An artificial intelligence-based phased array antenna layout design method, device and medium

Abstract

The application discloses a phased array antenna layout design method and device based on artificial intelligence and a medium, relates to the technical field of electronics, and comprises the following steps: S1, inputting layout data in a local model library into an artificial intelligence model and training; S2, recognizing a component specification book to complete packaging drawing; S3, completing layout distribution according to input data in combination with PCB thermal distribution and signal integrity specifications; S4, processing radio frequency wiring and array shielding holes, and then automatically wiring analog signals, digital signals and power signals; S5, completing triple review of DRC rules, radio frequency parameter verification and process parameter adaptation; and S6, outputting light drawing files and process requirement files required by PCB processing, and outputting files and process requirement files required by PCBA manufacturing and production. The application greatly improves the design efficiency and accuracy of the phased array antenna, reduces the layout design cycle, reduces the layout design error rate, reduces the waste of hardware cost, and improves the update iteration speed of the phased array antenna.

Description

Technical Field

[0001] This invention relates to the field of electronic technology, specifically to a method, apparatus, and medium for designing phased array antenna layouts based on artificial intelligence. Background Technology

[0002] With the widespread use of phased array antennas in wireless communication, the integration requirements for phased array antennas are becoming increasingly stringent. Not only does the integration of the chip need to be improved, but the integration of the phased array antenna printed circuit board (PCB) also needs to be increased, leading to a continuous increase in the difficulty of PCB layout design. Phased array antenna layout design is primarily completed by layout engineers, and only one layout design can be performed at a time. The higher the layout design difficulty, the longer the design cycle and the higher the error rate. These issues can seriously affect the performance indicators, production iteration cycle, and cost waste of phased array antennas. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a phased array antenna layout design method, device and medium based on artificial intelligence.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] In a first aspect, this application discloses a phased array antenna layout design method based on artificial intelligence, comprising the following steps:

[0006] S1. Input the layout data in the local model library into the artificial intelligence model and use it as the learning database of the artificial intelligence model. By training the artificial intelligence model, it can learn the layout design and strengthen its layout design ability through instructions.

[0007] S2. The trained artificial intelligence model identifies the component specifications, filters out components in the package library that have not yet been packaged, reads the corresponding specifications of the component, identifies its package information, and finally draws the schematic package and PCB package based on the design rules.

[0008] S3. Layout: The artificial intelligence model performs component layout and stack-up design based on the input data and in combination with PCB thermal distribution and signal integrity specifications.

[0009] S4. Layout routing: First, the RF traces and array shielding vias are automatically processed. Then, the analog signals, digital signals and power signals are automatically routed. During the layout routing design process, the trace paths, stack-up distribution, impedance control and via cross-layer configuration are continuously optimized and adjusted.

[0010] S5. Layout review: First, the layout is reviewed according to DRC rules, then the antenna and RF design parameters are reviewed, and finally the manufacturing feasibility is reviewed by analyzing the PCB manufacturing plant's process parameters.

[0011] S6. Manufacturing Output: Outputs the photoplotting files and process requirement files required for PCB processing, and also outputs the files and process requirement files required for PCBA manufacturing.

[0012] Based on the first aspect, step S1 specifically includes the following steps:

[0013] S11. Deploy the artificial intelligence model locally in conjunction with the actual operating environment and learn the PCB layout electrical design specifications;

[0014] S12. Import the component database into the artificial intelligence model, train the artificial intelligence model to recognize the packaging information of the component specifications, and learn the schematic packaging and PCB packaging drawing methods.

[0015] S13. Using the trained artificial intelligence model, the schematic and PCB packages in the component package library are copied and drawn. Then, the accuracy of the copied schematic and PCB packages is manually checked. If the accuracy is less than 100%, it means that the learning is insufficient, and the process returns to step S12 to relearn. If the accuracy reaches 100%, the process proceeds to the next step.

[0016] S14. Import the local PCB database into the artificial intelligence model. First, based on the schematic diagram, board manufacturing process requirements, and phased array design specifications, train the artificial intelligence model to learn PCB drawing methods. Then, use the trained artificial intelligence model to copy the PCB layout in the PCB database. Finally, manually check the accuracy of the copied PCB layout. If the accuracy is less than 100%, it means that the learning is insufficient, and the artificial intelligence model is retrained to learn the PCB drawing methods. If the accuracy reaches 100%, proceed to step S2.

