RFID electronic tag antenna design method based on analogue simulation

Abstract

The invention discloses an RFID electronic tag antenna design method based on analogue simulation, and relates to the technical field of electronic tags, and the method comprises the design steps: S1, preliminarily determining the design parameters of an electronic tag antenna based on the use environment and scene requirements, including the shape, the working frequency, the frequency bandwidth and the polarization direction; s2, constructing an antenna simulation model based on simulation software; and S3, inputting the preliminarily determined design parameters into the antenna simulation model, and optimizing the preliminary design parameters. The invention provides a design method of a passive long-distance RFID (Radio Frequency Identification) electronic tag antenna, which comprises the following steps of: inputting antenna design parameters into simulation software HFSS and ADS, carrying out computer analogue simulation, and verifying a design result through a computer analogue simulation technology, so that problems, electromagnetic interference and impedance mismatching in design can be found in advance; and theoretical feasibility and recognition efficiency of a design result are ensured, and an electromagnetic signal receiving and feedback material is selected based on a simulation result.

Description

Technical Field

[0001] The invention relates to the technical field of electronic tags, and in particular to a method for designing an RFID electronic tag antenna based on simulation. Background Art

[0002] As a contactless automatic identification technology, RFID technology has been widely used in logistics, warehousing, retail, and transportation. Passive RFID electronic tags have become the mainstream of RFID technology applications because of their advantages of no battery power and low cost. The electronic tag antenna is the transponder antenna of the RFID electronic tag. It is a communication induction antenna. It generally forms a complete RFID electronic tag transponder with the chip. The electronic tag antenna plays an important role in the RFID system. It is responsible for transmitting radio frequency signals between the tag and the reader. These antennas can be divided into metal etching antennas, printed antennas, copper-plated antennas, and die-cut antennas according to different materials and manufacturing processes. In terms of manufacturing technology, there are mainly three methods: etching, coil winding, and printed antennas, which are suitable for RFID electronic tag products of different frequencies. Traditional design methods mainly rely on experience and trial and error, and require multiple physical sample production and testing. The cycle is long and the cost is high. There is a lack of systematic design processes and tools, and it is difficult to quickly find the optimal design solution. The influence of material properties on antenna performance is often ignored, and it is difficult to give full play to the advantages of materials. In this regard, we propose an RFID electronic tag antenna design method based on simulation. Summary of the invention

[0003] In order to solve the above technical problems, a RFID electronic tag antenna design method based on simulation is provided. This technical solution solves the above problem that it is difficult to quickly find the optimal design solution.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is: a RFID electronic tag antenna design method based on simulation, the design steps are:

[0005] S1. Based on the use environment and scenario requirements, preliminarily determine the design parameters of the electronic tag antenna, including shape, operating frequency, bandwidth and polarization direction;

[0006] S2. Construct an antenna simulation model based on simulation software;

[0007] S3, inputting the preliminarily determined design parameters into the antenna simulation model to optimize the preliminary design parameters;

[0008] S4. Based on the simulation results, select electromagnetic signal receiving and feedback materials to obtain the design results of the antenna;

[0009] S5. Based on computer simulation technology, the design results of the antenna are simulated and verified to check the feasibility and recognition efficiency of the design results;

[0010] S6. When the simulation results are feasible, antenna samples are manufactured based on the final antenna design and actual tests are carried out to verify the effect;

[0011] S7. When the simulation results are infeasible and the recognition effect is poor, re-plan the design parameters and repeat steps S3-S5 until the simulation results are feasible.

[0012] Preferably, in step S1, the use environment of the current electronic tag antenna is pre-determined, and the use environment includes application scenarios, usage occasions and performance requirements. The design parameters of the electronic tag antenna by the manufacturer in the history are obtained based on big data, and the obtained parameters are integrated to form a parameter table. Based on the parameter table, designers perform preliminary design of the current electronic tag antenna parameters to obtain preliminary design results.

[0013] Preferably, the simulation software in step S2 includes HFSS and ADS, and the construction step is to open the simulation software, establish a geometric model, use the HFSS modeling tool to set the electromagnetic parameters of the material, assign the design parameters to the corresponding geometric structure, set the starting frequency, cutoff frequency and frequency step based on the operating frequency of the antenna, set the grid division, and complete the construction of the simulation model.

