Method for manufacturing a uniform near-field surface acoustic wave microfluidic chip

Abstract

The present application discloses a method for manufacturing a surface acoustic wave microfluidic chip with uniform near-field, which belongs to the field of microfluidics. In response to the problem of uneven lateral distribution of the acoustic field amplitude excited by the straight interdigital transducer in the existing surface acoustic wave microfluidic chip, the present application proposes a method for manufacturing a surface acoustic wave microfluidic chip with uniform lateral distribution of the acoustic field. Specifically, first, a model for calculating the acoustic field distribution of the aperture-varied interdigital transducer is constructed; further, the acoustic field distribution when the transducer fingers are weighted differently is calculated; further, an optimization algorithm is applied to obtain a weighting method that makes the acoustic field distribution in the near-field area uniform; finally, a microfluidic cavity is manufactured and installed in the near-field area of ​​the interdigital transducer. The acoustic field in the working area of ​​the device has good lateral invariance and is applicable to different piezoelectric substrate materials and microfluidic cavity structures. Compared with existing devices, the surface acoustic wave microfluidic chip of the present application can effectively improve the efficiency and accuracy of particle manipulation.

Description

Technical Field

[0001] The present application relates to the field of microfluidics, and more specifically, to a method for manufacturing a uniform near-field surface acoustic wave microfluidic chip. Background Art

[0002] Surface acoustic wave microfluidic chips can flexibly manipulate and process microliter-scale liquid biological samples and reagents without relying on the resonance of the microcavity walls, and can achieve operations such as aggregation, sorting, counting, manipulation and arrangement of particles or cells in samples and reagents. Therefore, they are widely used in medical research, biological and chemical analysis and other fields.

[0003] The interdigital transducer is the core component of the surface acoustic wave chip. The surface acoustic wave traveling wave field in the traveling wave chip and the surface acoustic wave standing wave field in the standing wave chip are both excited by one or more groups of interdigital transducers. Among them, the straight interdigital transducer with simple and convenient design is the most commonly used configuration in the surface acoustic wave microfluidic chip. However, since the aperture of the straight interdigital transducer used in the chip is always finite, the diffraction effect caused by the truncation of various physical quantities at the aperture edge is difficult to avoid, and the lateral uniformity of the sound field in the near field is often missing. This will make the sound pressure distribution in the working area of ​​the chip inconsistent with the expectation, and stimulate vortex sound flow in the local area, triggering a series of effects beyond the design expectations, affecting the working performance of the chip, and some surface acoustic wave microfluidic chips may even fail as a result.

[0004] In order to reduce the impact of the diffraction effect on the lateral uniformity of the sound field, one method in the prior art for designing surface acoustic wave microfluidic chips is to increase the aperture of the IDT and make the working area as close to the IDT as possible. However, this method still cannot avoid the lateral non-uniformity of the sound field caused by the diffraction effect, and the aperture of the IDT is limited by the size of the piezoelectric substrate. Another method is to reduce the aperture of the IDT so that the working area is located in a relatively uniform far field. However, the sound field intensity in the far field is often low, and this method may result in the sound field intensity in the working area not meeting the requirements. Therefore, in order to make the actual effect of the surface acoustic wave microfluidic chip meet the expectations and improve the chip performance, a surface acoustic wave microfluidic chip design scheme that improves the sound field uniformity in the near field is needed.

[0005] In the prior art, finger weighting of an IDT is usually used to produce a broadband response, filtering behavior or coding effect to serve specific communication applications. For example, the invention “Method, device, equipment and filter for determining finger parameters of an IDT” (application number: 202310252594.4) discloses a method for determining finger parameters of an IDT for a surface acoustic wave filter. This scheme performs finger weighting on the input transducer in order to enable the output transducer to obtain the required electrical signal and achieve a filtering effect, rather than to obtain a specific sound field distribution. This is significantly different from the purpose of compensating for the influence of diffraction effects on the sound field distribution and improving the uniformity of the sound field in the present application. For another example, the invention “An envelope amplitude weighted wavelet transform processor with diffraction suppression function” (application number: 201510451820.7) proposes to use the BLIR matching correction method to perform multiple iterative corrections on the aperture and phase of the input transducer to compensate for the influence of the diffraction effect on the wavelet transform signal received by the output transducer. However, this method can only compensate for the diffraction effect on the wavelet transform signal received by the output transducer, not its effect on the acoustic field distribution. Furthermore, this method uses a parabolic model to calculate the diffraction field, which cannot accurately calculate the diffraction field distribution on piezoelectric substrates with different anisotropic properties. This makes it difficult to improve the acoustic field uniformity in the working area of ​​the surface acoustic wave microfluidic chip and enhance its performance. Summary of the Invention

[0006] 1. Technical problems to be solved

[0007] In response to the problem of uneven surface acoustic wave field distribution in the near-field area of ​​surface acoustic wave microfluidic chips in the prior art, the present application provides a method for manufacturing a surface acoustic wave microfluidic chip with a uniform near-field. The method first constructs an acoustic field prediction model of a discrete aperture apodized interdigital transducer, and then uses an optimization algorithm to search for the optimal aperture apodization scheme based on the model. By performing aperture apodization on the interdigital transducer in the chip, the uniformity of the surface acoustic wave field distribution in the chip is improved, and the acoustic field intensity is not significantly affected.

[0008] 2. Technical solution

[0009] The purpose of this application is achieved through the following technical solutions.

