Integration method of novel ceramic-based thin film circuit product

Abstract

The invention relates to the technical field of semiconductor processing, in particular to an integration method of a novel ceramic-based thin film circuit product, and the method comprises the steps: obtaining an etching matrix; obtaining a fractal dimension sequence of each row of data, and constructing an abnormal index; each row of data is converted into a frequency domain signal, partitioned analysis is carried out, and local heterogeneous evaluation parameters are constructed; analyzing the collaborative change of different types of data at each moment to construct a local consistency parameter, and constructing a collaborative factor in combination with similarity; the differential coefficient of PID control is improved; and the energy of the ion beam is regulated and controlled. The invention aims to overcome oscillation and instability caused by heterogeneity of the surface of an electroplated film and linkage change of parameters, improve the regulation and control accuracy of ion beam energy and guarantee the integration quality of a novel ceramic-based film circuit product.

Description

Technical Field

[0001] This application relates to the field of semiconductor processing technology, and particularly to an integration method for a new type of ceramic-based thin-film circuit product. Background Art

[0002] The ceramic-based thin-film circuit integration technology is based on the high-temperature stability, corrosion resistance, and high mechanical strength of ceramic-based materials, combined with thin-film processes, demonstrating excellent performance in special fields. In fields with extremely high performance requirements such as military, healthcare, and aerospace, its excellent performance at high temperatures, corrosion resistance, and high-frequency performance make it an ideal choice for electronic device design. This technology not only has significant advantages in miniaturization and high-density interconnection but also provides strong support for improving system stability and innovative design. In the future, with the continuous progress of technology, the integration of new ceramic-based thin-film circuits is expected to show its importance in a wider range of application fields, promoting the development of electronic device technology.

[0003] In the preparation process of ceramic-based thin-film circuits, after lithography and circuit steps are completed, local etching is carried out to remove the seed layer and the underlayer to complete circuit integration. During the etching process, due to the heterogeneity caused by different material components in the ceramic substrate, the etching rate of the ion beam is inconsistent in different parts. And due to the synergistic effect of various parameters in the etching situation, temperature fluctuations, vacuum degree changes, etc. during etching will cause a chain change in the process environment, further leading to unstable etching during the integration of ceramic-based thin-film circuits, generating errors, making the actual characteristics of the ion beam deviate from the theoretically designed parameters, thus affecting the accuracy of etching. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides an integration method for a new type of ceramic-based thin-film circuit product to solve the existing problems.

[0005] The integration method for a new type of ceramic-based thin-film circuit product of the present invention adopts the following technical solutions: An embodiment of the present invention provides an integration method for a new type of ceramic-based thin-film circuit product, and the method includes the following steps: Obtain various types of etching data, and use the various types of etching data as each row of the etching matrix; The detrended fluctuation analysis is adopted to obtain the fractal dimension sequence of each row of data in the etching matrix; the element distribution characteristics of the fractal dimension sequence of each row of data are analyzed, and combined with the mutation point detection algorithm, the data trend within the local range of the mutation point data is analyzed to obtain the anomaly index of each row of data; the frequency domain signal of each row of data is obtained, and based on the change of the frequency distribution range and amplitude size in the frequency domain signal of each row of data, the stability parameter of each row of data is determined, and combined with the anomaly index, the local heterogeneity evaluation parameter of each row of data is obtained; Taking each data in each row of data in the etching matrix as the center, a preset initial window length is set, and it uniformly increases towards both ends with a preset step length. According to the consistency of the data change within the window after the window increases for each data corresponding to any two rows of data in the etching matrix, the local consistency parameter between the any two rows of data is determined; comprehensively considering the distribution similarity degree, local consistency parameter, and local heterogeneity evaluation parameter between different rows of data, the cooperation factor of the etching matrix is determined; The differential coefficient of the PID control is improved according to the cooperation factor of the etching matrix; the new ceramic-based thin film circuit product is integrated in combination with the improved differential coefficient of the PID control.

[0006] Preferably, the adopting the detrended fluctuation analysis to obtain the fractal dimension sequence of each row of data in the etching matrix includes: Regarding each row of the etching matrix as the input of the multifractal detrended fluctuation analysis, and outputting the fractal dimension curve of each row of data; uniformly sampling the fractal dimension curve to obtain the fractal dimension sequence.

