CN115595667B - Intelligent growth method, system, equipment and storage medium for tellurium-zinc-cadmium crystals - Google Patents
Intelligent growth method, system, equipment and storage medium for tellurium-zinc-cadmium crystals Download PDFInfo
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- CN115595667B CN115595667B CN202211282330.5A CN202211282330A CN115595667B CN 115595667 B CN115595667 B CN 115595667B CN 202211282330 A CN202211282330 A CN 202211282330A CN 115595667 B CN115595667 B CN 115595667B
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- 239000013078 crystal Substances 0.000 title claims abstract description 214
- 229910052793 cadmium Inorganic materials 0.000 title claims abstract description 171
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000003860 storage Methods 0.000 title claims abstract description 7
- 238000004088 simulation Methods 0.000 claims abstract description 21
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 230000008569 process Effects 0.000 claims description 22
- 238000012512 characterization method Methods 0.000 claims description 21
- 239000007788 liquid Substances 0.000 claims description 17
- 238000012544 monitoring process Methods 0.000 claims description 17
- 239000011701 zinc Substances 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 12
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 10
- 229910052714 tellurium Inorganic materials 0.000 claims description 10
- PORWMNRCUJJQNO-UHFFFAOYSA-N tellurium atom Chemical compound [Te] PORWMNRCUJJQNO-UHFFFAOYSA-N 0.000 claims description 10
- 239000010453 quartz Substances 0.000 claims description 9
- 238000005204 segregation Methods 0.000 claims description 9
- NSRBDSZKIKAZHT-UHFFFAOYSA-N tellurium zinc Chemical compound [Zn].[Te] NSRBDSZKIKAZHT-UHFFFAOYSA-N 0.000 claims description 9
- 238000009826 distribution Methods 0.000 claims description 8
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 claims description 7
- 229910052725 zinc Inorganic materials 0.000 claims description 7
- 239000000047 product Substances 0.000 claims description 6
- 230000008878 coupling Effects 0.000 claims description 4
- 238000010168 coupling process Methods 0.000 claims description 4
- 238000005859 coupling reaction Methods 0.000 claims description 4
- 239000002244 precipitate Substances 0.000 claims description 4
- 238000002834 transmittance Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 description 9
- 238000012549 training Methods 0.000 description 9
- 238000012360 testing method Methods 0.000 description 7
- QWUZMTJBRUASOW-UHFFFAOYSA-N cadmium tellanylidenezinc Chemical compound [Zn].[Cd].[Te] QWUZMTJBRUASOW-UHFFFAOYSA-N 0.000 description 4
- 238000010438 heat treatment Methods 0.000 description 4
- 229910000661 Mercury cadmium telluride Inorganic materials 0.000 description 3
- MCMSPRNYOJJPIZ-UHFFFAOYSA-N cadmium;mercury;tellurium Chemical compound [Cd]=[Te]=[Hg] MCMSPRNYOJJPIZ-UHFFFAOYSA-N 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000017525 heat dissipation Effects 0.000 description 3
- 238000012706 support-vector machine Methods 0.000 description 3
- MCMNRKCIXSYSNV-UHFFFAOYSA-N Zirconium dioxide Chemical compound O=[Zr]=O MCMNRKCIXSYSNV-UHFFFAOYSA-N 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 230000009977 dual effect Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- PXXKQOPKNFECSZ-UHFFFAOYSA-N platinum rhodium Chemical compound [Rh].[Pt] PXXKQOPKNFECSZ-UHFFFAOYSA-N 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 229910052580 B4C Inorganic materials 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- INAHAJYZKVIDIZ-UHFFFAOYSA-N boron carbide Chemical compound B12B3B4C32B41 INAHAJYZKVIDIZ-UHFFFAOYSA-N 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 229910002804 graphite Inorganic materials 0.000 description 1
- 239000010439 graphite Substances 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- HBMJWWWQQXIZIP-UHFFFAOYSA-N silicon carbide Chemical compound [Si+]#[C-] HBMJWWWQQXIZIP-UHFFFAOYSA-N 0.000 description 1
- 229910010271 silicon carbide Inorganic materials 0.000 description 1
- 238000007711 solidification Methods 0.000 description 1
- 230000008023 solidification Effects 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C30—CRYSTAL GROWTH
- C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
- C30B29/00—Single crystals or homogeneous polycrystalline material with defined structure characterised by the material or by their shape
- C30B29/10—Inorganic compounds or compositions
- C30B29/46—Sulfur-, selenium- or tellurium-containing compounds
- C30B29/48—AIIBVI compounds wherein A is Zn, Cd or Hg, and B is S, Se or Te
-
- C—CHEMISTRY; METALLURGY
- C30—CRYSTAL GROWTH
- C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
- C30B11/00—Single-crystal growth by normal freezing or freezing under temperature gradient, e.g. Bridgman-Stockbarger method
- C30B11/006—Controlling or regulating
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/10—Analysis or design of chemical reactions, syntheses or processes
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- Chemical & Material Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Engineering & Computer Science (AREA)
- Organic Chemistry (AREA)
- Materials Engineering (AREA)
- Metallurgy (AREA)
- Inorganic Chemistry (AREA)