[0014] Preferably, the step of optimizing the design parameters in step S3 is:

[0015] A1. Clarify the goal and determine the direction of optimizing design parameters according to design requirements;

[0016] A2. Determine the variables and select the parameters that have the greatest impact on the antenna performance as the parameters of the optimization variables. The parameters include geometric parameters, material parameters and excitation parameters.

[0017] A3. Use the gradient algorithm to iteratively update the optimization variables, search for the optimal solution in the direction of the objective function descent, set physical and performance constraints to ensure the feasibility of the results;

[0018] A4, iterative optimization, starting from the initial parameters, using the algorithm to iteratively update the parameters, input the model simulation, compare the results with the target, and determine whether convergence;

[0019] A5. Evaluate the results. After convergence, perform detailed simulation evaluation. If the results do not meet the standards, adjust the relevant settings and optimize again.

[0020] Preferably, the objective function of the antenna design in step A3 is f(x), where x=(x1, x2, ... x n), is an n-dimensional optimization variable vector, representing the design parameters of the antenna, where the objective function calculation formula is:

[0021]

[0022] where g i (x) is the i-th performance indicator function, w i is the corresponding weight coefficient;

[0023] Then calculate the gradient of the objective function relative to the optimization variable. The gradient represents the rate of change of the objective function in the direction of each optimization variable.

[0024] In step A4, the gradient algorithm is used for iterative update. In each iteration, the value of the optimization variable is updated based on the current optimization variable and the gradient of the objective function. The iterative update process is repeated until the gradient of the objective function is small enough and reaches the convergence condition. At this time, the optimization variable value obtained is the optimal solution under the constraint conditions.

[0025] Preferably, in step S4, material selection is performed by extracting performance indicators of the antenna from simulation results, analyzing the simulation results, identifying factors that limit antenna performance, including material loss and impedance mismatch, and screening out the optimal material based on performance bottlenecks. Material selection obtains a material library through big data, screens out materials that meet the target requirements in the material library, and simulates the screened materials.

[0026] Preferably, in step S5, simulation is performed based on ADS software, components are selected from the component library according to the physical structure and principle of the antenna to build an equivalent circuit, accurate parameters are set, S parameter ports are added, S parameter simulation is selected, and after setting the frequency range and step, simulation is performed to obtain the simulated transmission efficiency value, and the transmission efficiency value of the electronic tag antenna that meets the standard is obtained based on big data. This value is used as the threshold, and the transmission efficiency value obtained during the simulation is compared with the threshold. When it is greater than the threshold, it is judged that the currently designed electronic tag antenna design does not meet the requirements, otherwise it meets the requirements.

[0027] Preferably, the threshold value is acquired based on big data technology, and the transmission efficiency data of the electronic tag antennas that have been verified to be qualified are collected. The data are cleaned, screened and analyzed to remove outliers and noise data. Through statistical analysis methods, the standard qualified transmission efficiency value is determined as the threshold value. When making a comparison, a simple numerical comparison method is used to compare, so as to determine whether the design requirements are met.

[0028] Preferably, in step S6, after obtaining feasible antenna design simulation results, physical production and actual test verification are carried out, and when manufacturing antenna samples, a microstrip antenna is manufactured based on a PCB process.

[0029] Preferably, in step S6, the transmission efficiency of the manufactured electronic tag antenna is tested by using a direct measurement method based on the power measurement principle to measure the power input to the antenna and the power radiated from the antenna respectively, and the ratio of the two is calculated to obtain the transmission efficiency value to check whether the transmission efficiency value meets the requirements.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention provides a design method for a passive long-distance RFID electronic tag antenna. By inputting antenna design parameters into simulation software HFSS and ADS, computer simulation is performed, and the design result is verified by computer simulation technology, so that problems in the design, electromagnetic interference, and impedance mismatch can be discovered in advance, and the theoretical feasibility and recognition efficiency of the design result can be guaranteed. Electromagnetic signal receiving and feedback materials are selected based on the simulation results, and suitable materials can be selected in a targeted manner according to the performance requirements of the antenna, the reception sensitivity of the signal, and the feedback strength, thereby improving the overall performance of the antenna. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The figure is a flow chart of the steps of the electronic tag antenna design method of the present invention. DETAILED DESCRIPTION

[0033] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may conceive of other obvious variations.

