A bipolar device substrate optimization test system and method

Abstract

The present invention relates to the technical field of semiconductor substrate optimization testing, and in particular to a bipolar device substrate optimization testing system and method, the steps of which include: obtaining substrate surface optimization information, obtaining substrate material information and epitaxial material information; calculating a substrate surface optimization index according to the substrate surface optimization information; calculating a material matching index according to the substrate material information and the epitaxial material information; determining a substrate epitaxial performance optimization index according to the substrate surface optimization index and the material matching index, and evaluating the substrate epitaxial performance optimization effect according to the epitaxial performance optimization index. The present invention quantitatively evaluates the epitaxial performance optimization effect of a substrate through an epitaxial performance optimization index, thereby improving the reliability and accuracy of a substrate epitaxial performance optimization test.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor substrate optimization testing, and in particular to a bipolar device substrate optimization testing system and method. Background Art

[0002] Bipolar junction transistor (BJT) is one of the important semiconductor devices. Bipolar devices can amplify signals and have good power control, high-speed operation and durability. They are often used to form amplifier circuits or drive speakers, motors and other equipment, and are widely used in aerospace engineering, medical equipment, robotics and other fields.

[0003] The performance of bipolar devices depends on the surface characteristics of their substrate and the matching between the substrate material and the epitaxial layer material. A high-quality substrate can provide a stable lattice structure, good electrical conductivity and efficient thermal conductivity, thereby significantly improving the electrical and thermal performance of bipolar devices.

[0004] However, during the process of epitaxial layer growth, the substrate will be affected by problems such as substrate surface roughness, lattice mismatch, and uneven thermal expansion, which directly affect the growth quality of the epitaxial layer and the final performance of the device. Therefore, the substrate needs to be optimized and tested before the epitaxial layer growth. The substrate optimization test methods in the prior art mostly focus on the detection of a single performance, resulting in the substrate optimization test being unable to comprehensively evaluate the substrate optimization effect, affecting the overall performance of the bipolar device. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present invention provides a bipolar device substrate optimization test system and method, which evaluates the epitaxial performance optimization effect of the substrate by quantifying the epitaxial performance optimization index, thereby improving the reliability and accuracy of the substrate epitaxial performance optimization test.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a bipolar device substrate optimization test method, comprising the following steps:

[0008] S1. Obtain substrate surface optimization information, substrate material information and epitaxial material information;

[0009] S2. calculating a substrate surface optimization index according to the substrate surface optimization information;

[0010] S3, calculating a material matching index according to the substrate material information and the epitaxial material information;

[0011] S4. Determine an epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, and evaluate an epitaxial performance optimization effect of the substrate according to the epitaxial performance optimization index.

[0012] As a preferred technical solution, step S2 includes the following specific steps:

[0013] S21, obtaining the substrate surface optimization information, wherein the substrate surface optimization information includes polishing liquid flow information during the substrate surface optimization process, mechanical rotation linear speed information during the substrate surface optimization process, a first surface morphology image before the substrate surface optimization, and a second surface morphology image after the substrate surface optimization;

[0014] S22, calculating the polishing liquid distribution uniformity coefficient according to the polishing liquid flow information and the mechanical rotation linear speed information;

[0015] S23, calculating a substrate surface polishing coefficient according to the first surface topography image and the second surface topography image;

[0016] S24, calculating the substrate surface optimization index according to the polishing liquid distribution uniformity coefficient and the substrate surface polishing coefficient, the calculation formula of the substrate surface optimization index is as follows:

[0017] ;

[0018] In the formula It represents the uniformity coefficient of polishing liquid distribution. represents the substrate surface polishing coefficient, represents the adjustment factor, Represents the substrate surface optimization index.

[0019] As a preferred technical solution, the calculation formula of the polishing liquid distribution uniformity coefficient in step S22 is as follows:

[0020] ;

[0021] In the formula represents the substrate radius, Indicates the radial distance from the center of the substrate The polishing liquid flow rate at Indicates the radial distance from the center of the substrate The rotational speed at It represents the average value of the ratio of polishing liquid flow rate to rotation linear speed. Indicates the polishing liquid distribution uniformity coefficient.

