Miniature flexible surface acoustic wave sensor with adjustable bandwidth and design and preparation method thereof

Abstract

The invention discloses a frequency-adjustable bandwidth micro flexible surface acoustic wave sensor and a design and preparation method thereof. Structural design parameters and a structural design range are selected according to structural characteristics of an SAW sensor; structural design parameters, a substrate material and an electrode material are used as optimization objects, eigenvalue quality factors corresponding to all step length nodes of to-be-optimized parameters are obtained through finite element simulation calculation, a proper function fitting method is selected, function fitting is conducted on quality factors corresponding to the to-be-optimized parameters, and the quality factors corresponding to the to-be-optimized parameters are obtained; obtaining a fitting function of the to-be-optimized parameters; and optimizing the fitting function of the plurality of to-be-optimized parameters based on a multi-parameter particle swarm optimization algorithm to obtain optimized structural design parameters of the SAW sensor. A flexible substrate structure is prepared by adopting a high-precision electric field driven spray deposition 3D printing technology, and the whole SAW sensor structure is prepared by utilizing a high-precision photoetching technology. The problems that an existing SAW sensor is narrow in signal resonant frequency, large in size and poor in flexibility are solved, the stability of the SAW sensor in a complex environment is improved, and the measurement range of the SAW sensor in the complex environment is widened.

Description

Technical Field

[0001] This invention relates to the field of electronic devices, and in particular to a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor and its design and fabrication method, which can be applied to the monitoring of parameters such as strain and temperature in complex curved surface environments. Background Technology

[0002] Surface acoustic wave (SAW) sensors are widely used in various industrial fields for monitoring, such as temperature, strain, pressure, and gas. Conventional SAW sensors are usually manufactured based on rigid piezoelectric substrates, which have problems such as a single signal resonant frequency, large size, and poor flexibility. They are difficult to adapt to the monitoring needs of complex curved surfaces, especially in applications with limited or curved spaces such as rotating machinery and aerospace structures. Therefore, realizing the adjustable bandwidth, miniaturization, and flexibility of SAW sensors has significant engineering application value. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a tunable bandwidth miniature flexible SAW sensor and its design and fabrication method, which solves the problems of narrow signal resonant frequency, large size and poor flexibility of current SAW sensors, while improving the stability and measurement range of SAW sensors in complex environments.

[0004] The technical solution adopted in this invention is as follows:

[0005] A design method for a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor, comprising the following steps:

[0006] Step 1: Design the structure of the SAW sensor, and select the structural design parameters and range of the SAW sensor; the structure of the SAW sensor includes a piezoelectric substrate and interdigitated electrodes; the structural design parameters of the SAW sensor include, but are not limited to, electrode width, electrode length, electrode thickness, electrode gap, number of electrode pairs of positive and negative electrodes, and substrate thickness;

[0007] Step 2: Select a variety of substrate materials and a variety of electrode materials;

[0008] Step 3: Take the structural design parameters of the SAW sensor in Step 1, the substrate material and the electrode material in Step 2 as optimization objects respectively, and obtain the eigenvalue quality factors corresponding to all step size nodes of the parameters to be optimized through finite element simulation calculation. Select an appropriate function fitting method to perform function fitting on the quality factors corresponding to the parameters to be optimized, and obtain the fitting function of the parameters to be optimized.

[0009] Step 4: Optimize the fitting function of multiple parameters to be optimized based on the multi-parameter particle swarm optimization algorithm to obtain the optimized structural design parameters of the SAW sensor.

[0010] Furthermore, the interdigitated electrode is a variable width interdigitated electrode, that is, the electrode width of both the positive interdigitated electrode 2 and the negative interdigitated electrode 3 gradually decreases from the middle to both sides.

[0011] Furthermore, the substrate material is a flexible dielectric material, including but not limited to aluminum nitride and polyacrylonitrile;

[0012] Furthermore, the electrodes are made of flexible metal materials, including but not limited to aluminum, gold, platinum, and copper.

[0013] Furthermore, weights are introduced during the fitting process in step 3. When the main optimization objective is the overall performance of the sensor, the weight coefficient for controlling the interdigital logarithm is 1.5, the weight coefficient for controlling the electrode thickness is 1.3, the weight coefficient for controlling the electrode length is 1.2, and the weights for related parameters such as the control electrode gap are 1.