[0007] Preferably, the obtaining the anomaly index of each row of data is specifically: Presetting the local data sequence of each mutation point in the fractal dimension sequence of each row of data; According to the degree of element change and data increase or decrease trend in the local data sequence of each mutation point, calculating the local data characteristic value of each mutation point; Obtaining the dispersion degree of the elements in the fractal dimension sequence of each row of data; calculating the mean value of the local data characteristic values of all mutation points corresponding to each row of data; taking the result of the positive fusion of the dispersion degree and the local data characteristic value mean corresponding to each row of data as the anomaly index of each row of data.

[0008] Preferably, the calculating the local data characteristic value of each mutation point is specifically: For the fractal dimension sequence of each row of data, the slope of the straight line fitted by the local data sequence of each mutation point is positively fused with the element mean value of the first-order difference sequence of the local data sequence corresponding to the mutation point, and the local data characteristic value of each mutation point in the fractal dimension sequence of each row of data is obtained.

[0009] Preferably, the specific method for determining the stability parameter of each row of data is as follows: Obtain all the peaks and valleys of the frequency-domain signal of each row of data; Denote the absolute value of the amplitude difference between the peaks and valleys of the same ordinal position as the first difference; denote the absolute value of the frequency difference between the peaks and valleys of the same ordinal position as the second difference; denote the average value of the ratios between the first difference and the second difference at all ordinal positions as the stability parameter of each row of data in the frequency domain.

[0010] Preferably, the local heterogeneity evaluation parameter of each row of data is specifically the product of the local data eigenvalue and the stability parameter.

[0011] Preferably, determining the local consistency parameter between any two rows of data includes: Calculate the ratio between the length of each increased window and the maximum increased length of the window; obtain the negative correlation mapping result of the difference between the extreme values of the data within the window each time the window centered on each data in any two rows of data is increased; use the ratio as the weight of the corresponding negative correlation mapping result each time the window of any two rows of data is increased, and sum to obtain the local consistency parameter between any two rows of data.

[0012] Preferably, the specific process for determining the cooperation factor of the etching matrix is as follows: Obtain the similarity measure between any two rows of data and the difference of the local heterogeneity evaluation parameter, and combine the local consistency parameter to obtain the cooperation index between any two rows of data; Take the average value of the cooperation indices obtained from all pairwise combinations of row data in the etching matrix as the cooperation factor of the etching matrix.

[0013] Preferably, the specific method for obtaining the cooperation index between any two rows of data is as follows: For any two rows of data in the etching matrix, after positively fusing the similarity measure and the local consistency parameter, divide by the absolute value of the difference of the local heterogeneity evaluation parameter between any two rows of data to obtain the cooperation index between any two rows of data.

[0014] Preferably, improving the differential coefficient of the PID control according to the cooperation factor of the etching matrix includes: Preset an adjustment factor; obtain the normalized value of the cooperation factor of the etching matrix; calculate the product of the adjustment factor, the normalized value, and the differential coefficient of the PID control; use the product as the improved differential coefficient of the PID control.

[0015] The present invention has at least the following beneficial effects: The present invention improves the PID control algorithm for the problems of unstable etching and easy occurrence of errors caused by the heterogeneity of the ceramic base and the chain changes in the process environment during the etching step in the preparation of ceramic-based thin-film circuits. First, for the high precision of etching, microscopic multi-scale local analysis is carried out; then, for the heterogeneity of the electroplated thin-film surface during etching, FFT is used for partitioned analysis, and the heterogeneity situation reflected by the data is determined according to the change of its period and the difference in frequency width; finally, window analysis is carried out on the data for the chain changes between different data to obtain the final synergy factor, so that it can calculate the heterogeneity difference reflected by different data while reflecting the chain changes caused by the data synergy effect; then, the PID algorithm is improved according to the result, so that it can make real-time feedback on the oscillation and instability of the ion beam energy caused by the heterogeneity of the electroplated thin-film surface and the chain changes of parameters in real time, enhancing the robustness and practicability of the algorithm, improving the regulation accuracy of the ion beam energy, and ensuring the integration quality of the new ceramic-based thin-film circuit products. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of an integration method for a new ceramic-based thin-film circuit product provided by the present invention; Figure 2 It is a flowchart for obtaining the differential coefficient of the improved PID control. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific embodiments, structures, features and effects of an integration method for a new ceramic-based thin-film circuit product proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solution of an integration method for a novel ceramic-based thin-film circuit product provided by the present invention in conjunction with the accompanying drawings.