[0022] As a preferred technical solution, the calculation formula for the average value of the ratio of the polishing liquid flow rate to the rotation linear speed is as follows:

[0023] ;

[0024] In the formula represents the substrate radius, Indicates the radial distance from the center of the substrate The polishing liquid flow rate at Indicates the radial distance from the center of the substrate The rotational speed at It represents the average value of the ratio of polishing liquid flow rate to rotation linear speed.

[0025] As a preferred technical solution, the calculation formula of the substrate surface polishing coefficient in step S23 is as follows:

[0026] ;

[0027] In the formula Indicates the first surface topography image The substrate surface height at each sampling position, Indicates the second surface topography image The substrate surface height at each sampling position, represents the average value of the substrate surface height of all sampling positions in the first surface topography image, represents the average value of the substrate surface height of all sampling positions in the second surface topography image, represents the number of sampling locations, Represents the substrate surface polishing coefficient.

[0028] As a preferred technical solution, step S3 includes the following specific steps:

[0029] S31, obtaining the substrate material information and the epitaxial material information;

[0030] S32, calculating the material matching index according to the substrate material information and the epitaxial material information, the calculation formula of the material matching index is as follows:

[0031] ;

[0032] In the formula represents the lattice constant of the substrate material, represents the lattice constant of the epitaxial material, represents the thermal expansion coefficient of the substrate material, represents the thermal expansion coefficient of the epitaxial material, represents the thermal conductivity of the substrate material, represents the thermal conductivity of the epitaxial material, represents the lattice constant matching weight, represents the thermal expansion coefficient matching weight, represents the thermal conductivity matching weight, Indicates the material matching index.

[0033] As a preferred technical solution, step S4 includes the following specific steps:

[0034] S41, obtaining the substrate surface optimization index and the material matching index;

[0035] S42, calculating the epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, the calculation formula of the epitaxial performance optimization index is as follows:

[0036] ;

[0037] In the formula represents the substrate surface optimization index, Represents the material matching index, represents the substrate surface optimization weight, represents the epitaxial material matching weight, represents the epitaxial performance optimization index;

[0038] S43. If the epitaxial performance optimization index is greater than the preset epitaxial performance optimization threshold, the epitaxial performance optimization effect of the substrate meets the expected optimization effect; if the epitaxial performance optimization index is less than or equal to the preset epitaxial performance optimization threshold, the epitaxial performance optimization effect of the substrate does not meet the expected optimization effect.

[0039] It should be noted here that the adjustment factor, lattice constant matching weight, thermal expansion coefficient matching weight, thermal conductivity matching weight and preset epitaxial performance optimization threshold are obtained as follows: 5000 sets of substrate surface optimization information, substrate material information and epitaxial material information are collected, and whether the epitaxial performance optimization effect of the substrate meets the production requirements is distinguished, and the substrate surface optimization information, substrate material information and epitaxial material information are substituted into the epitaxial performance optimization index calculation formula for calculation, and the calculated epitaxial performance optimization index and the distinction result are simultaneously imported into the fitting software, and the optimal adjustment factor, lattice constant matching weight, thermal expansion coefficient matching weight, thermal conductivity matching weight and preset epitaxial performance optimization threshold that meet the distinction accuracy of the distinction result are output.

[0040] In a second aspect, an embodiment of the present invention provides a bipolar device substrate optimization test system, comprising:

[0041] An information acquisition module is used to acquire substrate surface optimization information, substrate material information and epitaxial material information;

[0042] A substrate surface optimization index calculation module, used to calculate a substrate surface optimization index according to the substrate surface optimization information;

[0043] A material matching index calculation module, used to calculate a material matching index according to the substrate material information and the epitaxial material information;

[0044] The epitaxial performance optimization index calculation module is used to determine the epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, and to evaluate the epitaxial performance optimization effect of the substrate according to the epitaxial performance optimization index.