[0014] Furthermore, the particle swarm optimization process in step 4 is as follows:

[0015] S4.1: For the structural parameters that need to be optimized, create a multivariate fitting adaptive function with the goal of maximizing the quality factor of the sensor;

[0016] S4.2: Define the structural parameters to be optimized as particles, determine the spatial dimension d for optimization, set the initial population size N, and the position x of each particle type. i and speed v i Each particle represents a potential design scheme, its position is represented by a combination of parameters, and its velocity is represented by the step size of parameter updates; the initial position and velocity settings are selected according to the design requirements of the SAW sensor, so that the particle swarm can explore the entire parameter design space.

[0017] S4.3: Initialize the fitness of each particle and record the best position in the particle's history. and the group's global optimal position ;

[0018] S4.4: Check the termination condition. If the condition is met, the optimization process ends; otherwise, the iterative calculation is repeated.

[0019] Furthermore, the fitness value of a particle is evaluated by calculating its quality factor Q obtained in the finite element simulation platform, and the particle's velocity and position are updated using the following formula:

[0020]

[0021]

[0022] In the formula, w is the optimized inertia weight, and c1 and c2 are the self-learning factor and the group learning factor, respectively. , They are two random numbers, ranging from [0,1].

[0023] Furthermore, the inertia weight w adopts a linear decreasing strategy to ensure that the algorithm performs a global search in the early stage and focuses on a fine local search in the later stage.

[0024] A method for fabricating a tunable bandwidth miniature flexible surface acoustic wave (SAW) sensor is provided. Based on the sensor design scheme obtained by the above-mentioned tunable bandwidth miniature flexible SAW sensor design method, a flexible substrate structure is prepared by high-precision electric field driven jet deposition 3D printing technology. On the prepared flexible substrate, the entire SAW sensor structure is prepared by high-precision photolithography etching technology.

[0025] A tunable bandwidth miniature flexible surface acoustic wave sensor is fabricated using the aforementioned method for fabricating a tunable bandwidth miniature flexible surface acoustic wave sensor.

[0026] The beneficial effects of this invention are:

[0027] (1) The present invention adopts a variable interdigitated finger electrode design. By gradually reducing the interdigitated finger width from the center to both ends, the limitation of the single resonant peak of the traditional equal-width interdigitated finger electrode is overcome, enabling the sensor to respond to input signals or environmental interference in a wider frequency range, thus broadening the working bandwidth of the SAW sensor and improving noise suppression capability and environmental adaptability.

[0028] (2) This invention proposes a surface acoustic wave (SAW) sensor design method based on finite element method (FED) calculation and eigenvalue quality factor. By introducing the eigenvalue quality factor as a quantitative evaluation index, the structural optimization parameters, electrode parameters, and substrate parameters of the SAW sensor with variable interdigitated width are obtained based on numerical simulation calculation. This method has strong practicality and wide applicability, and significantly improves the sensor's sensitivity, stability, and quality factor.

[0029] (3) This invention optimizes and improves the substrate material, and preferably uses a high-temperature resistant flexible dielectric material as the substrate. At the same time, the traditional processing method of SAW sensors is improved. By combining high-precision electric field driven jet deposition 3D printing to prepare flexible substrates and high-precision photolithography etching technology to prepare micro-nano electrode structures, the miniaturization and flexibility of SAW sensors are realized, enabling them to conform to complex curved surfaces and significantly reduce the added mass. Attached Figure Description

[0030] Figure 1This is a flowchart illustrating the design and fabrication method of a broadband miniature flexible surface acoustic wave sensor according to the present invention.

[0031] Figure 2 This is a structural diagram of the variable width interdigitated SAW sensor proposed in this invention.

[0032] Figure 3 This is a flowchart of the combined 3D printing and photolithography process proposed in this invention.