- Analytical Chemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Life Sciences & Earth Sciences (AREA)
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- Bioinformatics & Computational Biology (AREA)
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- Theoretical Computer Science (AREA)
- Crystals, And After-Treatments Of Crystals (AREA)
Abstract
The application discloses an intelligent growth method, system, equipment and storage medium for tellurium-zinc-cadmium crystals, wherein the method comprises the following steps: the terminal receives initial growth conditions of tellurium-zinc-cadmium crystals sent by the microcomputer, and inputs the initial growth conditions of the tellurium-zinc-cadmium crystals into a crystal growth model for calculation to obtain temperature parameters required by growth of the tellurium-zinc-cadmium crystals; the terminal judges whether high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameters by carrying out simulation treatment on the initial growth conditions of the tellurium-zinc-cadmium crystals and the temperature parameters; when judging that high-quality tellurium-zinc-cadmium crystals can grow under the temperature parameters, the terminal sends the temperature parameters to the microcomputer, so that the microcomputer controls temperature information in the tellurium-zinc-cadmium crystal growing furnace according to the temperature parameters.
Description
Technical Field
The application relates to the technical field, in particular to an intelligent growth method, system and equipment for tellurium-zinc-cadmium crystals and a storage medium.
Background
Cadmium zinc telluride (Cd) 1-x Zn x Te, CZT) an important group II-VI ternary compound semiconductor, is widely used for manufacturing nuclear radiation detectors due to its good photoelectric properties. In addition, the lattice constant of the tellurium-zinc-cadmium crystal can be continuously regulated and controlled along with the change of zinc content to realize the complete matching with the lattice of Mercury Cadmium Telluride (MCT) with any components, and the tellurium-zinc-cadmium crystal has higher infrared transmittance, so that the tellurium-zinc-cadmium crystal is the optimal substrate material for the epitaxial growth of mercury cadmium telluride.
The growth of tellurium-zinc-cadmium is difficult, the growth repeatability is poor, and the high-quality tellurium-zinc-cadmium crystal is difficult to grow at lower cost due to the factors of high growth temperature, low heat conductivity, small stacking fault energy, high balance vapor pressure of Cd components, large Zn segregation coefficient, large difference of temperature parameters required for growing tellurium-zinc-cadmium crystals with different zinc values and the like.
Disclosure of Invention
The technical problem solved by the scheme provided by the embodiment of the application is that the growth of the tellurium-zinc-cadmium crystal cannot be intelligently provided with proper temperature parameters, so that the tellurium-zinc-cadmium crystal has low single crystal rate and poor growth repeatability, and the high-quality tellurium-zinc-cadmium crystal is difficult to grow at low cost.
The intelligent growth method of the tellurium-zinc-cadmium crystal provided by the embodiment of the application comprises the following steps:
the terminal receives initial growth conditions of tellurium-zinc-cadmium crystals sent by the microcomputer, and inputs the initial growth conditions of the tellurium-zinc-cadmium crystals into a crystal growth model for calculation to obtain temperature parameters required by growth of the tellurium-zinc-cadmium crystals;
the terminal judges whether high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameters by carrying out simulation treatment on the initial growth conditions of the tellurium-zinc-cadmium crystals and the temperature parameters;
when judging that high-quality tellurium-zinc-cadmium crystals can grow under the temperature parameters, the terminal sends the temperature parameters to the microcomputer, so that the microcomputer controls temperature information in the tellurium-zinc-cadmium crystal growing furnace according to the temperature parameters.
According to the embodiment of the application, the intelligent growth system for tellurium-zinc-cadmium crystals comprises:
the terminal is used for receiving initial growth conditions of the tellurium-zinc-cadmium crystal sent by the microcomputer, inputting the initial growth conditions of the tellurium-zinc-cadmium crystal into a crystal growth model for calculation, and obtaining temperature parameters required by growth of the tellurium-zinc-cadmium crystal; through simulation treatment on the initial growth conditions of the tellurium-zinc-cadmium crystals and the temperature parameters, judging whether high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameters; when judging that high-quality tellurium-zinc-cadmium crystals can grow under the temperature parameters, sending the temperature parameters to the microcomputer;
and the microcomputer is used for sending the initial growth conditions of the tellurium-zinc-cadmium crystal to the terminal, receiving the temperature parameters sent by the terminal and controlling the temperature information in the tellurium-zinc-cadmium crystal growth furnace according to the temperature parameters.