[0045] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a bipolar device substrate optimization test method by calling the computer program stored in the memory.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute a bipolar device substrate optimization test method.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] The present invention first calculates a substrate surface optimization index according to substrate surface optimization information to quantify the substrate surface optimization effect, then calculates a material matching index according to substrate material information and epitaxial material information to quantify the degree of material matching between the substrate and the epitaxial layer, finally calculates an epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, and evaluates the epitaxial performance optimization effect of the substrate according to the epitaxial performance optimization index, thereby improving the reliability and accuracy of the substrate epitaxial performance optimization test. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 A schematic diagram of the overall process of a bipolar device substrate optimization test method provided by the present invention;

[0051] Figure 2 A schematic diagram of a process for calculating the substrate surface optimization index in a bipolar device substrate optimization test method provided by the present invention;

[0052] Figure 3A schematic structural diagram of a bipolar device substrate optimization test system provided by the present invention;

[0053] Figure 4 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0055] Reference Figure 1 As shown, an embodiment of the present invention provides a bipolar device substrate optimization test method, which specifically includes the following steps:

[0056] S1. Obtain substrate surface optimization information, substrate material information and epitaxial material information.

[0057] S2. calculating a substrate surface optimization index according to the substrate surface optimization information;

[0058] Substrate surface optimization is crucial in epitaxial layer growth and semiconductor device manufacturing. By optimizing the substrate surface, the growth quality of the epitaxial layer can be significantly improved, the generation of crystal defects can be reduced, and the crystal integrity and uniformity of the epitaxial layer can be improved. The optimized substrate surface helps to improve the lattice matching between the epitaxial material and the substrate, reduce interface stress and defects, thereby enhancing the electrical, optical and thermal properties of the device. At the same time, by optimizing the substrate surface, the reliability and service life of the device can be effectively improved. The substrate surface optimization index quantifies the overall state of the substrate surface after optimization by comprehensively considering the uniformity of polishing liquid distribution and the substrate surface polishing quality, thereby providing a reliable evaluation standard for subsequent epitaxial layer growth. Figure 2 As shown, calculating the substrate surface optimization index includes the following specific steps:

[0059] S21, obtaining the substrate surface optimization information, wherein the substrate surface optimization information includes the polishing liquid flow information during the substrate surface optimization process, the mechanical rotation linear speed information during the substrate surface optimization process, the first surface morphology image before the substrate surface optimization, and the second surface morphology image after the substrate surface optimization. The first surface morphology image and the second surface morphology image are obtained by atomic force microscopy (AFM). Atomic force microscopy (AFM) is the third generation of microscopes after optical microscopes and electron microscopes. It has high resolution characteristics at the atomic level. AFM works based on the attraction and repulsion between two objects. It mainly consists of three parts: a force detection part, a position detection part, and a feedback control system. The force detection part includes a tiny cantilever and a scanning probe on its top. By bringing the scanning probe close to the sample surface, atomic repulsion is generated, causing the tiny cantilever to bend slightly under force, thereby sensing the force between the sample surface and the needle tip. The position detection part contains a laser spot position detector. When the laser is irradiated at the probe of the tiny cantilever, reflection occurs. The laser reflected light will shift to a certain extent as the cantilever bends, and the offset is detected by the laser detector. The detector receives and records the information, converts it into an electrical signal and transmits it to the feedback control system; the feedback control system transmits the received electrical signal with sample information to the scanning tube made of piezoelectric ceramic tube after adjustment, drives the scanning tube to move, and finally presents the morphological characteristics of the sample surface on the display screen. Different from optical microscopes and scanning electron microscopes that can only provide two-dimensional images, AFM can fully display the true morphology of samples and provide three-dimensional surface images. In addition, AFM testing does not require any pretreatment of the samples to be tested, such as gold spraying, copper plating and other operations. These pretreatments will cause irreversible damage to the samples and the samples cannot be recovered. Scanning electron microscopes need to be in a vacuum environment to obtain surface morphology information, while AFM can be tested under normal pressure or in a liquid environment.