[0034] Reference Figure 1 As shown in the figure, the RFID electronic tag antenna design method based on simulation, the design steps are:

[0035] S1. Based on the use environment and scenario requirements, preliminarily determine the design parameters of the electronic tag antenna, including shape, operating frequency, bandwidth and polarization direction;

[0036] S2. Construct an antenna simulation model based on simulation software;

[0037] S3, inputting the preliminarily determined design parameters into the antenna simulation model to optimize the preliminary design parameters;

[0038] S4. Based on the simulation results, select electromagnetic signal receiving and feedback materials to obtain the design results of the antenna;

[0039] S5. Based on computer simulation technology, the design results of the antenna are simulated and verified to check the feasibility and recognition efficiency of the design results;

[0040] S6. When the simulation results are feasible, antenna samples are manufactured based on the final antenna design and actual tests are carried out to verify the effect;

[0041] S7. When the simulation results are infeasible and the recognition effect is poor, re-plan the design parameters and repeat steps S3-S5 until the simulation results are feasible.

[0042] This application determines the design parameters based on the use environment and scenario requirements, which can make the antenna fit the actual application from the design source. In the complex industrial production workshop environment, the appropriate operating frequency and bandwidth are determined according to the equipment layout and electromagnetic interference conditions, so that the antenna can work effectively in a specific scenario, avoiding the poor performance problem caused by the disconnection between the design and actual needs; using simulation software to build models and optimize parameters, with the help of the powerful computing power of computers, multiple parameter combinations can be quickly tried. Compared with the traditional method of repeatedly making physical models to adjust parameters, this method greatly saves time and cost, and can find the optimal shape and polarization direction parameter combination in a short time, thereby improving the theoretical performance of the antenna;

[0043] S4 selects electromagnetic signal receiving and feedback materials based on simulation results. It can select materials in a targeted manner according to the performance characteristics of the antenna in the simulation process, such as the receiving ability of signals in different frequency bands and the signal feedback strength. This helps to improve the overall performance of the antenna and enable the antenna to receive and feedback signals more efficiently in actual work.

[0044] Verification through computer simulation technology can discover potential problems in the design in advance, such as electromagnetic compatibility problems and impedance mismatch, and solve these problems before entering the physical production stage, avoiding waste of resources and time delays caused by design defects. Checking the recognition efficiency can also ensure that the antenna theoretically meets the requirements of practical applications.

[0045] In step S1, the use environment of the current electronic tag antenna is determined in advance. The use environment includes application scenarios, usage occasions and performance requirements. The design parameters of the electronic tag antenna by the manufacturer in history are obtained based on big data. The obtained parameters are integrated to form a parameter table. Based on the parameter table, designers make a preliminary design of the current electronic tag antenna parameters to obtain preliminary design results.

[0046] This application predetermines the use environment of the electronic tag antenna, covering the key elements of application scenarios, usage occasions and performance requirements. This enables designers to have an in-depth understanding of the actual operating conditions of the antenna, so as to carry out design work in a targeted manner. In logistics and warehousing scenarios, the antenna needs to have the ability to identify at a long distance and quickly read multiple tags; in library management scenarios, more attention may be paid to the antenna's accurate identification of tags in different locations. After clarifying these requirements, the designed antenna can better adapt to specific scenarios and improve work efficiency.

[0047] The simulation software in step S2 includes HFSS and ADS. The construction step is to open the simulation software, establish a geometric model, use the HFSS modeling tool, set the electromagnetic parameters of the material, assign the design parameters to the corresponding geometric structure, set the starting frequency, cutoff frequency and frequency step based on the working frequency of the antenna, set the grid division, and complete the construction of the simulation model.

[0048] This application uses the modeling tools of the simulation software to establish a geometric model, which can accurately reproduce the actual shape and size characteristics of the electronic tag antenna in a virtual environment. Whether it is a complex polygonal shape or a fine microstrip structure, it can be accurately constructed through the modeling tool, providing a reliable basis for subsequent simulation analysis, so that the simulation results can more realistically reflect the actual performance of the antenna; after building the simulation model, the performance of the antenna can be comprehensively evaluated and optimized in a virtual environment without the need to frequently make physical models for testing, which greatly reduces the number of physical production times and saves material costs and production time.