[0021] An integration method for a novel ceramic-based thin-film circuit product provided by an embodiment of the present invention.

[0022] Specifically, the following is provided an integration method for a novel ceramic-based thin-film circuit product. Please refer to Figure 1 , and the method includes the following steps: Step S001: Obtain various types of etching data and use the various types of etching data as the rows of the etching matrix.

[0023] In this embodiment, the semi-additive process with electroplating on adhesive is used to replace the traditional subtractive process. At the same time, a temporary protective layer is introduced in the etching stage, which can not only significantly improve the accuracy of the circuit, but also effectively protect the conductor layer during the process of etching the seed layer and the underlayer. Subsequently, advanced process methods such as layer-by-layer integration, electron beam evaporation, magnetron sputtering, electroless plating, and electroplating are used to form a nickel-chromium-gold composite thin film. Then, through precise process steps such as photolithography and selective electroplating, a conductive thin film or a resistive thin film is formed according to product requirements. Then, based on the circuit pattern to be etched, a photomask template is made for etching path planning, and the electroplated thin film is etched by dry physical ion sputtering method.

[0024] After the start of etching, etching data is collected, including but not limited to material change data, surface elevation difference data, temperature data, vacuum degree data, ion beam movement distance data, material change data at the corresponding position of the ion beam, surface roughness data at the corresponding position of the ion beam, ion beam energy data, ion beam current density data, etc.

[0025] In this embodiment, uniform collection is performed at time intervals of t, and the various types of collected etching data are normalized by Z-score. Z-score is a well-known technology in the art and will not be elaborated here. The etching matrix K is obtained, and the expression is: , wherein, is the data obtained for the m-th type of data at the n-th collection. In this embodiment, the value of m is 9.

[0026] It should be noted that taking the starting end to the branch end as a collection cycle, the branch end is the node where at least two different paths appear on the etching path, and the next etching position after passing through the node is taken as the starting end to perform data collection for the next cycle of the path. That is, within one collection cycle, the path passed by the ion beam is only one line segment and there will be no branch points.

[0027] Thus, the etching matrix is obtained.

[0028] Step S002: Use detrended fluctuation analysis to obtain the fractal dimension sequence of each row of data in the etching matrix; analyze the element distribution characteristics of the fractal dimension sequence of each row of data, and combine with the mutation point detection algorithm to analyze the data trend within the local range of the mutation point data, so as to obtain the anomaly index of each row of data; obtain the frequency domain signal of each row of data, and determine the stability parameter of each row of data based on the change of the frequency distribution range and amplitude size in the frequency domain signal of each row of data. Combine the anomaly index to obtain the local heterogeneity evaluation parameter of each row of data.

[0029] When etching ceramic thin-film circuits, due to the complex physical and chemical changes involved, the performance and surface quality of the circuits may be affected by a series of problems. For example, during the etching process, local subtle changes in the material quality of the electroplated layer surface may lead to uneven etching, thus affecting the performance of the circuit. If the temperature changes during the etching process, it will cause the thermal effect of the circuit, thereby causing thermal expansion or contraction of the circuit materials. Due to the precision of etching the electroplated layer, the data fluctuations caused by complex changes are all short-term fluctuations and are difficult to capture.