[0060] S22, calculating the polishing liquid distribution uniformity coefficient according to the polishing liquid flow information and the mechanical rotation linear speed information;

[0061] S23, calculating a substrate surface polishing coefficient according to the first surface topography image and the second surface topography image;

[0062] S24, calculating the substrate surface optimization index according to the polishing liquid distribution uniformity coefficient and the substrate surface polishing coefficient, the calculation formula of the substrate surface optimization index is as follows:

[0063] ;

[0064] In the formula It represents the uniformity coefficient of polishing liquid distribution. represents the substrate surface polishing coefficient, represents the adjustment factor, represents the substrate surface optimization index;

[0065] The polishing liquid distribution uniformity coefficient is calculated by analyzing the distribution of the polishing liquid at different positions on the substrate. Since the mechanical rotation linear speed is different at different positions on the substrate, the flow rate of the polishing liquid needs to be adjusted according to the rotation speed to ensure that the polishing process is evenly carried out on the entire substrate surface. If the polishing liquid is unevenly distributed, the polishing effect of each area on the substrate surface will be quite different, which will affect the growth quality of the epitaxial layer. The calculation formula for the polishing liquid distribution uniformity coefficient is as follows:

[0066] ;

[0067] In the formula represents the substrate radius, Indicates the radial distance from the center of the substrate The polishing liquid flow rate at Indicates the radial distance from the center of the substrate The rotational speed at It represents the average value of the ratio of polishing liquid flow rate to rotation linear speed. Indicates the uniformity coefficient of polishing liquid distribution;

[0068] The calculation formula for the average value of the ratio of polishing liquid flow rate to rotation linear speed is as follows:

[0069] ;

[0070] In the formula represents the substrate radius, Indicates the radial distance from the center of the substrate The polishing liquid flow rate at Indicates the radial distance from the center of the substrate The rotational speed at It represents the average value of the ratio of polishing liquid flow rate to rotation linear speed;

[0071] The substrate surface polishing coefficient is a direct quantification of the substrate surface morphology before and after polishing. By analyzing the surface morphology images of the substrate before and after polishing, the flatness and smoothness of the substrate surface can be evaluated. The flatness and smoothness of the substrate surface are crucial for the uniform growth of the epitaxial layer. If the substrate surface is not sufficiently polished, surface stress concentration, dislocation, lattice dislocation and other problems will occur during the epitaxial growth process, resulting in a decrease in the performance of the epitaxial layer. The calculation formula of the substrate surface polishing coefficient is as follows:

[0072] ;

[0073] In the formula Indicates the first surface topography image The substrate surface height at each sampling position, Indicates the second surface topography image The substrate surface height at each sampling position, represents the average value of the substrate surface height of all sampling positions in the first surface topography image, represents the average value of the substrate surface height of all sampling positions in the second surface topography image, represents the number of sampling locations, Represents the substrate surface polishing coefficient.

[0074] S3, calculating a material matching index according to the substrate material information and the epitaxial material information;

[0075] Lattice constant matching refers to the similarity of the lattice structure of the substrate material and the epitaxial material. Good lattice matching can avoid stress concentration caused by lattice asymmetry, reduce crystal defects such as dislocations and dislocations, improve crystal integrity and reduce scattering in electron migration. During the growth of the epitaxial layer, due to temperature changes, the substrate and the epitaxial material will undergo different degrees of thermal expansion or contraction. The difference in thermal expansion coefficients will cause stress accumulation at the interface, causing cracks or deformation of the material, and affecting the structural stability of the epitaxial layer. Good thermal expansion coefficient matching can ensure that the material maintains relatively consistent deformation during heat treatment or cooling, avoiding damage caused by excessive stress. At the same time, in high-power and high-frequency devices, heat dissipation performance is crucial. Therefore, the matching of the thermal conductivity of the epitaxial material and the thermal conductivity of the substrate is particularly important. If the thermal conductivity of the two is very different, the heat flux density at the interface will produce uneven heat distribution, resulting in local overheating of the device. Good matching of thermal conductivity can make the heat evenly conducted, avoid local temperature rise, thereby enhancing the heat dissipation performance of the device and extending its service life. The calculation of the material matching index includes the following specific steps:

[0076] S31, obtaining the substrate material information and the epitaxial material information;

[0077] S32, calculating the material matching index according to the substrate material information and the epitaxial material information, the calculation formula of the material matching index is as follows:

[0078] ;

[0079] In the formula represents the lattice constant of the substrate material, represents the lattice constant of the epitaxial material, represents the thermal expansion coefficient of the substrate material, represents the thermal expansion coefficient of the epitaxial material, represents the thermal conductivity of the substrate material, represents the thermal conductivity of the epitaxial material, represents the lattice constant matching weight, represents the thermal expansion coefficient matching weight, represents the thermal conductivity matching weight, It represents the material matching index, where the lattice constant, thermal expansion coefficient and thermal conductivity of the substrate material and the epitaxial material are obtained by referring to the material technical data sheet provided by the material supplier.