[0017] Based on the first aspect, step S2 specifically includes the following steps:

[0018] S21. Input the component specification sheet into the trained artificial intelligence model. The artificial intelligence model analyzes it, searches the package library, and confirms whether the component package exists. If it exists, the artificial intelligence model outputs a prompt that the component package already exists and opens the storage location of the component package in the package library. If it does not exist, the artificial intelligence model outputs a prompt that the component is a newly introduced component.

[0019] S22. Based on the component specifications, use an artificial intelligence model to draw the corresponding schematic and PCB footprints. Perform a self-check on the drawn schematic and PCB footprints. If the self-check is incorrect, correct it. If the self-check is correct, output it. Then, manually check the correctness of the schematic and PCB footprints again. If the check is incorrect, require the artificial intelligence model to correct it again. If the check is correct, put it into the footprint library for archiving.

[0020] Based on the first aspect, step S3 specifically includes the following steps:

[0021] S31. Input the data into the artificial intelligence model. The data includes schematic diagrams, antenna models, and radio frequency circuit layout design diagrams. The artificial intelligence model analyzes the data and judges the completeness and correctness of the information in the input data.

[0022] S32. If the data is incomplete or incorrect, the output data judgment result is abnormal, and the abnormal point is given simultaneously. The abnormal point is corrected manually, and the data is re-entered. If the data is complete and correct, the artificial intelligence model calls the PCB drawing software, imports the netlist, component PCB package and board outline, grabs and places RF devices according to the chip position in the RF layout design drawing, and places digital, power chips, connectors and resistors, capacitors and inductors according to thermal distribution and signal integrity to perform PCB layout design.

[0023] S33. After completing the PCB component layout design, output the PCB layout diagram and PCB fabrication stack-up structure, and manually review them; if the layout is unreasonable, identify the layout problems and optimization design points, and require the artificial intelligence model to optimize the layout design; if the layout is reasonable, the PCB layout design task is completed.

[0024] Based on the first aspect, step S4 specifically includes the following steps:

[0025] S41. The artificial intelligence model performs routing tasks according to RF routing requirements and layer stack-up. First, it designs RF routing and shielding vias, and then manually reviews them. If the reviewed design is unqualified, the unqualified problem points are input and the abnormal points are corrected. If the reviewed design is qualified, it performs analog signal routing and via processing to complete impedance control.

[0026] S42. Design digital and power signal routing based on signal integrity to complete the PCB board routing design. Then, manually review the designed PCB board routing. If the layout is not up to standard, identify routing issues and optimization points, and require the artificial intelligence model to optimize the routing design. If the layout is up to standard, the PCB layout design task is completed.

[0027] Based on the first aspect, step S5 specifically includes:

[0028] After completing the PCB layout design task, the AI ​​model performs a self-check, including layout DRC verification, antenna and RF parameter review, and manufacturability process parameter review. If the self-check fails, a self-check failure report is generated, and the AI ​​model automatically corrects the non-conformities in the report. If the self-check passes, a self-check parameter report is generated. Finally, the self-check parameter report is reviewed manually. If the manual review fails, the non-conformities in the self-check parameter report are identified, and the AI ​​model is required to modify the design. If the manual review passes, the PCB layout review task is completed.

[0029] Based on the first aspect, step S6 specifically includes:

[0030] The AI ​​model outputs the photoplotting and processing technology specifications required for PCB layout fabrication, as well as the documents and process specifications required for PCBA assembly. Simultaneously, the AI ​​model performs a self-check. If the self-check fails, it outputs a self-check failure report and automatically re-outputs the documents. If the self-check passes, it outputs a self-check parameter report for manual review. If the manual review fails, it identifies the non-conformities in the documents and requests the AI ​​to re-output them. If the manual review passes, the PCB manufacturing document output task is complete.

[0031] Secondly, this application discloses an artificial intelligence-based phased array antenna layout design apparatus, used in the aforementioned artificial intelligence-based phased array antenna layout design method, comprising:

[0032] The artificial intelligence model learning module is used to train artificial intelligence models using local model library data, and enable them to learn and improve their layout design.

[0033] The package drawing module is used to identify unpackaged components in the datasheet, extract package information, and generate schematic packages and PCB packages.

[0034] The layout module is used to complete the component layout and stack-up design based on the input data and in combination with PCB thermal distribution and signal integrity specifications.

[0035] The layout and routing module is used to complete the layout and routing design.