According to an embodiment of the present application, an electronic device includes: a memory; a processor; a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement a method for intelligent growth of cadmium zinc telluride crystals.
A computer-readable storage medium according to an embodiment of the present application has a computer program stored thereon; the computer program is executed by a processor to implement a method for intelligent growth of tellurium-zinc-cadmium crystals.
According to the scheme provided by the embodiment of the application, the method has the characteristics of big data analysis, crystal growth numerical simulation, intelligent growth, centralized control of a crystal growth furnace, high precision, high stable temperature control and the like, and the purpose of growing high-quality tellurium-zinc-cadmium crystals with low cost is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of an intelligent growth method of tellurium-zinc-cadmium crystals, which is provided by the embodiment of the application;
FIG. 2 is a schematic diagram of an intelligent growth system for tellurium-zinc-cadmium crystals, provided by an embodiment of the application;
FIG. 3 is a flow chart of a method for intelligent growth of cadmium zinc telluride crystals provided by an embodiment of the application;
FIG. 4 is a schematic diagram of an intelligent growth system for tellurium-zinc-cadmium crystals, which is provided by the embodiment of the application.
Detailed Description
The following detailed description of the preferred embodiments of the present application is provided in conjunction with the accompanying drawings, and it is to be understood that the preferred embodiments described below are merely illustrative and explanatory of the application, and are not restrictive of the application.
FIG. 1 is a flow chart of an intelligent growth method for tellurium-zinc-cadmium crystals, which is provided by the embodiment of the application, and as shown in FIG. 1, the method comprises the following steps:
step S101: the terminal receives initial growth conditions of tellurium-zinc-cadmium crystals sent by the microcomputer, and inputs the initial growth conditions of the tellurium-zinc-cadmium crystals into a crystal growth model for calculation to obtain temperature parameters required by growth of the tellurium-zinc-cadmium crystals;
step S102: the terminal judges whether high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameters by carrying out simulation treatment on the initial growth conditions of the tellurium-zinc-cadmium crystals and the temperature parameters;
step S103: when judging that high-quality tellurium-zinc-cadmium crystals can grow under the temperature parameters, the terminal sends the temperature parameters to the microcomputer, so that the microcomputer controls temperature information in the tellurium-zinc-cadmium crystal growing furnace according to the temperature parameters.
Specifically, the initial growth conditions of the tellurium-zinc-cadmium crystal comprise: zinc content in tellurium zinc cadmium, tellurium zinc cadmium polycrystal quality for growing tellurium zinc cadmium crystals, crucible type, crucible size, quartz tube size, support type and growth method; the temperature parameters include: heating program, cooling program and temperature gradient. Wherein the zinc content in the tellurium-zinc-cadmium is specifically 0-30%. The quality of tellurium-zinc-cadmium polycrystal used for growing tellurium-zinc-cadmium crystals is specifically 0-10 kg. The crucible types include quartz crucible, boron carbide crucible and graphite crucible. The crucible dimensions include crucible width, crucible length, crucible wall thickness. The quartz tube size comprises a quartz tube diameter, a quartz tube length, a quartz tube wall thickness and a shoulder angle. The support member types include silicon carbide and zirconia. The growth method comprises Bridgman method, moving heater method and vertical gradient solidification method.
Further, the terminal judges whether the high-quality tellurium-zinc-cadmium crystal can be grown under the temperature parameter by carrying out simulation treatment on the initial growth condition of the tellurium-zinc-cadmium crystal and the temperature parameter, and the terminal comprises the following steps: the terminal obtains the solid-liquid interface shape, the segregation coefficient of Zn and the temperature field distribution in the growth process of the tellurium-zinc-cadmium crystal by carrying out simulation treatment on the initial growth condition and the temperature parameter of the tellurium-zinc-cadmium crystal; when the solid-liquid interface shape in the growth process of the tellurium-zinc-cadmium crystal is a plane or a micro convex surface, the segregation coefficient of Zn is close to 1, and the temperature field distribution is proper temperature gradient, the terminal judges that the high-quality tellurium-zinc-cadmium crystal can be grown under the temperature parameters.
The embodiment of the application also comprises the following steps: when judging that high-quality tellurium-zinc-cadmium crystals cannot grow under the temperature parameters, retraining the crystal growth model by the terminal to obtain a trained crystal growth model; the terminal obtains temperature parameters required by tellurium-zinc-cadmium crystal growth by using a trained crystal growth model, and sends the temperature parameters to the microcomputer when judging that high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameters.
The embodiment of the application also comprises the following steps: and establishing a crystal growth model containing initial growth conditions, temperature parameters and characterization data of the tellurium-zinc-cadmium crystal.