[0030] In this embodiment, the data of the i-th row in the etching matrix is taken as an example for analysis. Taking the data of the i-th row as the input, use multifractal detrended fluctuation analysis (MFDFA), and the output is the fractal dimension curve. The fractal dimension curve is a curve about how the fractal dimension changes with the scale. The characteristics of this curve can be used to understand the multi-scale fractal structure of the data, so as to analyze the complexity of the data in the microscopic situation more deeply. Analyze the fractal dimension curve of the data of the i-th row, and uniformly sample M data points from the fractal dimension curve to form a fractal dimension sequence. In this embodiment, M is taken as 100; use the Pettitt mutation point detection algorithm to mark the mutation points in the fractal dimension sequence, and save the number of mutation points in the fractal dimension curve as A. Taking the a-th mutation point as the center, the set including the a-th mutation point, the first h data points before the a-th mutation point, and the last h data points after the a-th mutation point is used as the local data sequence of the a-th mutation point. In this embodiment, h is taken as , where is the number of scales of the MFDFA algorithm, is the ceiling function; it should be noted that for less than h data points before or after a mutation point, all the data points before or after it are taken as part of the elements of the local data sequence of the mutation point; perform linear fitting based on the gradient descent method on the local data sequence of the a-th mutation point, and finally obtain the slope of the fitted straight line to construct the anomaly index of the data of the i-th row.

[0031] First, the local data characteristic value of each mutation point is calculated according to the degree of element change in the local data sequence of each mutation point and the data increase or decrease trend. Specifically, for the fractal dimension sequence of each row of data, the slope of the straight line obtained by fitting the local data sequence of each mutation point is forward fused with the element mean of the first-order difference sequence of the local data sequence of the corresponding mutation point to obtain the local data characteristic value of each mutation point in the fractal dimension sequence of each row of data. In this embodiment, the forward fusion of multiple variables adopts the multiplication calculation method.

[0032] Afterwards, the abnormal index of each row of data is determined by combining the local data eigenvalues of all mutation points in each row of data, the distribution characteristics of the fractal dimension sequence of each row of data, and the number of mutation points: the discrete degree of the elements in the fractal dimension sequence of each row of data is obtained; the mean of the local data eigenvalues of all mutation points corresponding to each row of data is calculated; and the result of forward fusion of the discrete degree and the mean of the local data eigenvalue corresponding to each row of data is used as the abnormal index of each row of data.

[0033] In this embodiment, the discrete degree of sequence elements is calculated by using the variance method; the forward fusion results between multiple variables are calculated by multiplication.

[0034] It should be understood that the anomaly index represents the change characteristics and trends of each row in the short-term local state. First, the greater the degree of discreteness of the fractal dimension sequence elements of each row of data, the more significant the relative difference fluctuation of each row of data, that is, when etching, the type of data in this row has obvious volatility. At this time, the greater the constraint on local changes, the greater the anomaly index; among them, the local data characteristics reflect the local change characteristics of each row of data. By calculating the average value of the product of the slope of the fitted straight line of the local data sequence of the a-th mutation point and the first-order difference, the local fluctuation of the i-th row of data is further analyzed as a whole. The slope of the fitted straight line of the sequence composed of the local range data of the mutation point reflects the trend of the fractal dimension. The larger its value represents the growth trend of the fractal dimension within the mutation range. Combined with the first-order difference sequence of the data in the window, it reflects the change of the fractal dimension at different scales. The larger the product, the more violent the fluctuation under the condition of local growth, that is, when etching, the properties of the heat-conducting film change violently when the ion beam moves slightly, and the greater the anomaly index.

[0035] The fractal dimension of the data in the i-th row is obtained through the MFDFA algorithm, and the detrended multi-scale analysis method is used to reflect the fluctuations in the local state of the etching data at different scales. However, the etching process can cause changes in the quality and smoothness of the circuit board surface, resulting in oscillations, ripples, or other periodic changes on its surface. Moreover, different-scale surface structures that are easily formed during the etching process, such as microscopic textures or macroscopic fluctuations, can lead to a dependence effect between the etching efficiency and the changes in the microscopic surface topography. When the change in the surface topography is relatively subtle or even unchanged, the etching efficiency is high. When the change in the surface topography is large, the etching efficiency will also be affected. However, due to the high precision of the etching, the collected data may contain non-steady or transient signals, making it more difficult to observe its periodicity from the collected data. However, in the frequency domain, these transient signals may be more prominently presented in the form of frequency components, making them easier to analyze and understand. Moreover, the partitioning of the peaks and valleys can be used to highlight or separate specific frequency components in the frequency-domain signal, enabling a more intuitive and targeted display of the signal characteristics.