[0036] The layout review module is used to complete the triple review of DRC rules, RF parameter verification, and process parameter adaptation.

[0037] The manufacturing output module is used to generate photoplot files and documents required for PCBA production, and simultaneously outputs process requirement documents.

[0038] Thirdly, this application discloses a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method described above.

[0039] The beneficial effects of this invention are:

[0040] 1) This application relies on artificial intelligence for layout design, which can significantly improve the efficiency and accuracy of phased array antenna design, reduce the layout design cycle, reduce the error rate of layout design, reduce hardware cost waste, and help phased arrays to be updated and iterated quickly. Attached Figure Description

[0041] Figure 1 This is a schematic diagram illustrating the steps of a phased array antenna layout design method based on artificial intelligence according to an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram illustrating the learning process of an artificial intelligence model according to an embodiment of the present invention;

[0043] Figure 3 A flowchart illustrating the encapsulation of an artificial intelligence model according to an embodiment of the present invention;

[0044] Figure 4 This is a flowchart illustrating the layout of an artificial intelligence model according to an embodiment of the present invention.

[0045] Figure 5 This is a schematic diagram of the layout routing of an artificial intelligence model according to an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of the process for reviewing the layout of an artificial intelligence model according to an embodiment of the present invention.

[0047] Figure 7 This is a schematic diagram illustrating the process of manufacturing and outputting an artificial intelligence model layout according to an embodiment of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] This application discloses a phased array antenna layout design method, apparatus, and medium based on artificial intelligence. Relying on artificial intelligence for layout design can significantly improve the efficiency and accuracy of phased array antenna design, shorten the layout design cycle, reduce the error rate, minimize hardware cost waste, and facilitate rapid updates and iterations of phased arrays. A schematic diagram of the steps of the method is shown below. Figure 1 As shown, the process mainly includes six stages: AI learning, packaging and drawing, layout, routing, layout review, and manufacturing output. These six stages enable AI to design phased array antenna layouts. The AI ​​can perform multiple layout designs simultaneously and work continuously 24 hours a day. Layout engineers only need to check the design layouts and provide design corrections to the AI. Multiple layout designs can be checked simultaneously. The method specifically includes the following steps:

[0050] S1. Input a large amount of layout data from the local model library into the artificial intelligence model and use it as a learning resource library for the artificial intelligence model. By training the artificial intelligence model, it can learn the layout design and strengthen its layout design capabilities through instructions, thereby improving design details and process parameters, etc.

[0051] S2. The trained artificial intelligence model identifies the component specifications, filters out components in the package library that have not yet been packaged, reads the corresponding specifications of the component, identifies its package information, and finally draws the schematic package and PCB package based on the design rules.

[0052] S3. Layout: The artificial intelligence model performs component layout and stack-up design based on the input data and in combination with PCB thermal distribution and signal integrity specifications.

[0053] S4. Layout routing: First, the RF traces and array shielding vias are automatically processed. Then, the analog signals, digital signals and power signals are automatically routed. During the layout routing design process, the trace paths, stack-up distribution, impedance control and via cross-layer configuration are continuously optimized and adjusted.

[0054] S5. Layout review: First, the layout is reviewed according to DRC rules, then the antenna and RF design parameters are reviewed, and finally the manufacturing feasibility is reviewed by analyzing the PCB manufacturing plant's process parameters.

[0055] S6. Manufacturing Output: Outputs the photoplotting files and process requirement files required for PCB processing, and also outputs the files and process requirement files required for PCBA manufacturing.

[0056] Specifically, the flowchart of artificial intelligence model learning is as follows: Figure 2 As shown, step S1 includes the following steps:

[0057] S11. Deploy the artificial intelligence model locally in conjunction with the actual operating environment and learn the PCB layout electrical design specifications;

[0058] S12. Import the component database into the artificial intelligence model, train the artificial intelligence model to recognize the packaging information of the component specifications, and learn the schematic packaging and PCB packaging drawing methods.

[0059] S13. Using the trained artificial intelligence model, the schematic and PCB packages in the component package library are copied and drawn. Then, the accuracy of the copied schematic and PCB packages is manually checked. If the accuracy is less than 100%, it means that the learning is insufficient, and the process returns to step S12 to relearn. If the accuracy reaches 100%, the process proceeds to the next step.