[0080] S4, determining an epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, and evaluating an epitaxial performance optimization effect of the substrate according to the epitaxial performance optimization index;

[0081] Epitaxy is a technique for growing new crystal layers on a substrate, which is widely used in the semiconductor industry. The epitaxy performance of a substrate refers to the influence of the substrate material on the quality, structure, and electrical properties of the epitaxial layer during the growth of the epitaxial layer. Determining the epitaxy performance optimization index of a substrate includes the following specific steps:

[0082] S41, obtaining the substrate surface optimization index and the material matching index;

[0083] S42, calculating the epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, the calculation formula of the epitaxial performance optimization index is as follows:

[0084] ;

[0085] In the formula represents the substrate surface optimization index, Represents the material matching index, represents the substrate surface optimization weight, represents the epitaxial material matching weight, represents the epitaxial performance optimization index;

[0086] S43. If the epitaxial performance optimization index is greater than the preset epitaxial performance optimization threshold, the epitaxial performance optimization effect of the substrate meets the expected optimization effect; if the epitaxial performance optimization index is less than or equal to the preset epitaxial performance optimization threshold, the epitaxial performance optimization effect of the substrate does not meet the expected optimization effect.

[0087] Reference Figure 3 As shown, an embodiment of the present invention provides a bipolar device substrate optimization test system, comprising:

[0088] An information acquisition module 210 is used to acquire substrate surface optimization information, substrate material information and epitaxial material information;

[0089] A substrate surface optimization index calculation module 220, configured to calculate a substrate surface optimization index according to the substrate surface optimization information;

[0090] A material matching index calculation module 230, configured to calculate a material matching index according to the substrate material information and the epitaxial material information;

[0091] The epitaxial performance optimization index calculation module 240 is used to determine the epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, and to evaluate the epitaxial performance optimization effect of the substrate according to the epitaxial performance optimization index.

[0092] In an embodiment of the present invention, the substrate surface optimization index calculation module 220 is used to calculate the substrate surface optimization index according to the substrate surface optimization information. The calculation of the substrate surface optimization index includes the following specific steps:

[0093] Acquiring the substrate surface optimization information, wherein the substrate surface optimization information includes polishing liquid flow information during the substrate surface optimization process, mechanical rotation linear speed information during the substrate surface optimization process, a first surface morphology image before the substrate surface optimization, and a second surface morphology image after the substrate surface optimization;

[0094] Calculating a polishing liquid distribution uniformity coefficient according to the polishing liquid flow information and the mechanical rotation linear speed information;

[0095] Calculating a substrate surface polishing coefficient according to the first surface topography image and the second surface topography image;

[0096] The substrate surface optimization index is calculated according to the polishing liquid distribution uniformity coefficient and the substrate surface polishing coefficient. The calculation formula of the substrate surface optimization index is as follows:

[0097] ;

[0098] In the formula It represents the uniformity coefficient of polishing liquid distribution. represents the substrate surface polishing coefficient, represents the adjustment factor, represents the substrate surface optimization index;

[0099] The calculation formula of the polishing liquid distribution uniformity coefficient is as follows:

[0100] ;

[0101] In the formula represents the substrate radius, Indicates the radial distance from the center of the substrate The polishing liquid flow rate at Indicates the radial distance from the center of the substrate The rotational speed at It represents the average value of the ratio of polishing liquid flow rate to rotation linear speed. Indicates the uniformity coefficient of polishing liquid distribution;

[0102] The calculation formula for the average value of the ratio of polishing liquid flow rate to rotation linear speed is as follows:

[0103] ;

[0104] In the formula represents the substrate radius, Indicates the radial distance from the center of the substrate The polishing liquid flow rate at Indicates the radial distance from the center of the substrate The rotational speed at It represents the average value of the ratio of polishing liquid flow rate to rotation linear speed;

[0105] The calculation formula of substrate surface polishing coefficient is as follows:

[0106] ;

[0107] In the formula Indicates the first surface topography image The substrate surface height at each sampling position, Indicates the second surface topography image The substrate surface height at each sampling position, represents the average value of the substrate surface height of all sampling positions in the first surface topography image, represents the average value of the substrate surface height of all sampling positions in the second surface topography image, represents the number of sampling locations, Represents the substrate surface polishing coefficient.