[0010] This specification provides a method for manufacturing a uniform near-field surface acoustic wave microfluidic chip, including: setting key characteristic parameters of the chip, including the type and size of the piezoelectric substrate, the width W of the working area, work , the aperture W0 of the IDT, the operating frequency f and the distance between the far and near fields y g ; Among them, the type and size of the piezoelectric substrate are used to determine the size and structure of the chip; the working area width W workUsed to determine the size of the chip's working area; the IDT's aperture W0, number of fingers N, and operating frequency f are used to determine the IDT's parameters; the near-far field separation distance y g Used to divide the far field and near field area of ​​the interdigital transducer; according to the set working area width W work and the aperture W0 of the IDT, establish the discrete sequence {L i}, discrete sequence {L i} represents the range of the length of the finger within the aperture range; and according to the discrete sequence {L i}, calculate the discrete sequence {W i}, discrete sequence {W i} represents the value range of the equivalent line source length; the equivalent line source corresponds to the area composed of the overlapping parts of two adjacent fingers; the discrete sequence {W i}, each value is the line length, the surface acoustic wave field corresponding to the equivalent line source of different lengths is calculated, and the acoustic field prediction model of the straight interdigital transducer with discrete aperture apodization is established; a cost function is constructed, which is used to quantitatively evaluate the non-uniformity of the surface acoustic wave field generated by the interdigital transducer; the discrete sequence {l n} is a variable, and the optimization algorithm is used to optimize the discrete aperture apodization scheme to minimize the cost function; where the discrete sequence {l n} contains N values, each of which corresponds to the length of each finger of the IDT within the aperture range, and the range of each value is determined by the discrete sequence {L i}determine, according to the discrete sequence {l n Setting the length of each finger is the specific implementation method of the aperture tracking scheme; according to the finger weighting scheme, an interdigital transducer is manufactured on the set piezoelectric substrate according to public knowledge; a microfluidic cavity channel is manufactured and fixed in the working area of ​​the interdigital transducer to obtain a uniform near-field surface acoustic wave microfluidic chip.

[0011] Furthermore, the far-near field boundary distance y of the IDT is g satisfy: Wherein, λ is the wavelength of the surface acoustic wave, k is a coefficient related to the type of piezoelectric substrate, and its specific value can be found in reference materials disclosed in the art. In particular, for a 128° YX lithium niobate substrate, k = 0.675; the working area of ​​the chip should be located as close as possible to the near field of the interdigital transducer; other parameters such as substrate size, transducer operating frequency f, and the number of fingers N are determined as is well known in the art.

[0012] Furthermore, the finger length KL within the aperture range satisfies: (W0+W work ) / 2≤KL≤W0; Set the value range of KL to the discrete sequence {L i}; According to {L i} operation to obtain the discrete sequence {W i}, specifically, for any L m , L n ∈{L i}, binary function L m +L n -W0's value range is the discrete sequence {W i}.

[0013] Furthermore, with the discrete sequence {W i} where each value is the line length. The surface acoustic wave field corresponding to the equivalent line source of different lengths is calculated using the following method:

[0014]

[0015] Where u(x, y) is the scalar displacement amplitude at the spatial position (x, y) in the surface acoustic wave field corresponding to the equivalent line source. In particular, u(x, y) can be an in-plane displacement or an out-of-plane displacement according to actual application requirements; W is the length of the line source; i is the imaginary unit; k x and k y are the components of the surface acoustic wave wave vector in the x direction (parallel to the direction of the line source, i.e., the transverse direction) and the y direction (perpendicular to the direction of the line source, i.e., the axial direction), respectively, satisfying:

[0016] k=2πf / c(θ), Where θ is the angle between the wave vector direction and the x direction, c(θ) is the phase velocity of the surface acoustic wave propagating along the wave vector direction corresponding to θ; the relationship between the phase velocity c of the surface acoustic wave and the propagation direction θ is related to the type of piezoelectric substrate and can be used to characterize the anisotropy of the piezoelectric substrate. Its specific value can be found in reference materials disclosed in the field.

[0017] Furthermore, an acoustic field prediction model is constructed, including: precalculating the surface acoustic wave field distribution corresponding to line sources of different lengths; delaying and offsetting the surface acoustic wave field data of line sources of different lengths according to the aperture apodization scheme; superimposing the surface acoustic wave field distributions of line sources of different lengths that have undergone delay and offset processing to obtain the acoustic field distribution of a straight interdigital transducer with discrete aperture apodization; in this way, an acoustic field prediction model of an aperture apodized interdigital transducer is constructed, and the acoustic field distribution corresponding to different aperture apodization schemes can be calculated.

[0018] Specifically, since the length of each finger of the IDT has a finite number of discrete values, namely the discrete sequence {L i}, so the range of the effective line source length that may be involved is also a finite number of discrete values, that is, the discrete sequence {W i}, so the calculated surface acoustic wave field data corresponding to line sources of different lengths can be stored as a lookup table to facilitate subsequent calls and calculations. n}, determine the position, length and phase of each line source. For each line source, its phase is related to the voltage distribution on the adjacent interdigital fingers; generally, it can be considered that there is a phase difference of π between two adjacent equivalent line sources. According to the phase and position of each line source, the corresponding sound field is phase delayed and position shifted, and then all the delayed and shifted line source surface acoustic wave field distributions are coherently superimposed. The result of the superposition is the sound field distribution result generated by the flat interdigital transducer with discrete aperture tracking in the target area. The sound field distribution result is the prediction result obtained by the sound field prediction model, which reflects the aperture tracking scheme {l n This prediction model can quickly evaluate the acoustic field control effects of different aperture apodization schemes, providing a basis for optimized design.