[0049] The steps for optimizing the design parameters in step S3 are:

[0050] A1. Clarify the goal and determine the direction of optimizing design parameters according to design requirements;

[0051] A2. Determine the variables and select the parameters that have the greatest impact on the antenna performance as the parameters of the optimization variables. The parameters include geometric parameters, material parameters and excitation parameters.

[0052] A3. Use the gradient algorithm to iteratively update the optimization variables, search for the optimal solution in the direction of the objective function descent, set physical and performance constraints to ensure the feasibility of the results;

[0053] A4, iterative optimization, starting from the initial parameters, using the algorithm to iteratively update the parameters, input the model simulation, compare the results with the target, and determine whether convergence;

[0054] A5. Evaluate the results. After convergence, perform detailed simulation evaluation. If the results do not meet the standards, adjust the relevant settings and optimize again.

[0055] There are many benefits to optimizing the design parameters of the electronic tag antenna according to a specific process in step S3 of this application. A1 clarifies the goal, gives the optimization a clear direction, and improves the pertinence and efficiency; A2 determines the key optimization variables, grasps the core factors, and simplifies the problem; A3 uses the gradient algorithm to find the optimal solution, and combines constraints to ensure the feasibility of the result; A4 iterates the optimization and dynamically adjusts the parameters to approach the optimum and enhance the robustness; A5 evaluates the results, comprehensively checks the performance, and continuously improves if it does not meet the standards to ensure the design quality.

[0056] The objective function of antenna design in step A3 is f(x), where x = (x1, x2, ... x n ), is an n-dimensional optimization variable vector, representing the design parameters of the antenna, where the objective function calculation formula is:

[0057]

[0058] where g i (x) is the i-th performance indicator function, w i is the corresponding weight coefficient;

[0059] Then calculate the gradient of the objective function relative to the optimization variable. The gradient represents the rate of change of the objective function in the direction of each optimization variable.

[0060] In step A4, the gradient algorithm is used for iterative update. In each iteration, the value of the optimization variable is updated based on the current optimization variable and the gradient of the objective function. The iterative update process is repeated until the gradient of the objective function is small enough and reaches the convergence condition. At this time, the optimization variable value obtained is the optimal solution under the constraint conditions.

[0061] In this application, different application scenarios require that in terms of optimization parameters, the gradient can clearly define the rate of change of the objective function in the direction of each optimization variable, indicate the search direction for the optimization algorithm, and enable the algorithm to quickly approach the optimal solution along the negative direction of the gradient, thereby reducing the optimization time. In terms of design accuracy, the gradient reflects the degree of influence of each variable on the objective function, and the parameters can be accurately adjusted accordingly. At the same time, it can also determine to a certain extent whether it is close to the local optimum, adopt strategies to jump out of the local optimum trap, continue to search for the global optimal solution, and improve the overall quality of the antenna design;

[0062] The gradient algorithm updates the optimization variables based on the gradient of the objective function. The gradient represents the rate of change of the objective function in the direction of each optimization variable. This clearly indicates the search direction for the optimization process, allowing the algorithm to move in the direction where the objective function value decreases fastest, avoiding blind trials, greatly improving the optimization efficiency, and quickly locating a better parameter combination in the complex antenna design parameter space. The entire iterative process is carried out under physical and performance constraints, which ensures that the value of the optimization variable after each update is within a reasonable range. The optimal solution finally obtained can not only make the objective function reach a better value, but also meet the various requirements of antenna design in practical applications, size restrictions, and performance indicators, ensuring the feasibility and practicality of the design scheme.

[0063] In step S4, material selection extracts antenna performance indicators from simulation results, analyzes simulation results, identifies factors that limit antenna performance, including material loss and impedance mismatch, and selects the optimal material based on performance bottlenecks. Material selection obtains material library through big data, selects materials that meet target requirements from the material library, and simulates the selected materials.

[0064] This application extracts antenna performance indicators from simulation results and analyzes them, which can fully understand the performance of the antenna under the current design. By identifying the factors that limit antenna performance, material loss and impedance mismatch, designers can accurately locate the problem and clearly identify the direction that needs improvement. By obtaining a material library through big data, a rich and diverse material option can be provided. The material library contains materials with various characteristics, providing designers with a broader choice space.