The embodiment of the application also comprises the following steps: in the tellurium-zinc-cadmium crystal growth process, the microcomputer monitors the tellurium-zinc-cadmium crystal growth process in real time to obtain tellurium-zinc-cadmium crystal growth monitoring data, and periodically sends the tellurium-zinc-cadmium crystal growth monitoring data to the terminal; the terminal predicts whether high-quality tellurium-zinc-cadmium crystals can be grown under the tellurium-zinc-cadmium crystal growth monitoring data by analyzing the tellurium-zinc-cadmium crystal growth monitoring data; when it is predicted that high-quality tellurium-zinc-cadmium crystals cannot grow out under the tellurium-zinc-cadmium crystal growth monitoring data, the terminal calculates current temperature parameters required in the current tellurium-zinc-cadmium crystal growth process according to the tellurium-zinc-cadmium crystal growth monitoring data and the crystal growth model, and sends the current temperature parameters to the microcomputer.
In particular, the characterization data includes resistivity, leakage current, dislocation density, tellurium precipitate size, tellurium precipitate density, tellurium inclusion size, infrared transmittance, electron mobility-lifetime product, hole mobility-lifetime product.
FIG. 2 is a schematic diagram of an intelligent growth system for tellurium-zinc-cadmium crystals, as shown in FIG. 2, provided by the embodiment of the application, comprising: a terminal 201, configured to receive initial growth conditions of the tellurium-zinc-cadmium crystal sent by the microcomputer, and input the initial growth conditions of the tellurium-zinc-cadmium crystal into a crystal growth model for calculation, so as to obtain temperature parameters required by growth of the tellurium-zinc-cadmium crystal; through simulation treatment on the initial growth conditions of the tellurium-zinc-cadmium crystals and the temperature parameters, judging whether high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameters; when judging that high-quality tellurium-zinc-cadmium crystals can grow under the temperature parameters, sending the temperature parameters to the microcomputer; and the microcomputer 202 is used for sending the initial growth conditions of the tellurium-zinc-cadmium crystal to the terminal, receiving the temperature parameters sent by the terminal and controlling the temperature information in the tellurium-zinc-cadmium crystal growth furnace according to the temperature parameters.
An electronic device provided by an embodiment of the present application includes: a memory; a processor; a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement a method for intelligent growth of cadmium zinc telluride crystals.
A computer-readable storage medium provided by an embodiment of the present application has a computer program stored thereon; the computer program is executed by a processor to implement a method for intelligent growth of tellurium-zinc-cadmium crystals.
As shown in fig. 3, a database is established by the initial growth conditions, temperature parameters, and characterization data of a plurality of tellurium-zinc-cadmium crystals and corresponding tellurium-zinc-cadmium crystals. The terminal carries out statistical learning through a large number of tellurium-zinc-cadmium crystal initial growth conditions, temperature parameters and characterization data in a database, a model for coupling the initial growth conditions, the temperature parameters and the characterization data is generalized, the model can intelligently output proper tellurium-zinc-cadmium crystal growth temperature parameters according to the input initial growth conditions, the temperature parameters and the input initial growth conditions are simulated through crystal growth numerical simulation software built in the terminal, and therefore the solid-liquid interface shape, the segregation coefficient of Zn and the temperature field distribution in the tellurium-zinc-cadmium crystal growth process are obtained, and whether the temperature parameters are reasonable or not is judged. If the solid-liquid interface shape in the simulation result is a plane or a micro-convex surface, the segregation coefficient of Zn is close to 1, and the temperature gradient is proper, the terminal sends the temperature parameter to the microcomputer through the data exchanger; otherwise, the model will be re-deduced until the appropriate temperature parameters are obtained.
The terminal is internally provided with a Programmable Logic Controller (PLC), a LIBSVM software package and CGsim Basic simulation software.
Wherein, the temperature gradient calculation formula:
n is a unit vector of the normal direction of the isotherm,is the directional derivative of temperature along the normal direction.
The temperature gradient value, the temperature raising program and the temperature lowering program are intelligently given by the terminals according to the crystal growth conditions input by the microcomputer through the coupled model and the simulation result, and are input to the microcomputer through the data exchanger.
The terminal performs a specific statistical learning step:
and establishing a database by using initial growth conditions, temperature parameters and characterization data of the tellurium-zinc-cadmium crystal, and using a support vector machine to induce a model. The support vector machine (Support Vector Machine, SVM) is a two-class model. Given training set d= { (x) 1 ,y 1 ),(x 2 ,y 2 )...(x m ,y m ) And find a hyperplane S:the samples in the training set D are distinguished by taking the quality of the wafer as the standard, the quality of the wafer is judged according to the characterization data, if the volume resistivity rho is more than or equal to 10 10 Omega cm, leakage current less than or equal to 100nA, dislocation density less than 10 4 cm -2 Tellurium precipitation size less than 25nm and tellurium precipitation density less than 10 5 cm -3 Tellurium inclusion density is less than 1 x 10 5 cm -3 Tellurium inclusion size < 5 μm, infrared transmittance > 60%, electron mobility-lifetime product>10 -3 cm 2 V, hole mobility-lifetime product>10 -5 cm 2 And judging that the wafer has high quality, otherwise judging that the wafer has low quality, and inducing a model of coupling the initial growth condition, the temperature parameter and the characterization data to find the internal relation of the initial growth condition, the temperature parameter and the crystal quality.