[0019] Furthermore, the surface acoustic wave field superposition formula is:

[0020]

[0021] Among them, u total (x, y) is the scalar displacement amplitude at (x, y) in the surface acoustic wave field corresponding to the transducer, N is the number of fingers of the transducer, i is the imaginary unit, u n (x, y) is the surface acoustic wave field generated by the equivalent line source composed of the overlapping part of the n-th finger (n = 1, 2, ..., N-1) and the n+1-th finger, which is determined by the length of the equivalent line source. Since the sound field distribution corresponding to the equivalent line source of all possible lengths has been calculated above, the corresponding result is directly selected according to the length of the equivalent line source without recalculation; suppose the lengths of the n-th finger and the n+1-th finger within the aperture range are L n and L n+1 , then the length of the line source is L n +L n+1 -W0;x n is the distance from the midpoint of the line source to the central axis of the transducer, x n =(-1) n (L n -L n+1 ) / 2;y n is the average value of the positions of the nth finger and the n+1th finger in the y direction.

[0022] Furthermore, for the case where the designed chip contains only a single IDT, the surface acoustic wave field distribution u can be directly calculated using the surface acoustic wave field superposition formula. total(x, y); For the case where the designed chip contains two relatively parallel interdigital transducers, the surface acoustic wave field superposition formula is used to calculate the distribution u of the surface acoustic wave field total1 (x, y) and u total2 (x, y), and add the results to get the total surface acoustic wave field distribution u total (x, y).

[0023] Furthermore, for the case where the designed chip contains multiple interdigital transducers placed non-parallel to each other, the surface acoustic wave field superposition formula is applied to each transducer to calculate the corresponding surface acoustic wave field, and subsequent optimization processes are performed independently on each transducer.

[0024] Furthermore, a cost function for measuring near-field non-uniformity is established, including: setting a calculation area of ​​the cost function, the size of the calculation area is consistent with the size of the chip's working area, and is located in the near-field area corresponding to the interdigital transducer; establishing a cost function based on the chip type and the ideal surface acoustic wave field; the cost function is expressed as a discrete sequence {l n} is a variable, and the surface acoustic wave field distribution in the chip working area is calculated by the established acoustic field prediction model; the calculated surface acoustic wave field distribution is compared with the ideal surface acoustic wave field distribution preset according to the chip type to obtain a value for measuring the near-field non-uniformity; the value for measuring the near-field non-uniformity obtained in this way is the discrete sequence {l n}The corresponding cost function calculation result.

[0025] Furthermore, a cost function is established based on the chip type and the ideal distribution of the surface acoustic wave field, including: judging the type of the corresponding surface acoustic wave field and calculating the ideal sound field distribution based on the chip type and the set working frequency f; the types include traveling wave fields and standing wave fields; when the surface acoustic wave field is a traveling wave field, calculating the deviation function between the sound field distribution in the working area and the preset traveling wave field distribution as the cost function; when the surface acoustic wave field is a standing wave field, calculating the deviation function between the sound field distribution in the working area and the preset standing wave field distribution as the cost function.

[0026] Preferably, when optimizing the discrete aperture apodization scheme, the optimization algorithms that can be used include but are not limited to genetic algorithms, simulated annealing algorithms, particle swarm optimization algorithms, ant colony algorithms, gradient descent methods or machine learning optimization algorithms. The goal of the optimization is to obtain a uniform sound field distribution in the working area. Considering that we have set a cost function to quantitatively evaluate the non-uniformity of the sound field, we can use the calculation result of reducing the cost function as the optimization direction. Determine the constraints of the aperture apodization scheme and determine the value range of the finger length within the aperture range as a discrete sequence {L i} to ensure the feasibility and stability of the optimization results. According to the characteristics and complexity of the problem, select an appropriate optimization algorithm. Commonly used optimization algorithms include: Genetic algorithm: By simulating the biological evolution process, a set of candidate solutions are selected, crossed and mutated, and optimized generation by generation to obtain the optimal solution. Simulated annealing algorithm: Drawing on the physical annealing process, through random perturbations and probabilistic acceptance criteria, the global optimal solution is searched in the solution space. Particle swarm optimization algorithm: By simulating the clustering behavior of bird flocks or fish schools, the position and velocity update rules of particles are used to search for the optimal solution in the solution space. Ant colony algorithm: By simulating the behavior of ants looking for food, the pheromone update and path selection mechanism are used to search for the optimal path in the solution space. Machine learning algorithm: Using machine learning models such as neural networks and support vector machines, through training and optimization processes, learn and predict the optimal solution from data. The parameterization of the aperture apodization scheme is expressed by a discrete sequence {l n} as the input to the optimization algorithm. The non-uniformity index of the surface acoustic wave field, i.e., the calculated result of the cost function, is used as the output of the optimization algorithm to evaluate the quality and convergence of the current solution. An interface is established between the optimization algorithm and the acoustic field prediction model to enable automatic solution evaluation and iterative updating. The optimization algorithm is executed to obtain the optimal solution: The optimization algorithm parameters, such as population size, number of iterations, and learning rate, are initialized. A set of initial solutions is randomly generated as the starting point of the optimization algorithm. According to the rules of the optimization algorithm, the current solution is updated and iterated to continuously improve the solution quality. In each iteration, the acoustic field distribution corresponding to the current solution is calculated using the acoustic field prediction model and its non-uniformity index is evaluated. The iterative process is repeated until convergence conditions are reached or the maximum number of iterations is met, resulting in the optimal aperture apodization solution. The optimized aperture apodization solution obtained through optimization is applied to the design and fabrication of surface acoustic wave devices. The effectiveness and feasibility of the optimization results are verified through simulation and experimental testing. The changes in the surface acoustic wave distribution before and after optimization are analyzed to evaluate the performance and improvement of the optimization algorithm. If necessary, the optimization algorithm is adjusted and improved to obtain even better results.