[0108] In an embodiment of the present invention, the material matching index calculation module 230 is used to calculate the material matching index according to the substrate material information and the epitaxial material information. The calculation of the material matching index includes the following specific steps:

[0109] Acquiring the substrate material information and the epitaxial material information;

[0110] The material matching index is calculated according to the substrate material information and the epitaxial material information. The calculation formula of the material matching index is as follows:

[0111] ;

[0112] In the formula represents the lattice constant of the substrate material, represents the lattice constant of the epitaxial material, represents the thermal expansion coefficient of the substrate material, represents the thermal expansion coefficient of the epitaxial material, represents the thermal conductivity of the substrate material, represents the thermal conductivity of the epitaxial material, represents the lattice constant matching weight, represents the thermal expansion coefficient matching weight, represents the thermal conductivity matching weight, Indicates the material matching index.

[0113] In an embodiment of the present invention, the epitaxial performance optimization index calculation module 240 is used to determine the epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, and evaluate the epitaxial performance optimization effect of the substrate according to the epitaxial performance optimization index. Determining the epitaxial performance optimization index of the substrate includes the following specific steps:

[0114] Obtaining the substrate surface optimization index and the material matching index;

[0115] The epitaxial performance optimization index of the substrate is calculated according to the substrate surface optimization index and the material matching index. The calculation formula of the epitaxial performance optimization index is as follows:

[0116] ;

[0117] In the formula represents the substrate surface optimization index, Represents the material matching index, represents the substrate surface optimization weight, represents the epitaxial material matching weight, represents the epitaxial performance optimization index;

[0118] If the epitaxial performance optimization index is greater than the preset epitaxial performance optimization threshold, the epitaxial performance optimization effect of the substrate meets the expected optimization effect; if the epitaxial performance optimization index is less than or equal to the preset epitaxial performance optimization threshold, the epitaxial performance optimization effect of the substrate does not meet the expected optimization effect.

[0119] The above parameters and steps for each unit module to achieve corresponding functions in a bipolar device substrate optimization test system of the present invention can refer to the parameters and steps in the embodiment of a bipolar device substrate optimization test method mentioned above, and will not be repeated here.

[0120] Reference Figure 4As shown, an embodiment of the present invention further provides an electronic device 300, including a memory 320 for storing a computer program 322; and a processor 310 for executing the computer program 322 to implement a microbial functional factor evaluation method based on data analysis as in any of the above embodiments.

[0121] It should be noted that Figure 4 It is a structural diagram of an electronic device 300 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of application of the present invention.

[0122] Specifically, the electronic device 300 may include: at least one processor 310, at least one memory 320, a power supply 330, a communication interface 340, an input / output interface 350, and a communication bus 360. The memory 320 is used to store a computer program 322, which is loaded and executed by the processor 310 to implement the relevant steps in the microbial functional factor evaluation method based on data analysis disclosed in any of the above embodiments. In addition, the electronic device 300 in the embodiment of the present invention may be specifically an electronic computer.

[0123] In an embodiment of the present invention, a power supply 330 is used to provide a working voltage for each hardware device on the electronic device 300; the communication interface 340 can create a data transmission channel between the electronic device 300 and an external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present invention, and is not specifically limited here; the input and output interface 350 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0124] In addition, the memory 320, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 321, a computer program 322, etc., and the storage method can be temporary storage or permanent storage.

[0125] The operating system 321 is used to manage and control the hardware devices and computer programs on the electronic device 300, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program 322 that can be used to complete the microbial functional factor evaluation method based on data analysis performed by the electronic device 300 disclosed in any of the aforementioned embodiments, the computer program 322 can further include a computer program 322 that can be used to complete other specific tasks.