Firstly, a reserving method is used for dividing a sample into two mutually exclusive two-part integrated sets, so that deviation of a model caused by data inconsistency in the process of dividing a test set and a training set is avoided, and the consistency of data distribution of the test set and the training set is ensured as much as possible in the process of dividing the test set and the training set, namely, the front and back proportions of the test set and the training set are identical.
And taking 4/5 samples in the established database as a training set and the rest as a test set. Assume that training sample set d= { (x) 1 ,y 1 ),(x 2 ,y 2 ),…,(x n ,y n ) -wherein y i E { -1, +1} represents the characterization result showing low quality crystals and high quality crystals, respectively.
The task of the SVM is to find a "maximally spaced" dividing hyperplane, and thus the SVM is expressed as:
ξ i ≥0,i＝1,2,…,m (3)
in the method, in the process of the application,is a plane normal vector, b is displacement, ζ i For relaxation variables, C is penalty factor, +.>For the x-mapped feature vector, s.t is an abbreviation for subject to, i.e., a constraint.
Model for obtaining maximum soft interval partition hyperplane for solvingFirstly, utilizing lagrange multiplier method to obtain 'dual problem' of SVM basic model, then solving +.>And b.
A lagrangian function was introduced:
wherein alpha is i ≥0，μ i And 0 is Lagrangian multiplier.
Order theRespectively pair->b, the xi obtains the partial derivative by taking the value of the partial derivative as zero:
C＝α i +μ i (7)
the dual problem is obtained:
s.t.0≤α i ≤C,i＝1,2,…,m (9)
wherein the method comprises the steps ofAs a gaussian kernel function, the expression is as follows:
sigma is a width parameter of the Gaussian kernel function, and the influence range of the Gaussian function is controlled, so that the larger the sigma is, the larger the influence range of the Gaussian function is.
Solving for alpha from formulas (8), (9), (10) and (11) i
According to formulas (11) and (12) and the solved alpha i Solving forb. Wherein->Having a unique solution, b may have multiple solutions; when b has multiple solutions, the average is solved as the only solution.
Finally, a model and a classification decision function of the maximum soft interval division hyperplane are obtained:
wherein sign (·) is a sign function.
And (3) judging the performance of the model obtained by training:
first, the accuracy
The model classifies the proportion of the correct sample number to the test lumped sample number in the test set, and the precision calculation formula is as follows:
where m is the total number of samples,to classify the correct number of samples.
Second, accuracy rate
The accuracy rate is also called "precision checking rate", which refers to the probability of actually being positive samples in all samples predicted to be positive, namely the probability that high-quality crystals can be grown according to the crystal growth temperature parameters output by the model. The formula is as follows:
where P is the precision, TP is the true number of cases, and FP is the false number of cases.
The terminal carries out simulation specific steps:
geometric modeling: creating a digital description of the geometry of the growth system, wherein the system is axisymmetrically distributed, and a two-dimensional axisymmetric calculation model is adopted.
Grid generation: grid discretization is carried out on the growth system;
model description: setting material properties of each component of the growth system and physical processes to be considered by the system;
control equation: the tellurium-zinc-cadmium crystal growth control equation has the following form:
continuity equation:
boundary conditions: given the energy and mass exchange conditions between the growth system and the environment.
In addition, the microcomputer records and uploads data monitored in real time in the growth process of the tellurium-zinc-cadmium crystal to the terminal, the terminal records and analyzes the data, and the terminal sends corresponding operation commands to the microcomputer according to analysis results. The initial growth condition, the temperature parameter and the characterization data of the tellurium-zinc-cadmium crystal which grows through the intelligent control of the terminal are recorded in a database for the terminal to learn.
In the growth process of tellurium-zinc-cadmium crystal, the temperature in the tellurium-zinc-cadmium crystal growth furnace is changed. Taking a vertical gradient method as an example, firstly, heating the raw materials to about forty ℃ above the melting point, and preserving the heat for a period of time to thoroughly melt the raw materials. And then the solid-liquid interface moves from the head to the tail at a certain speed according to the preset temperature gradient and the preset cooling rate, so that the crystal growth is completed.