[0027] Preferably, when making an interdigital transducer, for the fingers within the transducer aperture whose length is less than the aperture, dummy fingers need to be set according to a known method; the purpose of setting the dummy fingers is to suppress the influence of phase distortion on the sound field distribution. Specifically, the length of each finger within the aperture of the interdigital transducer is determined according to the aperture tracking scheme obtained by optimization. Find the fingers whose length is less than the aperture, and these fingers need to be set with dummy fingers on the opposite bus bar. The material, width, spacing and other parameters of the dummy fingers are consistent with those of the normal fingers, and the length of the dummy fingers is determined by the difference between the length of the normal fingers and the length of the aperture. The dummy fingers do not affect the excitation of the surface acoustic wave, nor do they affect the calculation of the equivalent line source. Their function is to compensate for the fingers of insufficient length so that they exhibit a phase delay similar to that of the normal fingers during the propagation of the surface acoustic wave, thereby avoiding the influence of uneven phase delay on the uniformity of the sound field.

[0028] Preferably, the specific process of making and fixing the microfluidic cavity channel is as follows: design and make the microfluidic cavity channel according to the known method according to the actual needs, and pay attention to making the working area size of the microfluidic cavity channel consistent with the calculation area size of the cost function; fix the made microfluidic cavity channel on the piezoelectric substrate, and pay attention to fix it in the near field area of ​​the interdigital transducer and cover the calculation area of ​​the cost function to form a surface acoustic wave microfluidic chip with uniform near field. Specifically, according to the calculation area of ​​the established cost function, determine the fixed position of the microfluidic cavity channel so that the microfluidic cavity channel is fixed in the calculation area of ​​the cost function; according to the set working area width W work , determine the working area width of the microfluidic cavity channel so that the working area width of the microfluidic cavity channel is consistent with the working area width of the chip; integrate the fabricated microfluidic cavity channel with the interdigital transducer fabricated on the piezoelectric substrate so that the microfluidic cavity channel is fixed in the near-field area of ​​the interdigital transducer and covers the calculation area of ​​the cost function, thereby forming a surface acoustic wave microfluidic chip with uniform near-field.

[0029] 3. Beneficial effects

[0030] Compared with the existing technology, the advantages of this application are:

[0031] Using the equivalent line source model and plane wave angular spectrum theory, an acoustic field prediction model for a straight interdigital transducer with discrete aperture apodization is constructed. This model can accurately predict the surface acoustic wave field distribution under different aperture apodization schemes, providing a theoretical basis for optimizing the acoustic field uniformity.

[0032] The anisotropy of the piezoelectric substrate is taken into consideration when calculating the acoustic field distribution. The calculated acoustic field distribution is more consistent with the actual situation, and this calculation method can be widely applied to different piezoelectric substrate materials.

[0033] By choosing to use the discrete aperture apodization method, the designed finger length values ​​and equivalent line source length values ​​are both limited. The sound field distribution corresponding to equivalent line sources of different lengths can be calculated in advance. When subsequently calculating the sound field distribution corresponding to the aperture apodized interdigital transducer, the pre-calculated results can be directly referenced without repeated calculations, which greatly speeds up the calculation speed.

[0034] While improving the lateral uniformity of the sound field in the working area, it has little impact on the sound field intensity.

[0035] By determining the type of surface acoustic wave and establishing corresponding cost functions for traveling waves or standing waves, the optimization requirements of different types of surface acoustic wave microfluidic chips can be adapted, thereby improving the applicability and flexibility of the method.

[0036] The optimization algorithm is used to obtain the aperture tracking scheme of the interdigital transducer, which makes the sound field in the working area of ​​the surface acoustic wave microfluidic chip have good lateral invariance, effectively improves the uniformity of the surface acoustic wave field distribution in the near-field area, and overcomes the problem of near-field sound field unevenness caused by the diffraction effect in the existing technology.

[0037] An interdigital transducer is manufactured according to the optimized finger weighting scheme, and a microfluidic cavity channel with the same width as the chip working area is manufactured and fixed in its near-field area. This can combine the theoretical optimization results with actual chip production, ensuring the uniformity of the surface acoustic wave field distribution in the near-field area of ​​the chip.

[0038] For chips containing multiple interdigital transducers, by calculating the surface acoustic wave field distribution of each transducer separately and establishing the corresponding acoustic field prediction model, the acoustic field uniformity of complex chip structures can be optimized, expanding the application scope of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A design process for a uniform near-field surface acoustic wave microfluidic chip for this application;

[0040] Figure 2 Schematic diagram of the surface acoustic wave microfluidic chip structure in Example 1 of the present application;

[0041] Figure 3 Comparison of the sound field distribution generated on a cross-section of the chip working area using a non-apodized IDT and a uniform near-field IDT in Example 1 of the present application;

[0042] Figure 4 Schematic diagram of the surface acoustic standing wave microfluidic chip structure in Example 2 of the present application;

[0043] Figure 5 This is a comparison of the sound field distribution generated on the cross-section of the chip working area using a non-apodized IDT and a uniform near-field IDT in Example 2 of the present application.

[0044] Reference numerals: 01, piezoelectric substrate; 02, interdigital transducer; 03, microfluidic cavity; 04, working area of ​​surface acoustic wave microfluidic chip; 05, center section line of the working area. DETAILED DESCRIPTION

[0045] The present application is described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] Example 1

[0047] This embodiment specifically describes a uniform near-field surface acoustic wave microfluidic chip. The basic structure of the surface acoustic wave chip is as follows: Figure 2As shown, the chip comprises a piezoelectric substrate, an IDT, and a microfluidic cavity. The chip's operating area is typically within the IDT's near-field, located within the microfluidic cavity. The traveling surface acoustic waves (SAWs) generated by the IDT radiate into the cavity, forming a traveling wave field within the operating area, enabling manipulation of the reagent within the cavity.