[0036] Apply the Fast Fourier Transform (FFT) to the data in the i-th row to convert it from a time-domain signal to a frequency-domain signal, and record the number and magnitude of the peaks and valleys in the frequency-domain signal. Assume that the frequency-domain signal of the data in the i-th row contains peaks, and the magnitude of the x-th peak is , and the corresponding frequency is . Assume that the obtained frequency-domain signal contains valleys, and the magnitude of the y-th valley is , and the corresponding frequency is . Obtain the local heterogeneity evaluation parameter of the data in the i-th row.

[0037] First, based on the frequency distribution range and the change in the amplitude magnitude in the frequency-domain signal of the data in the i-th row, determine the stability parameter for evaluating the change of the data in the i-th row in the frequency domain: Denote the absolute value of the amplitude difference between the peak and valley of the same ordinal position as the first difference; Denote the absolute value of the frequency difference between the peak and valley of the same ordinal position as the second difference; Denote the average value of the ratio between the first difference and the second difference under all ordinal positions as the stability parameter of each row of data in the frequency domain.

[0038] In this embodiment, denote the stability parameter of the data in the i-th row in the frequency domain as , and its formula form is: ; In the formula, the stability parameter of the data in the i-th row in the frequency domain, is the minimum value function, , are respectively the numbers of peaks and valleys in the frequency-domain signal of the data in the i-th row, , are respectively the amplitude difference between the p-th peak and valley and the frequency difference between the p-th peak and valley in the frequency-domain signal of the i-th row of data.

[0039] After that, the product of the anomaly index and the stability parameter of each row of data is used as the local heterogeneity evaluation parameter for each row of data.

[0040] It should be understood that the local heterogeneity evaluation parameter reflects the heterogeneous situation of the electroplated thin film during the etching process in the data. By partitioning the p-th peak and valley of the frequency-domain signal, when is larger, it indicates that during the etching process, there are obvious peaks and valleys in the frequency domain of the i-th row of data, that is, the i-th row of data has a more obvious periodic change in the p-th frequency region, has a stronger periodic structure, and a strong periodic change reflected in the etching data means a higher heterogeneity on the electroplated surface. is larger, and vice versa; while represents the difference in the specific frequencies corresponding to the p-th peak and valley in the frequency-domain signal. In fact, this difference represents the width difference on the frequency axis. In frequency-domain analysis, the width of a frequency region can reflect the bandwidth or frequency range of the signal within that frequency region. For the peaks and valleys in the frequency-domain signal, the corresponding frequency difference represents the frequency distribution range of the signal within this frequency region. If it is larger, it means that within the p-th frequency region, the frequency difference between the peak and valley is larger, that is, the signal in this frequency region covers a wider frequency range, reflecting a more extensive interval of the p-th periodic change, that is, the occurrence of the p-th periodic change is more stable rather than drastic. is smaller.

[0041] S003: Taking each data in each row of the etching matrix as the center, preset the initial length of the window, and uniformly increase it towards both ends with a preset step size. According to the consistency of the data changes within the window after the window is increased for each data corresponding to any two rows of data in the etching matrix, determine the local consistency parameter between the any two rows of data; comprehensively determine the cooperation factor of the etching matrix based on the distribution similarity degree, local consistency parameter, and local heterogeneity evaluation parameter between different rows of data.

[0042] In etching data, due to the microscopic situation of ion etching, various parameters are extremely sensitive to changes in the environment or other parameters. Ion etching is a complex physical process, and there are correlations among various parameters. A change in one parameter may cause a coordinated change in other parameters. Suppose the energy of the ion beam is adjusted during the etching process, and the energy of the ion beam is increased. Increasing the energy of the ion beam will cause more energy to be converted into heat, increasing the temperature of the etching environment. Bombarding the material surface with high-energy ions will cause the evaporation of substances, increasing the number of gas molecules in the chamber, which will affect the vacuum degree. Moreover, adjusting the energy of the ion beam will affect the performance of the ion source, resulting in a change in the ion beam current density. High-energy ions will cause changes in the microscopic structure of the material surface, resulting in an increase in surface roughness, and so on.

[0043] First, calculate the local heterogeneity evaluation parameters of all rows and normalize their Norm norms to form a heterogeneity index sequence. For the i-th row data and the j-th row data in the etching matrix, calculate their Pearson correlation coefficient , and then, with the q-th data of the i-th row data as the center, construct a gradually increasing window. The initial scale of the window is . In this embodiment, I is taken as 3. The window increases uniformly at both ends, with a step size of 2 at each end, and the maximum increase times is U. U is taken as 3. Analyze the data within the window to construct the cooperation factor of the etching matrix.

