sorting data; the processor is used for processing the data in each module.
Further, the data information is cleaned, including:
receiving data information and acquiring type data, size data and processing data of a semiconductor;
marking the semiconductor type in the type data and acquiring a corresponding semi-class association value;
respectively taking values and marking the semiconductor area and the semiconductor thickness in the size data;
and marking the processing type in the processing data and obtaining a corresponding class adding preset value.
Further, cleaning the detection information includes:
receiving detection information and acquiring detected crack data and duration data;
respectively taking values and marking the number of cracks and the area of the cracks in the crack data;
and (5) taking the value and marking the detection duration in the duration data.
Further, the processing statistics module is configured to calculate the cleaned data information and the cleaned detection information to obtain an ontology value and a detection value, and includes:
carrying out normalization processing and value taking on each item of data marked in the data information, and utilizing a formulaCalculating to obtain a bulk value BT of the semiconductor; wherein a1, a2, a3 and a4 are expressed as different proportionality coefficients, BLGi is expressed as a half-class association value corresponding to a semiconductor type, JLYi is expressed as an additive preset value corresponding to a processing type, BMi is expressed as a semiconductor area, BHi is expressed as a semiconductor thickness, i=1, 2, 3..n;
carrying out normalization processing and value taking on each item of data marked in the detection information, and utilizing a formulaCalculating to obtain a detection value JC of the semiconductor; where b1 and b2 are expressed as different scaling factors, LSi is expressed as the number of cracks, LMi is expressed as the crack area, JSi is expressed as the detection duration, i=1, 2,3.
Further, the calculation and analysis module is configured to perform calculation and analysis according to the ontology value and the detection value, to obtain sorting data of the semiconductor, and includes:
acquiring the corresponding bulk value BT and detection value JC of different semiconductors by using a formulaCalculating to obtain a sorting value FJ of the semiconductor; wherein c1 and c2 are represented as different scaling factors;
calculating the ratio between the sorting value and the sorting threshold value, obtaining the integer part of the ratio, marking the integer part as K, and analyzing the K; if K is less than G, generating a first sorting signal; if G+1 is more than K and is more than or equal to G, generating a second sorting signal; if K is more than or equal to G+1, generating a third sorting signal; wherein G is expressed as a preset sorting integer;
the sorting value is combined with the first sorting signal, the second sorting signal and the third sorting signal to obtain sorting data.
Further, the sorting module sorts the semiconductors according to sorting data, including:
and acquiring sorting signals corresponding to the semiconductors in the sorting data, and sorting and transferring the semiconductors by using the sorting signals.
The invention has the beneficial effects that:
collecting data information of a semiconductor through a semiconductor module, and cleaning the data information; by collecting and processing the type data, the size data and the processing data of the semiconductor, effective data support is provided for the detection of the semiconductor, and the detection accuracy can be improved;
detecting the sorted semiconductors through a detection module to obtain detection information, and cleaning the detection information; by detecting and processing the semiconductors, effective data support is provided for sorting the semiconductors, so that different semiconductors can be sorted in different modes;
calculating the cleaned data information and detection information through a processing statistics module to obtain an ontology value and a detection value; by calculating the collected and processed data, the data are connected, so that the whole analysis is convenient, and the effectiveness of the data analysis can be improved;
calculating and analyzing according to the ontology value and the detection value by a calculation and analysis module to obtain sorting data of the semiconductor; sorting the semiconductors according to the sorting data through a sorting module; the method can comprehensively analyze the processing condition and the detection condition of the semiconductors, and achieves the purposes of sorting and transferring the semiconductors which do not meet the quality standard in the transportation process.
Drawings
The invention is further described with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a module of an intelligent sorting system for semiconductor production based on the internet of things.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention for achieving the intended purpose, the following detailed description will refer to the specific implementation, structure, characteristics and effects according to the present invention with reference to the accompanying drawings and preferred embodiments.
Referring to fig. 1, the invention discloses an intelligent sorting system for semiconductor production based on the internet of things, which comprises a processor, a semiconductor module, a detection module, a processing statistics module, a calculation analysis module and a sorting module;
in the semiconductor industry, infrared industrial cameras can be used to detect the quality of pure semiconductor materials, and in addition, ingots and wafer finished products cut into wafers, defects or cracks can be detected in a similar way, and then the wafers are processed into optoelectronic components or other semiconductor devices; in the processing process of cutting a wafer into a single chip, for saw blade and laser calibration, an infrared industrial camera is still the mainstream scheme applied at present, in the embodiment of the invention, the infrared industrial camera is used for detecting whether the quality of the semiconductor is qualified or not according to the crack and the quantity condition of the semiconductor, and the semiconductors which do not meet the quality standard are sorted and transported in time by comprehensively analyzing the production condition and the detection condition of different types of semiconductors.