[0060] S14. Import the local PCB database into the artificial intelligence model. First, based on the schematic diagram, board manufacturing process requirements, and phased array design specifications, train the artificial intelligence model to learn PCB drawing methods. Then, use the trained artificial intelligence model to copy the PCB layout in the PCB database. Finally, manually check the accuracy of the copied PCB layout. If the accuracy is less than 100%, it means that the learning is insufficient, and the artificial intelligence model is retrained to learn the PCB drawing methods. If the accuracy reaches 100%, proceed to step S2.

[0061] Specifically, the flowchart for the encapsulation and rendering of the artificial intelligence model is as follows: Figure 3 As shown, step S2 includes the following steps:

[0062] S21. Input the component specification sheet into the trained artificial intelligence model. The artificial intelligence model analyzes it, searches the package library, and confirms whether the component package exists. If it exists, the artificial intelligence model outputs a prompt that the component package already exists and opens the storage location of the component package in the package library. If it does not exist, the artificial intelligence model outputs a prompt that the component is a newly introduced component.

[0063] S22. Based on the component specifications, use an artificial intelligence model to draw the corresponding schematic and PCB footprints. Perform a self-check on the drawn schematic and PCB footprints. If the self-check is incorrect, correct it. If the self-check is correct, output it. Then, manually check the correctness of the schematic and PCB footprints again. If the check is incorrect, require the artificial intelligence model to correct it again. If the check is correct, put it into the footprint library for archiving.

[0064] Specifically, the flowchart for the layout of the artificial intelligence model is as follows: Figure 4 As shown, step S3 includes the following steps:

[0065] S31. Input the data into the artificial intelligence model. The data includes schematic diagrams, antenna models, radio frequency circuit layout design diagrams, etc. The artificial intelligence model analyzes the data and judges the completeness and correctness of the information in the input data.

[0066] S32. If the data is incomplete or incorrect, the output data judgment result is abnormal, and the abnormal point is given simultaneously. The abnormal point is corrected manually, and the data is re-entered. If the data is complete and correct, the artificial intelligence model calls the PCB drawing software, imports the netlist, component PCB package and board outline, grabs and places RF devices according to the chip position in the RF layout design drawing, and places digital, power chips, connectors and resistors, capacitors and inductors according to thermal distribution and signal integrity to perform PCB layout design.

[0067] S33. After completing the PCB component layout design, output the PCB layout diagram and PCB fabrication stack-up structure, and manually review them; if the layout is unreasonable, identify the layout problems and optimization design points, and require the artificial intelligence model to optimize the layout design; if the layout is reasonable, the PCB layout design task is completed.

[0068] Specifically, the flowchart of the AI ​​model layout routing is as follows: Figure 5 As shown, step S4 includes the following steps:

[0069] S41. The artificial intelligence model performs routing tasks according to RF routing requirements and layer stack-up. First, it designs RF routing and shielding vias, and then manually reviews them. If the reviewed design is unqualified, the unqualified problem points are input and the abnormal points are corrected. If the reviewed design is qualified, it performs analog signal routing and via processing to complete impedance control.

[0070] S42. Design digital and power signal routing based on signal integrity to complete the PCB board routing design. Then, manually review the designed PCB board routing. If the layout is not up to standard, identify routing issues and optimization points, and require the artificial intelligence model to optimize the routing design. If the layout is up to standard, the PCB layout design task is completed.

[0071] Specifically, the flowchart for the review of artificial intelligence model layout is as follows: Figure 6As shown, step S5 includes: after the artificial intelligence model completes the PCB layout design task, it performs a self-check, including layout DRC inspection, antenna and RF parameter verification, and manufacturability process parameter review; if the self-check fails, a self-check failure report is output, and the artificial intelligence model automatically corrects the non-conformities in the self-check failure report; if the self-check passes, a self-check parameter report is output; finally, the self-check parameter report is manually reviewed. If the manual review fails, the non-conformities in the self-check parameter report are identified, and the artificial intelligence model is required to modify the design; if the manual review passes, the PCB layout review task is completed.

[0072] Specifically, the flowchart for the creation and output of the artificial intelligence model layout is as follows: Figure 7 As shown, step S6 includes: the artificial intelligence model outputs the photoplotting and processing technology descriptions required for PCB layout processing, and outputs the documents and process descriptions required for PCBA assembly. At the same time, the intelligent model performs self-checks. If the self-check fails, it outputs a self-check failure report and automatically re-outputs the documents. If the self-check passes, it outputs a self-check parameter report for manual re-checking. If the manual re-check fails, it identifies the non-conforming points in the documents and requests the artificial intelligence to re-output them. If the manual re-check passes, the PCB manufacturing document output task is completed.