[0044] First, determine the local consistency parameter between two rows of data according to the consistency of the data changes within the window after the window centered on each data in different rows of data is increased: calculate the ratio of the length of the window increased each time to the maximum increased length of the window; obtain the negative correlation mapping result of the difference between the extreme values of the data within the window each time the window centered on each data in any two rows of data is increased; use the ratio as the weight of the corresponding negative correlation mapping result each time the two rows of data are increased, and sum to obtain the local consistency parameter between the two rows of data.

[0045] In this embodiment, the local consistency parameter between the i-th row data and the j-th row data is denoted as , and its formula form is ; in the formula, is the local consistency parameter between the i-th row data and the j-th row data, n is the number of columns of the etching matrix, u is the number of times the window is increased, U is the maximum number of times the window is increased, is the window weight when increased u times, is the ratio of the length of the window after being increased u times to the length of the window after being increased U times; , is the range of the data within the window when the window centered on the q-th data in the i-th row data and the j-th row data is increased by u times. exp( ) represents the exponential function with the natural constant as the base.

[0046] After that, by synthesizing the local heterogeneity evaluation parameters of each row of data and the distribution similarity degree between different rows of data, the cooperation factor of the etching matrix is determined: obtaining the similarity measure between any two rows of data, after positively fusing with the local consistency parameter, dividing by the absolute value of the difference between the local heterogeneity evaluation parameters of the two rows of data, to obtain the cooperation index between the two rows of data; taking the mean of the cooperation indexes obtained from all pairwise combinations of row data in the etching matrix as the cooperation factor of the etching matrix.

[0047] In this embodiment, the cooperation factor of the etching matrix is denoted as , and its formula form is: ; in the formula, is the cooperation factor of the etching matrix, m represents the number of rows of the etching matrix, is the similarity measure between the i-th row data and the j-th row data. In this embodiment, the Pearson correlation coefficient is selected for calculation; is the absolute value of the difference between the local heterogeneity evaluation parameters of the i-th row and the j-th row data, is the local consistency parameter between the i-th row data and the j-th row data, is a preset adjustment parameter greater than zero. It should be noted that to prevent the denominator from becoming zero, the adjustment parameter takes a value of 0.01 in this embodiment.

[0048] It should be understood that the cooperation factor of the etching matrix reflects the cooperation and heterogeneity effects between the etched data. By analyzing the fluctuation difference between the windows of the i-th row and the j-th row data centered on the data q when the window is increased by u times, the greater the fluctuation difference, the smaller the similarity between the two rows of data from a local perspective at this time, is smaller; then according to the proportion occupied by the window size under its maximum window size as the weight, that is, the more times the window is increased, the larger the window, the greater the weight, because the larger the window contains more local features, giving it a greater weight, multiplying the two and then summing over the window increase times and the window center to obtain the comprehensive local consistency parameter situation between the i-th row data and the j-th row data , the larger its value, the greater the local cooperation between the i-th row and the j-th row data, is larger.

[0049] Meanwhile, combining the absolute value of the difference in the local heterogeneity evaluation parameters between the data in the i-th row and the data in the j-th row, the local heterogeneity evaluation parameters of the two rows of data represent the heterogeneity between the data in the i-th row and the data in the j-th row. The larger the absolute value of the difference, the more different the heterogeneous conditions of the electroplated surface reflected by the data in the i-th row and the data in the j-th row, and the worse the synergy between the two rows. The smaller it is; while the Pearson correlation coefficient represents the linear correlation between the two rows of data, and the larger its value, the larger it is. Generally speaking constrain the local synergy through the overall correlation situation, and further introduce the heterogeneity differences reflected between different rows, so as to represent the comprehensive synergy situation and the degree of their mutual correlation that appear in different data during the etching process, enabling it to analyze the collaborative impact of etching from all data.

[0050] S004: Improve the differential coefficient of the PID control according to the synergy factor of the etching matrix; etch the electroplated thin film surface in combination with the improved differential coefficient of the PID control, and complete the wire bonding of the semiconductor chip through the thin film hybrid integration method.