[0126] An embodiment of the present invention further provides a computer-readable storage medium for storing a computer program 322. When the computer program 322 is executed by the processor 310, the microbial functional factor evaluation method based on data analysis as described in any of the above embodiments is implemented.

[0127] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

Claims

1. A bipolar device substrate optimization test method, characterized in that: The steps include: S1. Obtain substrate surface optimization information, substrate material information and epitaxial material information; S2. calculating a substrate surface optimization index according to the substrate surface optimization information; S3, calculating a material matching index according to the substrate material information and the epitaxial material information; S4, determining an epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, and evaluating an epitaxial performance optimization effect of the substrate according to the epitaxial performance optimization index; The step S2 comprises the following specific steps: S21, obtaining the substrate surface optimization information, wherein the substrate surface optimization information includes polishing liquid flow information during the substrate surface optimization process, mechanical rotation linear speed information during the substrate surface optimization process, a first surface morphology image before the substrate surface optimization, and a second surface morphology image after the substrate surface optimization; S22, calculating the polishing liquid distribution uniformity coefficient according to the polishing liquid flow information and the mechanical rotation linear speed information; S23, calculating a substrate surface polishing coefficient according to the first surface topography image and the second surface topography image; S24, calculating the substrate surface optimization index according to the polishing liquid distribution uniformity coefficient and the substrate surface polishing coefficient, the calculation formula of the substrate surface optimization index is as follows: ； In the formula It represents the uniformity coefficient of polishing liquid distribution. represents the substrate surface polishing coefficient, represents the adjustment factor, represents the substrate surface optimization index; The calculation formula of the polishing liquid distribution uniformity coefficient in step S22 is as follows: ； In the formula represents the substrate radius, Indicates the radial distance from the center of the substrate The polishing liquid flow rate at Indicates the radial distance from the center of the substrate The rotational speed at It represents the average value of the ratio of polishing liquid flow rate to rotation linear speed. Indicates the uniformity coefficient of polishing liquid distribution; The calculation formula of the substrate surface polishing coefficient in step S23 is as follows: ； In the formula Indicates the first surface topography image The substrate surface height at each sampling position, Indicates the second surface topography image The substrate surface height at each sampling position, represents the average value of the substrate surface height of all sampling positions in the first surface topography image, represents the average value of the substrate surface height of all sampling positions in the second surface topography image, represents the number of sampling locations, Represents the substrate surface polishing coefficient; The step S3 comprises the following specific steps: S31, obtaining the substrate material information and the epitaxial material information; S32, calculating the material matching index according to the substrate material information and the epitaxial material information, the calculation formula of the material matching index is as follows: ； In the formula represents the lattice constant of the substrate material, represents the lattice constant of the epitaxial material, represents the thermal expansion coefficient of the substrate material, represents the thermal expansion coefficient of the epitaxial material, represents the thermal conductivity of the substrate material, represents the thermal conductivity of the epitaxial material, represents the lattice constant matching weight, represents the thermal expansion coefficient matching weight, represents the thermal conductivity matching weight, Indicates the material matching index; The step S4 comprises the following specific steps: S41, obtaining the substrate surface optimization index and the material matching index; S42, calculating the epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, the calculation formula of the epitaxial performance optimization index is as follows: ； In the formula represents the substrate surface optimization index, Represents the material matching index, represents the substrate surface optimization weight, represents the epitaxial material matching weight, Represents the epitaxial performance optimization index.

2. A bipolar device substrate optimization test system, which is implemented based on a bipolar device substrate optimization test method described in claim 1, characterized in that: The system comprises: An information acquisition module is used to acquire substrate surface optimization information, substrate material information and epitaxial material information; A substrate surface optimization index calculation module, used to calculate a substrate surface optimization index according to the substrate surface optimization information; A material matching index calculation module, used to calculate a material matching index according to the substrate material information and the epitaxial material information; The epitaxial performance optimization index calculation module is used to determine the epitaxial performance optimization index of the substrate according to the substrate surface optimization index and the material matching index, and to evaluate the epitaxial performance optimization effect of the substrate according to the epitaxial performance optimization index.

3. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a bipolar device substrate optimization test method as described in claim 1 by calling the computer program stored in the memory.

4. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the bipolar device substrate optimization test method as claimed in claim 1.

Images