[0033] Reference numerals: 1-Positive external terminal; 2-Positive interdigitated electrode; 3-Negative interdigitated electrode; 4-Negative external terminal; 5-Reflection grid. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0035] Example 1

[0036] As attached Figure 1 As shown, the design method of a frequency-tunable bandwidth miniature flexible surface acoustic wave sensor proposed in this invention includes the following steps:

[0037] Step 1: Design the structure of the SAW sensor, as shown in the attached diagram. Figure 2 As shown, the SAW sensor structure mainly includes a piezoelectric substrate and interdigitated electrodes deposited on its surface in an alternating pattern. The interdigitated electrodes include a positive interdigitated electrode 2 and a negative interdigitated electrode 3. The positive interdigitated electrode 2 includes a positive external terminal 1 and multiple parallel positive electrodes, one end of which is connected to the positive external terminal 1. The negative interdigitated electrode 3 includes a negative external terminal 4 and multiple parallel negative electrodes, one end of which is connected to the negative external terminal 4. The positive and negative interdigitated electrodes 2 and 3 are rectangular interdigitated. Both the positive and negative electrodes are rectangular.

[0038] like Figure 2 As shown, the interdigitated electrodes in this application are variable-width interdigitated electrodes, that is, the electrode widths of both the positive interdigitated electrode 2 and the negative interdigitated electrode 3 gradually decrease from the middle to both sides.

[0039] The structural design parameters of SAW sensors mainly involve:

[0040] (1) Electrode width

[0041] In this embodiment, taking the width of the center interdigitated finger of the positive interdigitated electrode 2 as a reference, the electrode width gradually decreases towards both ends. The width between two adjacent positive and negative electrodes changes as follows towards both ends:

[0042]

[0043] in, It is half the width of the interdigitated fingers at the center of the positive interdigitated electrode. Let be the width of the i-th electrode extending from the center to both ends. Considering the sensor's detection performance requirements, the width of the interdigitated positive electrode center of the SAW sensor is... The value range is set to 20μm-25μm.

[0044] (2) Electrode length H

[0045] In this embodiment, considering the actual detection performance requirements of the sensor, the electrode length H of the SAW sensor is set to a range of 4mm-6mm.

[0046] (3) Electrode thickness d

[0047] Considering the actual processing accuracy and the actual application requirements of SAW sensors, the value range of the SAW sensor electrode thickness d in this embodiment of the invention is 0.1μm-0.3μm.

[0048] (4) Electrode gap

[0049] In this embodiment, based on the sensor's detection performance requirements, the electrode gap of the SAW sensor... The value range is 8μm-12μm.

[0050] (5) The number of electrode pairs N of the positive and negative electrodes

[0051] The sensor must have at least 50 pairs of positive interdigital electrodes 2 and negative interdigital electrodes 3, and the width of the piezoelectric substrate used must be greater than the length of the interdigital electrodes.

[0052] (6) Base thickness D

[0053] To ensure that the sensor meets the requirements of being lightweight and flexible, the substrate thickness D of the SAW sensor ranges from 0.5mm to 1mm.

[0054] Step 2: The substrate material is selected from flexible dielectric materials that are resistant to high temperatures, have a high electromechanical coupling coefficient, and low propagation loss, such as aluminum nitride (AlN) and polyacrylonitrile (PAN). The electrodes are selected from flexible metallic materials that are resistant to high temperatures and have good conductivity, such as aluminum (Al), gold (Au), platinum (Pt), and copper (Cu).

[0055] In sensor design, through The return loss of a sensor is characterized by a parameter, defined as the ratio of incident power to reflected power. Mathematically, it can be expressed as a quantitative index of return loss, and its calculation formula in decibels is as follows:

[0056]

[0057] In the formula, Γ is the reflection coefficient on the transmission line. The parameter is generally taken as a negative number, when A value of 0 indicates that the entire system is in a state of total loss, in which case the sensor cannot function in practice; when... When the minimum value is reached, the sensor system suffers the lowest loss and achieves the highest efficiency. The energy concentration characteristics of the IDT can be achieved by changing the electrode width, thereby improving the performance of the designed IDT. This allows the sensor to have higher energy efficiency;

[0058] The eigenvalue quality factor is calculated based on return loss.