The semiconductor module collects data information of the semiconductor, wherein the data information comprises type data, size data and processing data of the semiconductor; cleaning the data information to obtain type processing data, size processing data and processing data; comprising the following steps:
receiving data information and acquiring type data, size data and processing data of a semiconductor;
marking the semiconductor type in the type data, acquiring a corresponding semi-class association value, and marking the semiconductor type as BLi, i=1, 2, 3..n; setting different semiconductor types to correspond to different half-class association values, matching the semiconductor types in the type data with all the semiconductor types to obtain corresponding half-class association values, and marking the half-class association values as BLGi; the marked semiconductor type and the corresponding semi-class association value form type processing data;
respectively taking values and marking the semiconductor area and the semiconductor thickness in the size data, and marking the semiconductor area as BMi; marking the semiconductor thickness as BHi; the marked semiconductor area and semiconductor thickness constitute dimensional processing data;
marking the processing type in the processing data, acquiring a corresponding class adding preset value, and marking the processing type as JLi; setting different machining types to correspond to different class adding preset values, matching the machining types in the machining data with all the machining types to obtain corresponding class adding preset values, and marking the class adding preset values as JLYi; the marked processing type and the corresponding processing type preset value form processing data; the processing type includes, but is not limited to, wafer growth technology, thin film deposition, lithography, etching, doping technology, process integration, and the like; for example, if the sorted semiconductor has completed the photolithography step, the processing type corresponds to the photolithography type;
the detection module is used for detecting the sorted semiconductors to obtain detection information, and the detection information comprises crack data and duration data; cleaning the detection information to obtain detection cleaning information; comprising the following steps:
receiving detection information and acquiring detected crack data and duration data;
respectively taking values and marking the number of cracks and the area of the cracks in the crack data, and marking the number of the cracks as LSi; the crack area is marked as LMi;
the detection duration in the duration data is valued and marked, and the detection duration is marked as JSi;
the processing statistics module is used for calculating the cleaned data information and the cleaned detection information to obtain an ontology value and a detection value; comprising the following steps:
carrying out normalization processing and value taking on each item of data marked in the data information, and utilizing a formulaCalculating to obtain a bulk value BT of the semiconductor; wherein a1, a2, a3 and a4 are expressed as different proportionality coefficients, BLGi is expressed as a half-class association value corresponding to a semiconductor type, JLYi is expressed as an additive preset value corresponding to a processing type, BMi is expressed as a semiconductor area, BHi is expressed as a semiconductor thickness, i=1, 2, 3..n;
carrying out normalization processing and value taking on each item of data marked in the detection information, and utilizing a formulaCalculating to obtain a detection value JC of the semiconductor; wherein b1 and b2 are expressed as different scaling factors, LSi is expressed as the number of cracks, LMi is expressed as the area of the cracks, JSi is expressed as the detection duration, i=1, 2, 3..n;
in the embodiment of the invention, the conditions of the semiconductor are analyzed from the semiconductor type and the corresponding semiconductor type association value, the semiconductor area and the semiconductor thickness, the processing type and the corresponding addition type preset value thereof, so that data support is provided for subsequent quality detection; analyzing the detection condition of the semiconductor from the number of cracks, the area of the cracks and the detection time length, and providing data support for the subsequent sorting of the semiconductor.
The calculation and analysis module is used for carrying out calculation and analysis according to the body value and the detection value to obtain sorting data of the semiconductor; comprising the following steps:
acquiring the corresponding bulk value BT and detection value JC of different semiconductors by using a formulaCalculating to obtain a sorting value FJ of the semiconductor; wherein c1 and c2 are represented as different scaling factors;
calculating the ratio between the sorting value and the sorting threshold value, obtaining the integer part of the ratio, marking the integer part as K, and analyzing the K; if K is less than G, generating a first sorting signal; if G+1 is more than K and is more than or equal to G, generating a second sorting signal; if K is more than or equal to G+1, generating a third sorting signal; wherein G is a preset sorting integer, and G can take a value of 1, for example, the ratio between the sorting value and the sorting threshold is 2.5, and K is 2, so that the value meets K and is more than or equal to G+1, and a third sorting signal is generated;
combining the sorting value with the first sorting signal, the second sorting signal and the third sorting signal to obtain sorting data;
in the embodiment of the invention, the detected semiconductors are sorted according to the first sorting signal, the second sorting signal and the third sorting signal, wherein the first sorting signal indicates that the corresponding semiconductors have no cracks; the second sorting signal indicates that the corresponding semiconductor has a small number of cracks meeting the quality standard; the third sorting signal indicates that the corresponding semiconductor has a small amount of cracks which do not meet the quality standard; the quality standard is set based on existing semiconductor quality inspection standards.