The data monitored in real time in the growth process of the tellurium-zinc-cadmium crystal mainly comprises the temperature of each temperature zone of the crystal growth furnace, the temperature gradient and the solid-liquid interface shape of the crystal growth. The shape of the solid-liquid interface is divided into a plane, a convex surface and a concave surface. When the heat dissipation amount of the solid-liquid interface is equal to the heat source amount, the solid-liquid interface is a plane, but the plane is difficult to maintain for a long time in the crystal growth process, and the interface shape which is kept slightly convex is more beneficial to the crystal growth; when the heat dissipation quantity of the solid-liquid interface is smaller than the heat source quantity, the solid-liquid interface is convex; when the heat dissipation amount of the solid-liquid interface is larger than the heat source amount, the solid-liquid interface is concave.
Specifically, the microcomputer uploads the detected real-time data to the terminal, the terminal inputs the detected real-time data and the initial growth conditions to simulation software, and the quality of crystals grown under the real-time data is predicted through simulation; if the high-quality crystal can be grown under the current data, the terminal does not adjust the real-time data; otherwise, the model will compare and analyze the real-time data with the data in the database, then the model will give out the temperature parameter which is considered to grow high quality crystal, and verify whether the temperature parameter is suitable through simulation, if so, the terminal sends the temperature parameter to the microcomputer, and adjusts the monitored real-time data; if not, the model will re-output the temperature parameters until they are appropriate.
As shown in fig. 4, a system for intelligent growth of tellurium-zinc-cadmium crystals comprises: the system comprises a terminal, a microcomputer, a temperature controller and a crystal growing furnace, wherein the microcomputer and the terminal as well as the temperature controller and the microcomputer are respectively used for carrying out data exchange through a data exchanger to upload monitoring information and receive operation orders, and the temperature controller and the microcomputer are mutually independent in function. The terminal is connected with the microcomputer and the temperature controller through the data exchanger respectively. Firstly, inputting initial conditions of tellurium-zinc-cadmium crystal growth into a microcomputer, wherein the initial conditions comprise zinc content in tellurium-zinc-cadmium, tellurium-zinc-cadmium polycrystal quality for growing tellurium-zinc-cadmium crystals, crucible type, crucible size, quartz tube size, support piece type and growth method. And the terminal intelligently outputs proper temperature parameters of tellurium-zinc-cadmium crystal growth according to initial growth conditions and crystal growth numerical simulation results input by n (n is more than or equal to 1) microcomputers, and respectively sends the temperature parameters to the corresponding microcomputers through a data exchanger. The microcomputer receives the temperature parameters sent by the terminal and automatically sets a heating program, a cooling program and a temperature gradient. The microcomputer respectively controls m (m is more than or equal to 1) temperature control systems according to the temperature parameters, and uploads the monitored real-time change signals of the temperature of the crystal growth furnace to the terminal, and the terminal sends corresponding operation commands to the microcomputer according to the real-time temperature signals of the crystal growth furnace uploaded by the microcomputer. If the temperature is lower than the set value, the terminal sends an operation command for continuing heating until the temperature reaches the set value, and at the moment, the terminal sends an operation command for maintaining the current temperature; if the temperature control system fails or is out of regulation, the terminal will send alarm command to the microcomputer when the temperature rises above the set value and exceeds the alarm value, and the microcomputer will send alarm immediately.
The operation instructions sent by the microcomputer receiving terminal comprise a heating program, a cooling program and a temperature gradient setting. The microcomputer can realize independent control of m (m is more than or equal to 1) temperature control systems, and each temperature control system has a PID self-adaption function. The temperature controller comprises a high-quality transformer, a heating unit and a high-precision platinum-rhodium thermocouple, wherein the high-quality transformer, the high-precision platinum-rhodium thermocouple and the heating unit form closed-loop temperature control, and high-precision and high-stability temperature control is realized.
According to the scheme provided by the embodiment of the application, the terminal intelligently outputs the temperature parameter suitable for tellurium-zinc-cadmium crystal growth according to the input crystal growth initial condition, the model of coupling the temperature parameter with the characterization data and the crystal growth numerical simulation result, thereby laying a foundation for growing high-quality tellurium-zinc-cadmium crystals; in addition, initial growth conditions, temperature parameters and characterization data of the tellurium-zinc-cadmium crystals grown through intelligent control of the terminal are recorded in a database for terminal learning, and virtuous circle is formed between the terminal and the crystal growth, so that a model induced by the terminal is more accurate and proper, and the quality of the grown tellurium-zinc-cadmium crystals is higher. In addition, the terminal intelligently outputs proper temperature parameters of tellurium-zinc-cadmium crystal growth to each microcomputer according to initial conditions of tellurium-zinc-cadmium crystal growth input by n (n is more than or equal to 1) microcomputers. The terminal monitors the crystal growth process and sends corresponding operation commands to the microcomputer in real time. The microcomputer can realize independent control of m (m is more than or equal to 1) temperature control systems, so that the crystal growth efficiency is greatly improved, centralized monitoring and management of crystal growth are realized, and the crystal growth cost is saved.