[0048] Determine the characteristic parameters of the surface acoustic wave microfluidic chip: Input: piezoelectric substrate material and size: 128° YX lithium niobate, thickness 0.5mm; IDT initial aperture W0: 3200μm; finger pitch p: 100μm; drive frequency f: 19.70MHz; number of finger pairs: 20 pairs (number of fingers N = 40); working area position: the center is located on the axis of the IDT, 6400μm from the first finger; working area size: width W work 1920 μm, length 800 μm. Calculation: SAW wavelength: λ = 200 μm (calculated based on the drive frequency and substrate material), far-near field boundary distance: y g =0.675W0 2 / λ=34.56mm; Output: Determine that the working area is within the near field range.

[0049] Determine the finger length and equivalent line source length: working area width W work The initial aperture W0 of the IDT is 1920 μm, and the ratio of the two is 0.6. In order to make the fingers cover the working area, the minimum value of the finger length within the aperture range should be around 0.8W0. For the convenience of subsequent calculations, the discrete sequence {L i} is set as the first item The last term is W0, and the tolerance is 8-digit arithmetic progression. From this we can get that for any L m , L n ∈{L i}, binary function L m +L n -W0's value range is the first item The last term is W0, and the tolerance is 15-digit arithmetic progression, which is the discrete sequence representing the range of line source length {W i}.

[0050] Calculate the surface acoustic wave field corresponding to line sources of different lengths: sequentially transform the discrete sequence {W i}Substitute the values ​​in the formula as the line source length W:

[0051]

[0052] The surface acoustic wave fields corresponding to line sources of different lengths can be obtained, where u(x, y) is the scalar displacement amplitude at the spatial position (x, y) in the surface acoustic wave field corresponding to the line source. This is considered to be the off-plane displacement that plays a dominant role in surface acoustic wave microfluidics. The piezoelectric substrate used in the surface acoustic wave microfluidic chip described in this example is 128° YX lithium niobate, and the driving frequency f is 19.70 MHz. Substituting the relationship between the surface acoustic wave phase velocity c and the propagation direction θ into the formula:

[0053] k=2πf / c(θ), The two components k of the surface acoustic wave wave vector can be obtained x and k y , where the direction corresponding to θ=0 is consistent with the X-axis direction of the lithium niobate crystal.

[0054] The acoustic field prediction model for a straight IDT with discrete aperture apodization is established: The acoustic field distribution of a straight IDT with discrete aperture apodization can be obtained by delaying and superposing the surface acoustic wave fields corresponding to line sources of different lengths based on the aperture apodization. Specifically, the surface acoustic wave field superposition formula is:

[0055]

[0056] Where N = 40, i is the imaginary unit, u n The choice of (x, y) is determined by the length of the overlapping portion of the nth finger (n = 1, 2, ..., 39) and the n+1th finger: let the lengths of the nth finger and the n+1th finger within the aperture be L respectively. n and L n+1 , then the length of the line source is L n +L n+1 -W0;x n is the distance from the midpoint of the line source to the central axis of the transducer, x n =(-1) n (L n -L n+1 ) / 2;y n is the average value of the positions of the nth finger and the n+1th finger in the y direction. For two adjacent line sources, the distance between them in the y direction is half a wavelength, i.e. 100 μm. From this, we can obtain the displacement amplitude distribution u of the surface acoustic wave field corresponding to the IDT under a certain discrete aperture apodization scheme. total (x, y).

[0057] Set a cost function to measure the non-uniformity of the sound field: For a surface acoustic wave chip, the sound field in the working area is a surface acoustic wave field, and its ideal distribution is a uniform amplitude throughout the entire working area. In order to measure the difference between the actual sound field distribution and the ideal distribution, a cost function can be set. Here, the cost function is defined as the standard deviation of the normalized amplitude within the working area. Specifically, the sound field amplitude value at each position in the working area is calculated, and the amplitude value is normalized so that the root mean square of the amplitude in the working area is 1. The standard deviation of the normalized amplitude value is calculated to obtain the value of the cost function. In this way, the higher the lateral uniformity of the sound field, the smaller the standard deviation of the normalized amplitude, and the smaller the value of the cost function. Therefore, the goal of optimization is to minimize the value of the cost function to obtain the sound field distribution with the highest lateral uniformity.

[0058] In this example, a genetic algorithm is selected as the optimization algorithm, and the optimal finger weighting method is searched through iterative evolution. Specifically, binary coding is used, and the length of each finger is represented by a 3-bit binary number. They are connected in sequence to form a binary code with a length of 120, representing a complete aperture apodization scheme. The opposite of the cost function plus a positive number is used as the fitness function Fit, so that the higher the uniformity of the sound field, the larger the Fit value. 200 binary codes with a length of 120 are randomly generated as the initial population. Iterative optimization: The binary codes in the population are decoded into the corresponding finger weighting method, and the sound field distribution under each weighting method is calculated based on the sound field prediction model. The fitness function Fit is calculated, and the weighting method with a larger Fit value is preferentially selected. The selected weighting method is crossover and mutation operations are performed to generate a new generation of population. The above selection, crossover and mutation process is repeated, and the population is iteratively updated. After multiple iterations, the binary code with the largest Fit value in the last generation of population is decoded to obtain the optimal aperture apodization scheme. Through iterative optimization using a genetic algorithm, we can efficiently search for the finger weighting method that maximizes the lateral distribution of the traveling surface acoustic wave field within the working area. This optimization method comprehensively considers the weighting effect of each finger and continuously optimizes through an evolutionary mechanism to ultimately obtain the global optimal solution.