[0059] The quality factor Q is a parameter characterizing the energy storage efficiency of a sensor resonant system. It is defined as the ratio of stored energy to dissipated energy during the resonant period. The value of Q is related to various factors such as propagation loss, device input impedance, and acoustic-to-electrical conversion efficiency. In actual sensor testing, the quality factor Q is used to reflect the sensor's quality. The formula for calculating the quality factor Q is:

[0060]

[0061] In the formula and The frequency of the sensor at the -3dB bandwidth of the return loss indicates that the higher the quality factor value, the lower the energy loss of the system, the sharper the resonance characteristics, and the stronger the stability.

[0062] Step 3: Taking the structural design parameters of the SAW sensor from Step 1, and the substrate and electrode materials from Step 2 as optimization objects, the eigenvalue quality factors corresponding to all step-size nodes of the parameters to be optimized are obtained through finite element simulation. A suitable function fitting method is then used to fit the quality factors corresponding to the parameters to be optimized. Appropriate weights are introduced during the fitting process. When the main optimization objective is the overall performance of the sensor, the weight coefficient for controlling the interdigital logarithm is 1.5, the weight coefficient for controlling the electrode thickness is 1.3, the weight coefficient for controlling the electrode length is 1.2, and the weight for related parameters such as the electrode gap is 1.

[0063] Step 4: Optimize the fitting function of multiple parameters to be optimized using a multi-parameter particle swarm optimization algorithm to obtain the optimized parameters. The specific particle swarm optimization algorithm is as follows:

[0064] S4.1: First, determine the structural parameters that need to be optimized, and create a multivariate fitting fitness function based on these parameters. These parameters include electrode width, electrode length, electrode thickness, electrode gap, substrate thickness, and the number of positive and negative electrode pairs, etc. The optimization objective is to maximize the sensor's quality factor, which is a key indicator characterizing the stability of the resonant frequency and energy utilization.

[0065] S4.2: Set the parameter to be optimized as a particle, determine the spatial dimension d of optimization, set the initial population size N, and the position x of the particle type. i and speed v i Each particle represents a potential design scheme, its position is represented by a parameter combination, and its velocity represents the step size for parameter updates. The initial position and velocity settings are reasonably selected according to the design requirements of the SAW sensor, enabling the particle swarm to explore the entire parameter design space.

[0066] S4.3: Initialize the fitness (i.e., objective function) of each particle and record the best position in the particle's history. and the group's global optimal position The fitness value of a particle is evaluated by calculating its quality factor Q obtained in the finite element simulation platform. Then, the particle's velocity and position are updated using the following formula:

[0067]

[0068]

[0069] In the formula, w represents the optimization inertia weight, which controls the "inertia" of particles in the search space and affects the balance between global and local search. c1 and c2 are the self-learning factor and the group learning factor, respectively. , These are two random numbers, ranging from [0,1]. To prevent particles from converging to a local optimum too early, the inertia weight w can be linearly reduced, gradually decreasing from a larger value to a smaller value, thus ensuring that the algorithm performs a global search in the early stages and focuses on a fine-grained local search in the later stages.

[0070] S4.4: Check the termination condition. If the condition is met, the optimization process ends; otherwise, the iterative calculation is restarted. The termination condition is that the number of evolutionary iterations reaches the maximum number of evolutionary iterations. When the maximum number of evolutionary generations is reached, the point with the largest value in the total particle swarm is taken as the optimal solution, and the positions of each particle swarm corresponding to the optimal solution are output, which are the corresponding optimized structure parameters.

[0071] Since the position and velocity of each particle in the particle swarm optimization algorithm are constantly changing, the fitness (objective function) also changes accordingly. For similar optimization problems, there is often more than one optimal solution. Therefore, it is necessary to adjust the fitness, number of iterations, maximum number of particles, etc., and repeat the iterative calculation.

[0072] In this embodiment, modeling and simulation are performed based on the selected substrate and electrode materials. A SAW sensor with variable interdigitated width is modeled using a finite element method (FEM) platform. Four electrode materials (Al, Au, Pt, and Cu) are combined with two flexible high-temperature resistant substrates (AlN and PAN) for simulation. The effects of substrate thickness, electrode thickness, and different electrode-substrate combinations on the resonant frequency of the SAW sensor, as well as the effect of electrode thickness on the sensor's resonant frequency, are investigated. The eigenvalue quality factor corresponding to the target resonant frequency is calculated using derived values ​​from global calculations. This eigenvalue quality factor quantitatively evaluates the resonant performance of the SAW sensor at different characteristic frequencies. A higher value indicates more stable resonance and higher energy utilization at that frequency, making it a core evaluation indicator for sensor optimization design. Based on the optimization results, the substrate and electrode materials of the SAW sensor are determined.