[0051] Taking the deviation between the actually measured ion beam energy and the rated ion beam energy for etching as the input, use a PID controller to control the ion beam energy emitted by the ion beam source. However, when controlling the ion beam, due to the accuracy requirements of the dry physical ion sputtering method, the subtle material changes on the electroplated thin film surface and the synergy effects between various data can have a greater impact on the etching effect. Improve the differential coefficient in the PID control: , In the formula, is the improved differential coefficient, is the synergy factor of the etching matrix, kd is the differential coefficient of the PID control, is the adjustment factor, which takes the value of 2 in this embodiment, and the implementer can adjust it according to the actual accuracy requirements, is the sigmoid function. Among them, the acquisition process of the improved differential coefficient of the PID control is as Figure 2 shown.

[0052] Substitute the improved differential coefficient into the PID algorithm for use, and perform real-time analysis on the collected data, so that it can calculate a synergy factor in real time during etching to adjust the differential coefficient, enabling it to make real-time feedback according to the ion beam energy oscillation or instability caused by the heterogeneity of the electroplated thin film surface or the synergy effect during electroplating, that is, amplify (or reduce) the feedback of the differential part on the error change rate through the synergy factor, so as to achieve precise control of the ion beam energy.

[0053] The etching of the electroplated thin film surface is completed according to the PID algorithm for the control of the ion beam energy. The etched substrate is mounted on the tube base by means of eutectic welding or paste bonding to connect different components. Finally, by means of thin film hybrid integration, more than one semiconductor chip or other chip components are integrated on the convex ceramic substrate, and the wire bonding of the semiconductor chip is completed to achieve high integration. Thus, the integration of the new ceramic-based thin film circuit product is completed.

[0054] In summary, in the etching step of the ceramic-based thin film circuit preparation in the embodiment of the present invention, the PID control algorithm is improved for the problems of unstable etching and easy occurrence of errors caused by the heterogeneity of the ceramic base and the chain change of the process environment. First, for the high precision of etching, microscopic multi-scale local analysis is carried out; then, for the heterogeneity of the electroplated thin film surface during etching, FFT is used for zonal analysis, and the heterogeneity situation reflected by the data is determined according to the change of its period and the difference in frequency width; finally, window analysis is carried out on the data for the chain change between different data to obtain the final cooperation factor, so that it can calculate the heterogeneity difference reflected by different data while reflecting the chain change caused by the data cooperation effect; then, the PID algorithm is improved according to the result, so that it can make real-time feedback on the oscillation and instability of the ion beam energy caused by the heterogeneity of the electroplated thin film surface and the chain change of parameters in real time according to the cooperation effect and heterogeneity, enhancing the robustness and practicability of the algorithm, improving the regulation accuracy of the ion beam energy, and ensuring the integration quality of the new ceramic-based thin film circuit product.

[0055] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification are described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0057] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; modifying the technical solutions recorded in the foregoing embodiments, or equivalently replacing some of the technical features, does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of each embodiment of the present application, and all should be included in the protection scope of the present application.

Claims

1. An integration method for a new type of ceramic-based thin film circuit product, characterized in that, The method includes the following steps: Obtain etching data of various types and use the etching data of various types as each row of the etching matrix; Adopt detrended fluctuation analysis to obtain the fractal dimension sequence of each row of data in the etching matrix; analyze the element distribution characteristics of the fractal dimension sequence of each row of data, combine with the mutation point detection algorithm, analyze the data trend within the local range of the mutation point data, and obtain the anomaly index of each row of data; obtain the frequency domain signal of each row of data, and determine the stability parameter of each row of data based on the change of the frequency distribution range and amplitude size in the frequency domain signal of each row of data. Combine the anomaly index to obtain the local heterogeneity evaluation parameter of each row of data; Taking each data in each row of the etching matrix as the center, preset the initial length of the window, and uniformly increase it at a preset step size towards both ends. According to the consistency of the data change within the window after the window is increased for each data corresponding to any two rows of data in the etching matrix, determine the local consistency parameter between the any two rows of data; comprehensively determine the distribution similarity degree, local consistency parameter, and local heterogeneity evaluation parameter between different rows of data to determine the cooperation factor of the etching matrix; Improve the differential coefficient of the PID control according to the cooperation factor of the etching matrix; combine the improved differential coefficient of the PID control to etch the surface of the electroplated thin film, and complete the wire bonding of the semiconductor chip through the thin film hybrid integration method.