[0065] In step S5, simulation is performed based on ADS software. According to the physical structure and principle of the antenna, components are selected from the component library to build an equivalent circuit, accurate parameters are set, S parameter ports are added, S parameter simulation is selected, and after setting the frequency range and step, simulation is performed to obtain the simulated transmission efficiency value. Based on big data, the transmission efficiency value of the electronic tag antenna that meets the standard is obtained. This value is used as the threshold, and the transmission efficiency value obtained during the simulation is compared with the threshold. When it is greater than the threshold, it is judged that the currently designed electronic tag antenna design does not meet the requirements, otherwise it meets the requirements.

[0066] This application uses big data to obtain standard qualified electronic tag antenna transmission efficiency values ​​as thresholds. This threshold combines a large amount of historical design and actual application data, and is widely representative and scientific. For designs that do not meet the requirements, by continuously adjusting parameters and simulating and comparing again, the antenna design can be gradually optimized and the transmission efficiency can be improved, ultimately making the design meet actual application requirements and improving the overall product quality.

[0067] The threshold acquisition is based on big data technology. The transmission efficiency data of electronic tag antennas that have been verified to be qualified are collected, the data are cleaned, screened and analyzed, outliers and noise data are removed, and the standard qualified transmission efficiency value is determined as the threshold through statistical analysis methods. When making comparisons, a simple numerical comparison method is used to determine whether the design requirements are met.

[0068] This application uses big data technology to widely collect transmission efficiency data of electronic tag antennas that have been verified to be qualified. The number of samples is huge and the sources are rich, which enables the acquired data to fully reflect the transmission efficiency characteristics of qualified antennas under different types and application scenarios, and provides a solid foundation for determining accurate thresholds. The simple numerical comparison method directly compares the transmission efficiency value obtained by simulation with the threshold. The operation process is very simple and does not require complex calculations or analysis. Designers or engineers can quickly draw judgment results, greatly improving work efficiency.

[0069] In step S6, after the antenna design simulation results are feasible, physical production and actual test verification are carried out. When making antenna samples, microstrip antennas are made based on PCB technology. In step S6, the transmission efficiency of the manufactured electronic tag antenna is tested. The direct measurement method is used based on the power measurement principle to measure the power input to the antenna and the power radiated by the antenna respectively, and the ratio of the two is calculated to obtain the transmission efficiency value to check whether the transmission efficiency value meets the requirements.

[0070] The PCB process of this application is a common and mature antenna manufacturing process. During the production process, it can be verified whether the design scheme is suitable for the process and whether the process itself can meet the design requirements. The direct measurement method is based on the power measurement principle, directly measuring the power input to the antenna and the power radiated by the antenna, and then calculating the ratio of the two to obtain the transmission efficiency value. This method has an intuitive measurement process and accurate and reliable results. It can truly reflect the energy conversion efficiency of the antenna in actual work and provide accurate data support for evaluating antenna performance.

[0071] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. The RFID electronic tag antenna design method based on simulation is characterized by: The design steps are: S1. Based on the use environment and scenario requirements, preliminarily determine the design parameters of the electronic tag antenna, including shape, operating frequency, bandwidth and polarization direction; S2. Construct an antenna simulation model based on simulation software; S3, inputting the preliminarily determined design parameters into the antenna simulation model to optimize the preliminary design parameters; S4. Based on the simulation results, select electromagnetic signal receiving and feedback materials to obtain the design results of the antenna; S5. Based on computer simulation technology, the design results of the antenna are simulated and verified to check the feasibility and recognition efficiency of the design results; S6. When the simulation results are feasible, antenna samples are manufactured based on the final antenna design and actual tests are carried out to verify the effect; S7. When the simulation results are infeasible and the recognition effect is poor, re-plan the design parameters and repeat steps S3-S5 until the simulation results are feasible.

2. The RFID electronic tag antenna design method based on simulation according to claim 1 is characterized in that: In step S1, the use environment of the current electronic tag antenna is determined in advance. The use environment includes application scenarios, usage occasions and performance requirements. The design parameters of the electronic tag antenna by the manufacturer in history are obtained based on big data. The obtained parameters are integrated to form a parameter table. Based on the parameter table, designers make a preliminary design of the current electronic tag antenna parameters to obtain preliminary design results.