[0073] Example 2

[0074] Using the optimized SAW sensor substrate and electrode materials obtained in Example 1, this invention also proposes a method for fabricating a tunable bandwidth micro-flexible surface acoustic wave sensor. A flexible substrate structure is fabricated using high-precision electric field-driven jet deposition 3D printing technology. The entire SAW sensor structure is then fabricated on the pre-fabricated flexible substrate using high-precision photolithography etching technology. (See attached...) Figure 3 The specific steps of the preparation method include:

[0075] Step 1: 3D printing to create a flexible substrate:

[0076] Based on the sensor structure designed by the above method and the optimized substrate and electrode materials of the SAW sensor, a flexible and high-temperature resistant substrate material is prepared by high-precision electric field driven jet deposition 3D printing. In this embodiment, AlN-based material is used as an example.

[0077] Step 2, Substrate Pretreatment and Adhesive Application:

[0078] Loose particles on the AlN substrate surface were purged with nitrogen gas. The substrate was then ultrasonically cleaned in acetone and ethanol solutions for 10 minutes each. It was rinsed with ultrapure water and dried with nitrogen gas. Positive photoresist was then uniformly spin-coated onto the AlN substrate, with appropriate spin speed and duration to achieve the desired thickness, and then dried.

[0079] Step 3, Exposure, Development and Post-Baking:

[0080] Using a prepared photomask containing a variable-width interdigitated pattern and a reflective grating pattern, the photoresist-coated substrate is subjected to ultraviolet exposure on a photolithography machine. Development is performed using the appropriate developer to remove the photoresist from the exposed areas, exposing the AlN surface. After development, the substrate is rinsed with ultrapure water, dried with nitrogen, and then baked to harden the photoresist.

[0081] Step 4, Coating and Etching:

[0082] After development, an Al film is deposited on the surface of the substrate using magnetron sputtering technology, with the thickness controlled at an optimized value. The aluminum film areas not protected by photoresist are then removed by wet etching.

[0083] Step 5, Remove adhesive and clean:

[0084] Remove any remaining photoresist and its covering aluminum film using a stripping solution, thoroughly clean the substrate, and dry it with nitrogen. Inspect the quality of the interdigitated electrode pattern under a microscope to ensure there are no short circuits or open circuits, the lines are clear, and the interdigitated width meets the design specifications.

[0085] Step 6, Reflector grating processing and encapsulation:

[0086] The connectivity of the reflective grating region is checked. Finally, a flexible polymer protective layer is coated on the chip surface for encapsulation, protecting the electrode structure and enhancing environmental adaptability. The wafer is then diced into individual miniature flexible SAW sensor chips using laser cutting or precision dicing.

[0087] Example 3

[0088] Based on the sensor structure designed in Example 1 and the optimized substrate and electrode materials of the SAW sensor, a tunable bandwidth miniature flexible surface acoustic wave sensor was fabricated using the preparation method of Example 2.

[0089] In summary, this invention presents a design and fabrication method for a tunable bandwidth micro-flexible surface acoustic wave (SAW) sensor, which can be used to design and fabricate flexible SAW sensors with tunable bandwidth. Addressing the issue of traditional SAW sensors having a single frequency, this invention proposes a variable-width interdigitated structure to achieve a tunable bandwidth design for the SAW sensor. Furthermore, addressing the drawbacks of traditional SAW sensors, such as large size and poor flexibility, this invention proposes an optimization method for the flexible substrate and electrodes based on numerical modeling and simulation calculations, and a fabrication method combining high-precision electric field-driven jet deposition 3D printing technology with photolithography, thus realizing the design and fabrication of a tunable bandwidth micro-flexible SAW sensor.