2. The integration method of a novel ceramic-based thin film circuit product as described in claim 1, characterized in that, The obtaining the fractal dimension sequence of each row of data in the etching matrix by using detrended fluctuation analysis includes: Taking each row of the etching matrix as the input of the multifractal detrended fluctuation analysis, and outputting the fractal dimension curve of each row of data; uniformly sampling the fractal dimension curve to obtain the fractal dimension sequence.

3. The integration method of a new ceramic-based thin-film circuit product as described in claim 1, characterized in that, The obtaining the anomaly index of each row of data is specifically: Preset the local data sequence of each mutation point in the fractal dimension sequence of each row of data; Calculate the local data eigenvalue of each mutation point according to the degree of element change and data increase and decrease trend in the local data sequence of each mutation point; Obtain the dispersion degree of the elements in the fractal dimension sequence of each row of data; calculate the average value of the local data eigenvalues of all mutation points corresponding to each row of data; take the result of the positive fusion of the dispersion degree and the local data eigenvalue average value corresponding to each row of data as the anomaly index of each row of data.

4. The integration method of a novel ceramic-based thin film circuit product according to claim 3, characterized in that, The calculating the local data eigenvalue of each mutation point is specifically: For the fractal dimension sequence of each row of data, fuse the slope of the straight line fitted by the local data sequence of each mutation point with the element average value of the first-order difference sequence of the local data sequence corresponding to the mutation point in the positive direction to obtain the local data eigenvalue of each mutation point in the fractal dimension sequence of each row of data.

5. The integration method of a new ceramic-based thin film circuit product as claimed in claim 1, characterized in that, The determining the stability parameter of each row of data is specifically: Obtain all the peaks and valleys of the frequency domain signal of each row of data; Record the absolute value of the amplitude difference between the peaks and valleys of the same ordinal position as the first difference; record the absolute value of the frequency difference between the peaks and valleys of the same ordinal position as the second difference; take the average value of the ratio between the first difference and the second difference under all ordinal positions as the stability parameter of each row of data in the frequency domain.

6. The integration method of a novel ceramic-based thin film circuit product as claimed in claim 1, characterized in that, The local heterogeneity evaluation parameter of each row of data is specifically the product of the local data eigenvalue and the stability parameter.

7. The integration method of a novel ceramic-based thin film circuit product according to claim 1, characterized in that, Determining the local consistency parameter between any two rows of data includes: Calculating the ratio between the window length increased each time and the maximum window increase length; obtaining the negative correlation mapping result of the difference between the extreme values of the data within the window after each increase of the window centered on each data in any two rows of data; using the ratio as the weight of the corresponding negative correlation mapping result after each increase of any two rows of data, and summing to obtain the local consistency parameter between any two rows of data.

8. The integration method of a novel ceramic-based thin film circuit product as described in claim 1, characterized in that, The specific process of determining the collaborative factor of the etching matrix is as follows: Obtaining the similarity measure between any two rows of data, the difference in the local heterogeneity evaluation parameter, and combining the local consistency parameter to obtain the collaborative index between any two rows of data; Taking the mean of the collaborative indices obtained from all pairwise combinations of row data in the etching matrix as the collaborative factor of the etching matrix.

9. The integration method of a novel ceramic-based thin-film circuit product as claimed in claim 8, wherein, The specific method for obtaining the collaborative index between any two rows of data is as follows: For any two rows of data in the etching matrix, after positively fusing the similarity measure and the local consistency parameter, divide by the absolute value of the difference in the local heterogeneity evaluation parameter between any two rows of data to obtain the collaborative index between any two rows of data.

10. The integration method of a novel ceramic-based thin-film circuit product as described in claim 1, characterized in that, Improving the differential coefficient of PID control according to the collaborative factor of the etching matrix includes: Presetting an adjustment factor; obtaining the normalized value of the collaborative factor of the etching matrix; calculating the product of the adjustment factor, the normalized value, and the differential coefficient of PID control; taking the product as the improved differential coefficient of PID control.

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