[0073] For example, this application also discloses an artificial intelligence-based phased array antenna layout design apparatus for the aforementioned artificial intelligence-based phased array antenna layout design method, comprising:

[0074] The artificial intelligence model learning module is used to train artificial intelligence models using local model library data, and enable them to learn and improve their layout design.

[0075] The package drawing module is used to identify unpackaged components in the datasheet, extract package information, and generate schematic packages and PCB packages.

[0076] The layout module is used to complete the component layout and stack-up design based on the input data and in combination with PCB thermal distribution and signal integrity specifications.

[0077] The layout and routing module is used to complete the layout and routing design.

[0078] The layout review module is used to complete the triple review of DRC rules, RF parameter verification, and process parameter adaptation.

[0079] The manufacturing output module is used to generate photoplot files and documents required for PCBA production, and simultaneously outputs process requirement documents.

[0080] By way of example, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when it is run.

[0081] For example, the aforementioned computer-readable storage media may include, but are not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0082] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. An artificial intelligence-based phased array antenna layout design method, characterized in that, Comprise the following steps: S1, the layout data in the local model library is input into the artificial intelligence model, which is used as the learning database of the artificial intelligence model, and the artificial intelligence model is trained to learn the layout design, and the ability of the layout design is strengthened through the instruction; S2, the training of the artificial intelligence model is used to identify the component specification book, and the component in the packaging library which has not been drawn is screened and identified, and the specification book corresponding to the component is read, the packaging information is identified, and finally the schematic packaging and PCB packaging are drawn based on the design rules; S3, layout, the artificial intelligence model combines the PCB thermal distribution and signal integrity specification to design and stack the layout of the component according to the input data; S4, the layout wiring, first, the radio frequency wiring and array shielding hole processing are automatically completed, and then the automatic wiring of analog signal, digital signal and power signal is carried out. In the process of layout wiring design, the wiring path, stack distribution, impedance control and via layer configuration are continuously optimized and adjusted; S5, layout review, first, the layout is reviewed through DRC rule, then the antenna and radio frequency design parameters are reviewed, and finally the processing and manufacturing nature is reviewed by analyzing the process parameters of PCB processing factory; S6, manufacturing output, output the light drawing file and process requirement file required by PCB processing, and output the file and process requirement file required by PCBA manufacturing production.

2. The artificial intelligence-based phased array antenna layout design method of claim 1, wherein, Step S1 specifically includes the following steps: S11, combined with the actual running environment, the artificial intelligence model is applied to the local application deployment, and the PCB layout electrical design specification is learned; S12, the database of the component is imported into the artificial intelligence model, the artificial intelligence model is trained to identify the packaging information of the component specification book, and the schematic packaging and PCB packaging drawing method is learned; S13, the trained artificial intelligence model is used to copy and draw the schematic packaging and PCB packaging in the component packaging library, and then the correctness of the copied and drawn schematic packaging and PCB packaging is reviewed by artificial; if the correctness is less than 100%, it means that the learning is not enough, and the step S12 is returned to learn again; if the correctness is 100%, the next step is entered; S14, the local PCB database is imported into the artificial intelligence model, first, the artificial intelligence model is trained to learn the PCB drawing method based on the schematic, the board processing requirement and the phased array design specification; the PCB layout in the PCB database is copied and drawn by the trained artificial intelligence model, and finally the correctness of the copied and drawn PCB layout is reviewed by artificial; if the correctness is less than 100%, it means that the learning is not enough, and the artificial intelligence model is retrained to learn the PCB drawing method; if the correctness is 100%, the step S2 is entered.

3. The method of claim 2, wherein the method further comprises: Step S2 specifically includes the following steps: S21, input the component specification book into the trained artificial intelligence model, the artificial intelligence model analyzes it, searches in the packaging library, and confirms whether the component packaging exists; if it exists, the artificial intelligence model feedbacks the prompt that the component packaging already exists, and opens the storage location corresponding to the component packaging in the packaging library; if it does not exist, the artificial intelligence model feedbacks the prompt that the component is a newly introduced component; S22, based on the component specification book, the corresponding schematic diagram packaging and PCB packaging are drawn through the artificial intelligence model, and the drawn schematic diagram packaging and PCB packaging are self-checked, if the self-checking is incorrect, the self-correction is performed; if the self-checking is correct, it is output, and then the correctness of the schematic diagram packaging and PCB packaging is reviewed by artificial again, if the review is incorrect, the artificial intelligence model is required to modify again; if the review is correct, it is put into the packaging library for archiving.