[0059] After obtaining the best finger weighting method through genetic algorithm optimization, the aperture of the interdigital transducer needs to be tracked according to the optimization results. Specifically, the length of each finger is set in sequence according to the optimization results. Then, for the fingers with a length less than W0 within the aperture range, dummy fingers need to be set on their opposite bus bars to make the metal electrode distribution more uniform and suppress the influence of phase distortion during surface acoustic wave propagation on the uniformity of the sound field (such as Figure 2As shown in Figure 2 , the designed IDT is fabricated on a 128° YX lithium niobate wafer using known methods (such as photolithography and metal deposition). A microfluidic cavity is fabricated according to actual needs and affixed to the lithium niobate substrate. Note that the working area of ​​the microfluidic cavity must be consistent with the calculation area of ​​the cost function. Through these steps, a uniform near-field surface acoustic wave microfluidic chip based on the optimized design is obtained.

[0060] Compared with the non-apodized IDT, the uniform near-field surface acoustic wave microfluidic chip of the present application can significantly improve the lateral uniformity of the sound field in the working area. Figure 3 As shown: Un-varied fork-finger transducer: the sound field distribution on the center section of the working area is affected by the diffraction effect and has obvious fluctuations. Uniform near-field fork-finger transducer: the sound field distribution on the center section of the working area is smooth, and the influence of the diffraction effect is suppressed. The root mean squares of the two sound field distributions are 1.0119 and 0.9846 respectively, indicating that the sound field intensity is almost unaffected by the finger weighting. After normalizing the amplitudes of the two sound fields, the corresponding standard deviations are calculated to be 0.0761 and 0.0054 respectively. It can be seen that the use of the surface acoustic wave microfluidic chip of the present application can reduce the standard deviation of the sound field distribution in the working area by more than 90% with little effect on the sound field intensity. This significantly improves the lateral uniformity of the surface acoustic wave field, which is of great help in improving the performance of surface acoustic wave devices.

[0061] Example 2

[0062] This embodiment specifically describes a uniform near-field surface acoustic wave microfluidic chip. The basic structure of the surface acoustic wave chip is as follows: Figure 4 Compared with the traveling surface acoustic wave chip in Example 1, the standing wave chip includes two groups of interdigital transducers, each of which excites traveling surface acoustic waves that radiate into the cavity, forming a standing surface acoustic wave field in the working area, thereby realizing the manipulation of the reagent in the cavity.

[0063] The design process of uniform near-field acoustic surface standing wave microfluidic chip is as follows Figure 1 The specific steps are as follows:

[0064] 128°YX lithium niobate is selected as the piezoelectric substrate material with a thickness of 0.5mm. The working area is determined to be a rectangle with a width of 3600μm and a length of 1600μm. Two groups of interdigital transducers are designed to be symmetrical about the center of the working area, with the first finger 11.25mm away from the center of the working area. Each group of interdigital transducers contains 30 pairs of fingers (60 in total), the initial aperture W0 is 6000μm, and the finger spacing p is 100μm. The driving frequency f is set to 19.70MHz. According to the characteristics of the substrate material, the wavelength λ of the excited surface acoustic wave is about 200μm. Using the formula y g =0.675W0 2 / λ calculates the far-near field boundary distance and obtains y g = 121.5 mm. Make sure the working area is within the near field (less than 121.5 mm from the IDT).

[0065] In this example, it is necessary to determine the length of the fingers in the interdigital transducer and the length of the equivalent line source. The specific implementation is as follows: the width of the working area W work : 3600μm, initial aperture of IDT W0: 6000μm, W work / W0=3600μm / 6000μm=0.6. The minimum finger length should be around 0.8W0 to ensure that the finger can cover the working area. Generate a discrete sequence representing the range of finger length values ​​within the aperture range {L i}: First item: 0.79W0, Last item: W0, Tolerance: 0.03W0, Number of items: 8, {L i} is an 8-bit arithmetic progression. Generate a discrete sequence representing the range of line source length {W i}: For any L m , L n ∈{L i}, binary function L m +L n -W0 value range consists of {W i}, first item: 0.58W0 (i.e. 0.79W0+0.79W0-W0), last item: W0 (i.e. W0+W0-W0), tolerance: 0.03W0 (with {L i}, number of items: 15. i} is a 15-digit arithmetic progression. Through the above steps, we can get two discrete sequences {L i} and {W i}, respectively represent the range of the finger length and line source length within the aperture range. Among them, {L i The generation of {W i The generation of} is based on the combination of finger lengths.

[0066] Calculate the surface acoustic wave field corresponding to line sources of different lengths: sequentially transform the discrete sequence {W i}Substitute the values ​​in the formula as the line source length W:

[0067]

[0068] The surface acoustic wave fields corresponding to line sources of different lengths can be obtained, where u(x, y) is the scalar displacement amplitude at the spatial position (x, y) in the surface acoustic wave field corresponding to the line source. This is considered to be the off-plane displacement that plays a dominant role in surface acoustic wave microfluidics. The piezoelectric substrate used in the surface acoustic wave microfluidic chip described in this example is 128° YX lithium niobate, and the driving frequency f is 19.70 MHz. Substituting the relationship between the surface acoustic wave phase velocity c and the propagation direction θ into the formula:

[0069] k=2πf / c(θ), The two components k of the surface acoustic wave wave vector can be obtained x and k y , where the direction corresponding to θ=0 is consistent with the X-axis direction of the lithium niobate crystal.

[0070] The sound field prediction model in this example is basically the same as that in Example 1, but it is necessary to consider that there are two groups of IDTs in the standing wave chip that are symmetrical about the center of the working area. The specific implementation steps are as follows: Use the surface acoustic wave superposition formula to calculate the amplitude distribution u in the working area corresponding to a group of IDTs total (x, y). total (x, y) performs a central symmetric operation about the center of the working area to obtain u total *(x, y). Add the two amplitude distributions, i.e. u total (x, y) + u total *(x, y) to obtain the amplitude distribution of the standing wave acoustic field within the working area of ​​the standing wave chip. This method can be used to establish an acoustic field prediction model suitable for standing wave chips based on Example 1. This model considers the contributions of two sets of interdigital transducers and can more accurately describe the distribution characteristics of the standing wave acoustic field.