Although the present application has been described in detail hereinabove, the present application is not limited thereto and various modifications may be made by those skilled in the art in accordance with the principles of the present application. Therefore, all modifications made in accordance with the principles of the present application should be understood as falling within the scope of the present application.
Claims (7)
1. An intelligent growth method of tellurium-zinc-cadmium crystals is characterized by comprising the following steps:
after a crystal growth model containing tellurium-zinc-cadmium crystal initial growth conditions, temperature parameters and characterization data is established and stored, the terminal receives the tellurium-zinc-cadmium crystal initial growth conditions sent by a microcomputer, and inputs the tellurium-zinc-cadmium crystal initial growth conditions into the crystal growth model for calculation to obtain the temperature parameters required by tellurium-zinc-cadmium crystal growth;
the terminal judges whether high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameter by carrying out simulation treatment on the initial growth condition of the tellurium-zinc-cadmium crystals and the temperature parameter, and the terminal comprises the following steps:
the terminal obtains the solid-liquid interface shape, the segregation coefficient of Zn and the temperature field distribution in the growth process of the tellurium-zinc-cadmium crystal by carrying out simulation treatment on the initial growth condition and the temperature parameter of the tellurium-zinc-cadmium crystal, and judges that the high-quality tellurium-zinc-cadmium crystal can be grown under the temperature parameter when the solid-liquid interface shape in the growth process of the tellurium-zinc-cadmium crystal is a plane or a micro convex surface, the segregation coefficient of Zn is close to 1 and the temperature field distribution is a proper temperature gradient;
judging that high-quality tellurium-zinc-cadmium crystals can grow under the temperature parameters, and sending the temperature parameters to the microcomputer by the terminal so that the microcomputer controls temperature information in a tellurium-zinc-cadmium crystal growing furnace according to the temperature parameters;
wherein, the initial growth conditions of the tellurium-zinc-cadmium crystal comprise: zinc content in tellurium zinc cadmium, tellurium zinc cadmium polycrystal quality for growing tellurium zinc cadmium crystals, crucible type, crucible size, quartz tube size, support type and growth method; the temperature parameters include: heating program, cooling program and temperature gradient;
the terminal establishes and stores a crystal growth model containing initial growth conditions, temperature parameters and characterization data of tellurium-zinc-cadmium crystals, and the method comprises the following steps: a database is established through the initial growth conditions, temperature parameters and characterization data of a large number of tellurium-zinc-cadmium crystals and corresponding tellurium-zinc-cadmium crystals; and carrying out statistical learning through a large number of initial growth conditions, temperature parameters and characterization data of tellurium-zinc-cadmium crystals in a database, and summarizing a model of coupling the initial growth conditions, the temperature parameters and the characterization data.
2. The method as recited in claim 1, further comprising:
judging that high-quality tellurium-zinc-cadmium crystals cannot grow under the temperature parameters, and retraining the crystal growth model by the terminal to obtain a trained crystal growth model;
the terminal obtains temperature parameters required by tellurium-zinc-cadmium crystal growth by using a trained crystal growth model, and sends the temperature parameters to the microcomputer when judging that high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameters.
3. The method as recited in claim 2, further comprising:
in the tellurium-zinc-cadmium crystal growth process, the microcomputer monitors the tellurium-zinc-cadmium crystal growth process in real time to obtain tellurium-zinc-cadmium crystal growth monitoring data, and periodically sends the tellurium-zinc-cadmium crystal growth monitoring data to the terminal;
the terminal predicts whether high-quality tellurium-zinc-cadmium crystals can be grown under the tellurium-zinc-cadmium crystal growth monitoring data by analyzing the tellurium-zinc-cadmium crystal growth monitoring data, and comprises the following steps: the terminal inputs the tellurium-zinc-cadmium crystal growth monitoring data and the growth initial conditions to simulation software, and predicts the quality of the grown crystal under the tellurium-zinc-cadmium crystal growth monitoring data through simulation;
predicting that a high-quality tellurium-zinc-cadmium crystal cannot be grown under the tellurium-zinc-cadmium crystal growth monitoring data, calculating a current temperature parameter required in a current tellurium-zinc-cadmium crystal growth process by the terminal according to the tellurium-zinc-cadmium crystal growth monitoring data and the crystal growth model, and sending the current temperature parameter to the microcomputer, wherein the method comprises the following steps of: and the crystal growth model compares and analyzes the tellurium-zinc-cadmium crystal growth monitoring data with data in a database, outputs a temperature parameter which is considered to be capable of growing high-quality crystals, verifies whether the temperature parameter is proper through simulation, and if so, the terminal sends the current temperature parameter to the microcomputer.