4. The artificial intelligence-based phased array antenna layout design method of claim 3, wherein, Step S3 specifically includes the following steps: S31, input the data into the artificial intelligence model, the data including schematic diagram, antenna model, radio frequency circuit layout design drawing; the artificial intelligence model analyzes it, judges the information completeness and correctness of the input data; S32, if the data is not complete or correct, output the data determination result exception, synchronously give the abnormal point, modify the abnormal point by artificial, and re-input the data; if the data is complete and correct, the artificial intelligence model calls the PCB drawing software, imports the netlist, component PCB packaging and board frame, according to the chip position in the radio frequency layout design drawing, grabs and places the radio frequency devices, according to the thermal distribution and signal integrity, places the digital, power supply, connector and resistance, capacitance and inductance devices, and performs PCB layout design; S33, after completing the PCB component layout design, output the PCB layout drawing and PCB processing layer structure, and review them by artificial; if the layout is unreasonable, the layout problem point and optimization design point are proposed, and the artificial intelligence model is required to optimize the layout design; if the layout is reasonable, the PCB layout design task is completed.

5. The method of claim 4, wherein the method further comprises: Step S4 specifically includes the following steps: S41, the artificial intelligence model performs the wiring task according to the radio frequency wiring requirements and the layer, first performs the radio frequency wiring and shielding hole design, and reviews them by artificial, if the review design is unqualified, input the unqualified problem point, and modify the abnormal point; if the review design is qualified, perform analog signal wiring and via processing, and complete impedance control; S42, according to the signal integrity, perform digital and power signal wiring design, thereby completing the PCB board wiring design, and review the designed PCB board wiring by artificial; if the board is unqualified, the wiring problem point and optimization design point are proposed, the artificial intelligence model is required to optimize the wiring design, if the board is qualified, the PCB layout design task is completed.

6. The artificial intelligence-based phased array antenna layout design method of claim 5, wherein, Step S5 specifically includes: The artificial intelligence model performs self-checking after completing the PCB layout design task, including layout DRC checking, antenna and radio frequency parameter review, and manufacturability process parameter review; if the self-checking is unqualified, a self-checking unqualified report is output, and the artificial intelligence model automatically corrects the unqualified points in the self-checking unqualified report; if the self-checking is qualified, a self-checking parameter report is output; finally, the self-checking parameter report is rechecked by manual, if the manual rechecking is unqualified, the unqualified points in the self-checking parameter report are proposed, and the artificial intelligence model is required to modify the design; if the manual rechecking is qualified, the PCB layout review task is completed.

7. The artificial intelligence-based phased array antenna layout design method of claim 6, wherein, The step S6 specifically comprises: The artificial intelligence model outputs the photo drafting and processing technology required for PCB layout processing, and outputs the files and process requirements required for PCBA patching, and simultaneously performs self-checking; if the self-checking is unqualified, a self-checking unqualified report is output, and the files are automatically re-output; if the self-checking is qualified, a self-checking parameter report is output, which is used for manual rechecking; if the manual rechecking is unqualified, the unqualified points in the files are proposed, and the artificial intelligence is required to re-output; if the manual rechecking is qualified, the PCB manufacturing file output task is completed.

8. An apparatus for designing a phased array antenna layout based on artificial intelligence, used for the method for designing a phased array antenna layout based on artificial intelligence according to any one of claims 1-7, characterized in that, Comprise: An artificial intelligence model learning module for training the artificial intelligence model through local model library data, and learning and strengthening the layout design; A packaging drawing module for identifying un-packaged components in the specification book, extracting packaging information, and generating schematic packaging and PCB packaging; A layout layout module for completing component layout and layer design according to input data, combining PCB thermal distribution and signal integrity specifications; A layout wiring module for completing layout wiring design; A layout review module for completing DRC rule, radio frequency parameter verification and process parameter adaptation triple review; A manufacturing output module for generating photo drafting files and PCBA production required files, and synchronously outputting process requirement files.

9. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program, wherein the computer program is executed by the processor to implement the method of any one of claims 1-7.

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