[0071] Set a cost function to measure the non-uniformity of the acoustic field: For the surface acoustic wave standing wave chip, the acoustic field in the working area is a surface acoustic wave standing wave field, and its ideal distribution has antinodes and node positions. Therefore, the standard deviation cannot be used directly to judge the uniformity of the acoustic field. In standing wave type surface acoustic wave microfluidic chips, more attention is usually paid to the lateral uniformity of the amplitude at the antinode and node positions. Based on this, the cost function can be set to the standard deviation of the normalized amplitude at the antinode position in the working area. The higher the lateral uniformity of the sound field, the smaller the calculated result of the cost function will be. Through the above steps, a suitable cost function can be obtained to measure the lateral uniformity of the surface acoustic wave standing wave field. This cost function pays attention to the characteristics of the standing wave sound field and can better guide the subsequent optimization design process.

[0072] In this example, a genetic algorithm is used as the optimization algorithm, searching for the optimal finger weighting scheme through iterative evolution. Specifically, considering the centrosymmetry of the two groups of interdigital transducers, only one group needs to be encoded. The length of each finger is represented by a 3-bit binary number, which is sequentially connected to form a binary code with a length of 180, representing a complete aperture apodization scheme. The fitness function Fit is calculated by adding a positive number to the inverse of the cost function. The higher the lateral uniformity of the sound field, the larger the Fit value. 200 binary codes with a length of 180 are randomly generated as the initial population. The binary codes in the population are decoded into the corresponding finger weighting scheme. The sound field distribution under each weighting scheme is calculated based on the sound field prediction model. The fitness function Fit is then calculated, and the weighting scheme with the largest Fit value is preferentially selected. The selected weighting scheme is then crossover and mutated to generate a new generation of population. The selection, crossover, and mutation process is repeated to iteratively update the population. After multiple iterations, the binary code with the largest Fit value in the final generation of population is decoded to obtain the optimal aperture apodization scheme, which achieves the most uniform lateral distribution of the traveling wave field on the surface acoustic wave within the working area. Through iterative optimization using a genetic algorithm, the optimal finger weighting scheme can be efficiently searched for. This optimization method comprehensively considers the weighting effects of each finger and continuously optimizes through an evolutionary mechanism, ultimately achieving a global optimal solution. Through multiple iterations, the genetic algorithm is able to find the optimal aperture apodization scheme within the vast search space of finger weighting schemes. This scheme can maximize the lateral uniformity of the traveling surface acoustic wave field within the working area.

[0073] Since the two groups of IDTs in the standing wave chip are symmetrical about the center of the working area, the optimal weighting method obtained by the genetic algorithm can be applied to the two groups of IDTs at the same time. Specifically, the aperture of the two groups of IDTs is apodized separately, and the finger length is set according to the optimized weighting method. Dummy fingers are set in the apodized IDTs to suppress the influence of phase distortion on the uniformity of the sound field. After setting the dummy fingers, the structures of the two groups of IDTs are as follows: Figure 4 As shown in Figure 2, the designed IDT is fabricated on a 128° YX lithium niobate wafer using known methods (such as photolithography and metal deposition). A microfluidic cavity is fabricated according to actual requirements and fixed to the lithium niobate substrate. Note that the working area of ​​the microfluidic cavity must be consistent with the calculation area of ​​the cost function.

[0074] Since the two groups of IDTs are symmetrical about the center of the working area, the center section of the working area corresponds exactly to the position of the standing wave field antinode. By comparing the surface acoustic wave microfluidic chip using the non-apodized IDT and the one using the uniform near-field IDT of the present application, it can be found that (e.g. Figure 5(as shown): Un-averted fork-finger transducer: at the position of the wave trough, the sound field distribution is affected by the diffraction effect and has obvious fluctuations. Uniform near-field fork-finger transducer: at the position of the wave trough, the sound field distribution is smooth and the influence of the diffraction effect is suppressed. The amplitudes of the two sound field distributions at the wave trough are quantitatively analyzed: the root mean square value: 1.9525 for the un-averted and 1.9639 for the uniform near field, and the finger weighting slightly increases the standing wave field strength. The normalized standard deviation: 0.0415 for the un-averted and 0.0030 for the uniform near field, and the standard deviation has dropped by more than 90%. It can be seen that the uniform near-field surface acoustic wave microfluidic chip of the present application can significantly improve the uniformity of the amplitude at the wave trough with little effect on the standing wave sound field strength. This is of great significance for improving the performance of standing wave devices.

[0075] The invention of the present application and its implementation methods are described schematically above. This description is not restrictive. Without departing from the spirit or basic features of the present application, the present application can be implemented in other specific forms. What is shown in the drawings is only one of the implementation methods of the invention of the present application. The actual structure is not limited to this. Any figure mark in the claims should not limit the claims involved. Therefore, if ordinary technicians in this field are inspired by it, without departing from the purpose of the present invention, they can design structural methods and embodiments similar to the technical solution without creativity, which should all fall within the scope of protection of this patent. In addition, the word "including" does not exclude other elements or steps, and the word "one" before an element does not exclude including "a plurality of" such elements. The multiple elements stated in the product claim can also be implemented by one element through software or hardware. Words such as first and second are used to indicate names, and do not indicate any specific order.