The sorting module sorts the semiconductors according to sorting data; the processor is used for processing the data in each module; comprising the following steps:
acquiring sorting signals corresponding to semiconductors in sorting data, and sorting and transferring the semiconductors by using the sorting signals; sorting the corresponding semiconductors to a first conveyor belt for conveying according to the first sorting signals; sorting the corresponding semiconductors to a second conveyor belt for conveying according to the second sorting signals; sorting the corresponding semiconductors to a third conveyor belt for conveying according to the third sorting signals;
the formulas in the invention are all formulas with dimensions removed and numerical calculation, and a formula closest to the actual situation is obtained by collecting a large amount of data and performing software simulation, and preset proportionality coefficients and thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating the large amount of data.
The present invention is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present invention.
Claims (2)
1. The intelligent sorting system for the semiconductor production based on the Internet of things is characterized by comprising a processor, a semiconductor module, a detection module, a processing statistics module, a calculation analysis module and a sorting module;
the semiconductor module collects data information of the semiconductor, wherein the data information comprises type data, size data and processing data of the semiconductor; cleaning the data information to obtain type processing data, size processing data and processing data;
the detection module is used for detecting the sorted semiconductors to obtain detection information, and the detection information comprises crack data and duration data; cleaning the detection information to obtain detection cleaning information;
the processing statistics module is used for calculating the cleaned data information and the cleaned detection information to obtain an ontology value and a detection value;
the calculation and analysis module is used for carrying out calculation and analysis according to the body value and the detection value to obtain sorting data of the semiconductor;
the sorting module sorts the semiconductors according to sorting data; the processor is used for processing the data in each module;
cleaning the data information, including:
receiving data information and acquiring type data, size data and processing data of a semiconductor;
marking the semiconductor type in the type data and acquiring a corresponding semi-class association value;
respectively taking values and marking the semiconductor area and the semiconductor thickness in the size data;
marking the processing type in the processing data and obtaining a corresponding class adding preset value;
cleaning the detection information, including:
receiving detection information and acquiring detected crack data and duration data;
respectively taking values and marking the number of cracks and the area of the cracks in the crack data;
the detection duration in the duration data is valued and marked;
the processing statistics module is used for calculating the cleaned data information and detection information to obtain an ontology value and a detection value, and comprises the following steps:
carrying out normalization processing and value taking on each item of data marked in the data information, and utilizing a formulaCalculating to obtain a bulk value BT of the semiconductor; wherein a1, a2, a3 and a4 are expressed as different proportionality coefficients, BLGi is expressed as a half-class association value corresponding to a semiconductor type, JLYi is expressed as an additive preset value corresponding to a processing type, BMi is expressed as a semiconductor area, BHi is expressed as a semiconductor thickness, i=1, 2, 3..n;
carrying out normalization processing and value taking on each item of data marked in the detection information, and utilizing a formulaCalculating to obtain a detection value JC of the semiconductor; wherein b1 and b2 are expressed as different scaling factors, LSi is expressed as the number of cracks, LMi is expressed as the area of the cracks, JSi is expressed as the detection duration, i=1, 2, 3..n;
the calculation and analysis module is used for carrying out calculation and analysis according to the body value and the detection value to obtain sorting data of the semiconductor, and comprises the following steps:
acquiring the corresponding bulk value BT and detection value JC of different semiconductors by using a formulaCalculating to obtain a sorting value FJ of the semiconductor; wherein c1 and c2 are represented as different scaling factors;
calculating the ratio between the sorting value and the sorting threshold value, obtaining the integer part of the ratio, marking the integer part as K, and analyzing the K; if K is less than G, generating a first sorting signal; if G+1 is more than K and is more than or equal to G, generating a second sorting signal; if K is more than or equal to G+1, generating a third sorting signal; wherein G is expressed as a preset sorting integer;
the sorting value is combined with the first sorting signal, the second sorting signal and the third sorting signal to obtain sorting data.
2. The intelligent sorting system for semiconductor production based on the internet of things according to claim 1, wherein the sorting module sorts the semiconductors according to sorting data, comprising:
and acquiring sorting signals corresponding to the semiconductors in the sorting data, and sorting and transferring the semiconductors by using the sorting signals.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107390650A (en) * | 2016-11-18 | 2017-11-24 | 厦门嵘拓物联科技有限公司 | A kind of data collecting system based on Internet of Things and the data compression method based on the system |
| CN110763700A (en) * | 2019-10-22 | 2020-02-07 | 深选智能科技(南京)有限公司 | Method and equipment for detecting defects of semiconductor component |
| CN112613362A (en) * | 2020-12-10 | 2021-04-06 | 合肥派腾智能设备科技有限公司 | Article mark identification system based on Internet of things |
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|---|---|---|---|---|
| CN107390650A (en) * | 2016-11-18 | 2017-11-24 | 厦门嵘拓物联科技有限公司 | A kind of data collecting system based on Internet of Things and the data compression method based on the system |
| CN110763700A (en) * | 2019-10-22 | 2020-02-07 | 深选智能科技(南京)有限公司 | Method and equipment for detecting defects of semiconductor component |
| CN112613362A (en) * | 2020-12-10 | 2021-04-06 | 合肥派腾智能设备科技有限公司 | Article mark identification system based on Internet of things |
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