4. The method of claim 3, wherein the characterization data comprises resistivity, leakage current, dislocation density, tellurium precipitate size, tellurium precipitate density, tellurium inclusion size, infrared transmittance, electron mobility-lifetime product, hole mobility-lifetime product.
5. An intelligent growth system for tellurium-zinc-cadmium crystals, which is characterized by comprising:
the terminal is used for establishing a database through the initial growth conditions, temperature parameters and characterization data of a large number of tellurium-zinc-cadmium crystals and corresponding tellurium-zinc-cadmium crystals; the method comprises the steps of performing statistical learning through a large number of tellurium-zinc-cadmium crystal initial growth conditions, temperature parameters and characterization data in a database, establishing and storing a crystal growth model containing the tellurium-zinc-cadmium crystal initial growth conditions, the temperature parameters and the characterization data, receiving the tellurium-zinc-cadmium crystal initial growth conditions sent by a microcomputer, inputting the tellurium-zinc-cadmium crystal initial growth conditions into the crystal growth model for calculation, and obtaining temperature parameters required by tellurium-zinc-cadmium crystal growth; through simulation treatment on the initial growth conditions of the tellurium-zinc-cadmium crystals and the temperature parameters, judging whether high-quality tellurium-zinc-cadmium crystals can be grown under the temperature parameters; judging that high-quality tellurium-zinc-cadmium crystals can grow under the temperature parameters, and sending the temperature parameters to the microcomputer;
the terminal is specifically configured to obtain a solid-liquid interface shape, a segregation coefficient of Zn and a temperature field distribution in the growth process of the tellurium-zinc-cadmium crystal by performing simulation treatment on the initial growth condition and the temperature parameter of the tellurium-zinc-cadmium crystal, and determine that a high-quality tellurium-zinc-cadmium crystal can be grown under the temperature parameter when the solid-liquid interface shape in the growth process of the tellurium-zinc-cadmium crystal is a plane or a micro-convex surface, the segregation coefficient of Zn is close to 1, and the temperature field distribution is a proper temperature gradient;
the microcomputer is used for sending the initial growth conditions of the tellurium-zinc-cadmium crystal to the terminal, receiving the temperature parameters sent by the terminal and controlling the temperature information in the tellurium-zinc-cadmium crystal growth furnace according to the temperature parameters;
wherein, the initial growth conditions of the tellurium-zinc-cadmium crystal comprise: zinc content in tellurium zinc cadmium, tellurium zinc cadmium polycrystal quality for growing tellurium zinc cadmium crystals, crucible type, crucible size, quartz tube size, support type and growth method; the temperature parameters include: heating program, cooling program and temperature gradient.
6. An electronic device, comprising: a memory; a processor; a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any of claims 1-4.
7. A computer-readable storage medium, characterized in that a computer program is stored thereon; the computer program being executed by a processor to implement the method of any of claims 1-4.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6325851B1 (en) * | 1998-09-16 | 2001-12-04 | Komatsu Electronic Metals Co. | Crystal manufacturing apparatus and method |
| CN112466407A (en) * | 2020-11-25 | 2021-03-09 | 哈尔滨工业大学 | Method for obtaining large-size chalcopyrite single crystals based on CGSim software simulation |
| CN113106539A (en) * | 2020-06-05 | 2021-07-13 | 眉山博雅新材料有限公司 | Crystal preparation system |
| CN113130017A (en) * | 2020-06-05 | 2021-07-16 | 北京世纪金光半导体有限公司 | Crystal growth analysis method and system based on artificial intelligence |
| CN114411236A (en) * | 2022-02-10 | 2022-04-29 | 北京青禾晶元半导体科技有限责任公司 | Growth method of simulated crystal, growth method of crystal and device |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6325851B1 (en) * | 1998-09-16 | 2001-12-04 | Komatsu Electronic Metals Co. | Crystal manufacturing apparatus and method |
| CN113106539A (en) * | 2020-06-05 | 2021-07-13 | 眉山博雅新材料有限公司 | Crystal preparation system |
| CN113130017A (en) * | 2020-06-05 | 2021-07-16 | 北京世纪金光半导体有限公司 | Crystal growth analysis method and system based on artificial intelligence |
| CN112466407A (en) * | 2020-11-25 | 2021-03-09 | 哈尔滨工业大学 | Method for obtaining large-size chalcopyrite single crystals based on CGSim software simulation |
| CN114411236A (en) * | 2022-02-10 | 2022-04-29 | 北京青禾晶元半导体科技有限责任公司 | Growth method of simulated crystal, growth method of crystal and device |
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