[0090] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A design method for a frequency-tunable bandwidth miniature flexible surface acoustic wave sensor, characterized in that, The steps are as follows: Step 1: Design the structure of the SAW sensor, and select the structural design parameters and range of the SAW sensor; the structure of the SAW sensor includes a piezoelectric substrate and interdigitated electrodes; the structural design parameters of the SAW sensor include, but are not limited to, electrode width, electrode length, electrode thickness, electrode gap, number of electrode pairs of positive and negative electrodes, and substrate thickness; Step 2: Select a variety of substrate materials and a variety of electrode materials; Step 3: Take the structural design parameters of the SAW sensor in Step 1, the substrate material and the electrode material in Step 2 as optimization objects respectively, and obtain the eigenvalue quality factors corresponding to all step size nodes of the parameters to be optimized through finite element simulation calculation. Select an appropriate function fitting method to perform function fitting on the quality factors corresponding to the parameters to be optimized, and obtain the fitting function of the parameters to be optimized. Step 4: Optimize the fitting function of multiple parameters to be optimized based on the multi-parameter particle swarm optimization algorithm to obtain the optimized structural design parameters of the SAW sensor.

2. The design method of a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor according to claim 1, characterized in that, The interdigitated electrodes are variable-width interdigitated electrodes, meaning that the electrode widths of both the positive interdigitated electrode 2 and the negative interdigitated electrode 3 gradually decrease from the middle to both sides.

3. The design method of a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor according to claim 1, characterized in that, The substrate material is a flexible dielectric material, including but not limited to aluminum nitride and polyacrylonitrile.

4. The design method of a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor according to claim 1, characterized in that, The electrodes are made of flexible metal materials, including but not limited to aluminum, gold, platinum and copper.

5. The design method of a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor according to claim 1, characterized in that, In the fitting process of step 3, weights are introduced. When the main optimization objective is the overall performance of the sensor, the weight coefficient of the control interdigital logarithm is 1.5, the weight coefficient of the control electrode thickness is 1.3, the weight coefficient of the control electrode length is 1.2, and the weight of related parameters such as the control electrode gap is 1.

6. The design method of a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor according to claim 1, characterized in that, The particle swarm optimization process in step 4 is as follows: S4.1: For the structural parameters that need to be optimized, create a multivariate fitting adaptive function with the goal of maximizing the quality factor of the sensor; S4.2: Define the structural parameters to be optimized as particles, determine the spatial dimension d for optimization, set the initial population size N, and the position x of the particle species. i and velocity v i Each particle represents a potential design scheme, its position is represented by a combination of parameters, and its velocity is represented by the step size of parameter updates; the initial position and velocity settings are selected according to the design requirements of the SAW sensor, so that the particle swarm can explore the entire parameter design space. S4.3: Initialize the fitness of each particle and record the best position in the particle's history. and the group's global optimal position ; S4.4: Check the termination condition. If the condition is met, the optimization process ends; otherwise, the iterative calculation is repeated.

7. The design method of a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor according to claim 6, characterized in that, The fitness value of a particle is evaluated by calculating its quality factor Q obtained in the finite element simulation platform, and the particle's velocity and position are updated using the following formula: ； ； In the formula, w is the optimized inertia weight, and c1 and c2 are the self-learning factor and the group learning factor, respectively. , It consists of two random numbers, ranging from [0,1].

8. The design method of a frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor according to claim 7, characterized in that, The inertia weight w adopts a linear decreasing strategy to ensure that the algorithm performs a global search in the early stage and focuses on a fine local search in the later stage.

9. A method for fabricating a tunable bandwidth miniature flexible surface acoustic wave sensor, characterized in that, Based on the design method of a tunable bandwidth micro flexible surface acoustic wave sensor as described in claim 1, a sensor design scheme is obtained by using high-precision electric field driven jet deposition 3D printing technology to prepare a flexible substrate structure. On the prepared flexible substrate, the entire SAW sensor structure is prepared by high-precision photolithography etching technology.

10. A frequency-adjustable bandwidth miniature flexible surface acoustic wave sensor, characterized in that, This was prepared based on the method for preparing a tunable bandwidth micro flexible surface acoustic wave sensor as described in claim 9.

Images