Claims

1. A method for fabricating a uniform near-field surface acoustic wave microfluidic chip, comprising: Set the key characteristic parameters of the chip, which include the type and size of the piezoelectric substrate, the working area width W work , the aperture W0 of the IDT, the operating frequency f and the distance between the far and near fields y g ; Among them, the type and size of the piezoelectric substrate are used to determine the size and basic structure of the chip; the working area width W work Used to determine the size of the chip's working area; the IDT's aperture W0, number of fingers N, and operating frequency f are used to determine the IDT's parameters; the near-far field separation distance y g Used to divide the far field and near field areas of the interdigital transducer; According to the set working area width W work and the aperture W0 of the IDT, establish the discrete sequence {L i }, discrete sequence {L i } represents the range of the length of the finger within the aperture range; and according to the discrete sequence {L i }, calculate the discrete sequence {W i }, discrete sequence {W i } represents the value range of the equivalent line source length; wherein, the equivalent line source corresponds to the area formed by the overlapping parts of two adjacent fingers; With discrete sequence {W i The values ​​in} are line lengths. The surface acoustic wave fields corresponding to equivalent line sources of different lengths are calculated, and an acoustic field prediction model for a straight interdigital transducer with discrete aperture apodization is established. Construct a cost function to quantitatively evaluate the non-uniformity of the surface acoustic wave field generated by the interdigital transducer; n } is a variable, the discrete sequence {l n } represents the length of each finger within the aperture range, and the discrete aperture apodization scheme is optimized using an optimization algorithm to minimize the cost function; According to the finger weighting scheme, an interdigital transducer is fabricated on a set piezoelectric substrate; a microfluidic cavity channel is fabricated and fixed in the working area of ​​the chip to obtain a uniform near-field surface acoustic wave microfluidic chip; With discrete sequence {W i The values ​​in} are line lengths. The surface acoustic wave fields corresponding to equivalent line sources of different lengths are calculated using the following method: where u ( x , y ) is the scalar displacement amplitude at the spatial position ( x , y ) in the surface acoustic wave field corresponding to the equivalent line source; W is the length of the line source; i is the imaginary unit; k x and k y are the components of the surface acoustic wave's wave vector in the x and y directions, respectively, satisfying: Where θ is the angle between the wave vector direction and the x direction, ; c(θ) is the phase velocity of the surface acoustic wave propagating along the wave vector direction corresponding to θ; Construct a sound field prediction model, including: Pre-calculate the surface acoustic wave field distribution corresponding to line sources of different lengths; According to the aperture apodization scheme, the surface acoustic wave field distribution of line sources with different lengths is delayed and shifted; The surface acoustic wave field distributions of line sources of different lengths that have been delayed and shifted are superimposed to obtain the acoustic field distribution of a straight interdigital transducer with discrete aperture apodization. Construct an acoustic field prediction model for aperture-apodized interdigital transducers; The surface acoustic wave field distribution superposition formula is: Among them, u total (x, y) is the scalar displacement amplitude at (x, y) in the surface acoustic wave field corresponding to the transducer, N is the number of fingers of the transducer, i is the imaginary unit, and u is the n ( x , y ) is the surface acoustic wave field generated by the equivalent line source composed of the overlapping part of the n-th finger (n = 1, 2, …, N−1) and the n+1-th finger; x n y is the distance from the midpoint of the line source to the central axis of the transducer; n is the average value of the positions of the nth finger and the n+1th finger in the y direction.

2. The method for manufacturing a uniform near-field surface acoustic wave microfluidic chip according to claim 1, characterized in that: The far-near field boundary distance y of the interdigital transducer g satisfy: Where λ is the wavelength of the surface acoustic wave and k is a coefficient related to the type of piezoelectric substrate.

3. The method for manufacturing a uniform near-field surface acoustic wave microfluidic chip according to claim 1, characterized in that: The length KL of the finger within the aperture range satisfies: (W0 + W work ) ∕ 2≤ KL ≤W0.

4. The method for manufacturing a uniform near-field surface acoustic wave microfluidic chip according to claim 1, characterized in that: Also includes: The chip contains two interdigital transducers placed in parallel, and the surface acoustic wave field distribution u is calculated by the surface acoustic wave field superposition formula. total1 (x, y) and u total2 (x, y), add the two calculation results to get the total surface acoustic wave field distribution u total ( x , y ).

5. The method for manufacturing a uniform near-field surface acoustic wave microfluidic chip according to claim 1, characterized in that: Also includes: For a chip containing multiple IDTs placed non-parallel to each other, the surface acoustic wave field distribution of each transducer is calculated separately, and the sound field prediction model corresponding to each IDT is established, and subsequent optimization is performed independently for each transducer.

6. The method for manufacturing a uniform near-field surface acoustic wave microfluidic chip according to claim 1, characterized in that: Construct a cost function to quantitatively measure near-field non-uniformity, including: Set the calculation area of ​​the cost function. The size of the calculation area is consistent with the working area of ​​the chip and is located in the near field area corresponding to the interdigital transducer. Establish a cost function based on the chip type and the distribution of the surface acoustic wave field; The cost function is a discrete sequence {l n } is a variable, and the surface acoustic wave field distribution in the chip working area is calculated through the established acoustic field prediction model; Comparing the calculated surface acoustic wave field distribution with the preset surface acoustic wave field distribution based on the chip type to obtain a value that measures the near-field non-uniformity; A cost function that quantitatively measures near-field non-uniformity is obtained.

7. The method for manufacturing a uniform near-field surface acoustic wave microfluidic chip according to claim 6, characterized in that: According to the chip type and the distribution of the surface acoustic wave field, a cost function is established, including: According to the chip type and the set operating frequency f, the type of the corresponding surface acoustic wave field is determined and the distribution of the surface acoustic wave field is calculated; the types include traveling wave field and standing wave field; When the surface acoustic wave field is a traveling wave field, the deviation function between the acoustic field distribution in the working area and the preset traveling wave field distribution is calculated as the cost function; When the surface acoustic wave field is a standing wave field, a deviation function between the acoustic field distribution in the working area and the preset standing wave field distribution is calculated as a cost function.

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