3. The RFID electronic tag antenna design method based on simulation according to claim 1 is characterized in that: The simulation software in step S2 includes HFSS and ADS. The construction step is to open the simulation software, establish a geometric model, use the HFSS modeling tool, set the electromagnetic parameters of the material, assign the design parameters to the corresponding geometric structure, set the starting frequency, cutoff frequency and frequency step based on the working frequency of the antenna, set the grid division, and complete the construction of the simulation model.

4. The RFID electronic tag antenna design method based on simulation according to claim 1 is characterized in that: The steps for optimizing the design parameters in step S3 are: A1. Clarify the goal and determine the direction of optimizing design parameters according to design requirements; A2. Determine the variables and select the parameters that have the greatest impact on the antenna performance as the parameters of the optimization variables. The parameters include geometric parameters, material parameters and excitation parameters. A3. Use the gradient algorithm to iteratively update the optimization variables, search for the optimal solution in the direction of the objective function descent, set physical and performance constraints to ensure the feasibility of the results; A4, iterative optimization, starting from the initial parameters, using the algorithm to iteratively update the parameters, input the model simulation, compare the results with the target, and determine whether convergence; A5. Evaluate the results. After convergence, perform detailed simulation evaluation. If the results do not meet the standards, adjust the relevant settings and optimize again.

5. The RFID electronic tag antenna design method based on simulation according to claim 4 is characterized in that: The objective function of antenna design in step A3 is f(x), where x = (x1, x2, ... x n ), is an n-dimensional optimization variable vector, representing the design parameters of the antenna, where the objective function calculation formula is: where g i (x) is the i-th performance indicator function, w i is the corresponding weight coefficient; Then calculate the gradient of the objective function relative to the optimization variable. The gradient represents the rate of change of the objective function in the direction of each optimization variable. In step A4, the gradient algorithm is used for iterative update. In each iteration, the value of the optimization variable is updated based on the current optimization variable and the gradient of the objective function. The iterative update process is repeated until the gradient of the objective function is small enough and reaches the convergence condition. At this time, the optimization variable value obtained is the optimal solution under the constraint conditions.

6. The RFID electronic tag antenna design method based on simulation according to claim 1 is characterized in that: In step S4, material selection extracts antenna performance indicators from simulation results, analyzes simulation results, identifies factors that limit antenna performance, including material loss and impedance mismatch, and selects the optimal material based on performance bottlenecks. Material selection obtains material library through big data, selects materials that meet target requirements from the material library, and simulates the selected materials.

7. The RFID electronic tag antenna design method based on simulation according to claim 1 is characterized in that: In step S5, simulation is performed based on ADS software. According to the physical structure and principle of the antenna, components are selected from the component library to build an equivalent circuit, accurate parameters are set, S parameter ports are added, S parameter simulation is selected, and after setting the frequency range and step, simulation is performed to obtain the simulated transmission efficiency value. Based on big data, the transmission efficiency value of the electronic tag antenna that meets the standard is obtained. This value is used as the threshold, and the transmission efficiency value obtained during the simulation is compared with the threshold. When it is greater than the threshold, it is judged that the currently designed electronic tag antenna design does not meet the requirements, otherwise it meets the requirements.

8. The RFID electronic tag antenna design method based on simulation according to claim 7 is characterized in that: The threshold acquisition is based on big data technology. The transmission efficiency data of electronic tag antennas that have been verified to be qualified are collected, the data are cleaned, screened and analyzed, outliers and noise data are removed, and the standard qualified transmission efficiency value is determined as the threshold through statistical analysis methods. When making comparisons, a simple numerical comparison method is used to determine whether the design requirements are met.

9. The RFID electronic tag antenna design method based on simulation according to claim 1 is characterized in that: In step S6, after the antenna design simulation results are feasible, physical production and actual test verification are carried out. When making antenna samples, microstrip antennas are made based on PCB technology.

10. The RFID electronic tag antenna design method based on simulation according to claim 1, characterized in that: In step S6, the transmission efficiency of the manufactured electronic tag antenna is tested. A direct measurement method is used based on the power measurement principle to measure the power input to the antenna and the power radiated from the antenna. The ratio of the two is calculated to obtain the transmission efficiency value to check whether the transmission efficiency value meets the requirements.

Images