A PI-based high heat-resistant flexible copper-clad laminate and its gradient curing process

Abstract

This invention relates to the field of copper clad laminate (CCL) curing technology, specifically to a PI-based high heat-resistant flexible CCL and its gradient curing process. The process steps include: dividing the surface into sub-windows to analyze the variation deviation of infrared data, quantifying the thermal stress accumulation index, and iteratively combining the thermal stress accumulation indices between sub-windows to form sub-temperature control regions; introducing a correlation sensitivity between deformation data and the thermal stress accumulation index to obtain the temperature adjustment range of the sub-temperature control regions. Using cross-correlation to quantify the hysteresis time between thermal stress and deformation, and combining the temperature adjustment range with the direction of adjacent infrared thermal reaction deviations, adjustment temperature data is generated to obtain the curing temperature curve. This invention, by dynamically subdividing the surface temperature control region, analyzes the hysteresis effect of local thermal stress and deformation, controls the temperature data at different local spatial locations, suppresses the accumulation of thermal stress at the interface between the copper foil and the PI substrate, and improves the performance stability and reliability of the high heat-resistant flexible CCL.

Description

Technical Field

[0001] This invention relates to the field of copper clad laminate curing technology, specifically to a PI-based high heat-resistant flexible copper clad laminate and its gradient curing process. Background Technology

[0002] PI-based high-heat-resistant flexible copper-clad laminate is a type of copper-clad laminate with polyimide (PI) as the substrate, possessing excellent high-temperature heat resistance, flexibility, and electrical properties. Traditional copper-clad laminate materials can no longer meet the stability and reliability requirements of modern electronic devices under high-temperature, high-frequency, or high-humidity environments. Therefore, the development of high-heat-resistant and highly flexible copper-clad laminate materials has become particularly important.

[0003] Gradient curing is a novel method proposed to optimize the fabrication process of PI-based copper-clad laminates. In existing curing processes, due to the significant difference in thermal expansion coefficients between the polyimide substrate and the copper foil, a single heating / cooling rate is typically used, lacking precise temperature field control. This results in significant thermal stress in different areas of the product, leading to severe warping and deformation after curing. It is difficult to simultaneously ensure sufficient curing of the high-heat-resistant resin system and thermal protection of the polyimide substrate, thus affecting the yield of subsequent products. Summary of the Invention

[0004] To address the aforementioned technical problems in the prior art, the present invention aims to provide a PI-based high heat-resistant flexible copper-clad laminate and its gradient curing process. The specific technical solution adopted is as follows:

[0005] This invention provides a gradient curing process for PI-based high heat-resistant flexible copper-clad laminates, the process comprising:

[0006] Infrared and deformation data of the surface are acquired at each detection moment during the copper clad laminate curing process; the surface of the copper clad laminate is divided into sub-windows;

[0007] Based on the degree of deviation between the infrared data of each sub-window and the local sub-window, the thermal stress accumulation index of each sub-window at the detection time is obtained; based on the positional distribution between sub-windows and the magnitude of the thermal stress accumulation index, they are combined into sub-temperature control regions.

[0008] By correlating the deformation data of each sub-temperature control zone at the current detection moment with the changes in the overall thermal stress accumulation index, the temperature adjustment range of each sub-temperature control zone at the current detection moment can be obtained.

[0009] Based on the temporal cross-correlation between the overall thermal stress accumulation index and deformation data in each sub-temperature control area of ​​the preset time analysis window, the deformation lag time is determined.

[0010] Based on the direction of the infrared data deviation between each sub-temperature control zone and its adjacent sub-temperature control zone, the temperature adjustment amplitude is adjusted on the original temperature data of the deformation lag time to obtain the adjusted temperature data of the sub-temperature control zone; based on the adjusted temperature data of all sub-temperature control zones, the curing temperature curve of the copper-clad laminate is obtained.

[0011] Furthermore, the method for obtaining the thermal stress accumulation index includes:

[0012] For any sub-window, the average pixel value of all infrared data in the sub-window at the current detection time is taken as the thermal stress value of the sub-window; after calculating the difference in thermal stress value between the sub-window and each other sub-window within the preset local range, the average of all thermal stress value differences is taken as the local thermal conduction resistance coefficient of the sub-window at the current detection time.

[0013] Calculate the rate of change of the local heat conduction resistance coefficient of the sub-window between the previous detection time and the current detection time, and obtain the resistance change coefficient of the sub-window at the current detection time;

[0014] By combining the local thermal conduction resistance coefficient and resistance change coefficient of the sub-window at the current detection time, the thermal stress accumulation index of the sub-window at the current detection time can be obtained.

[0015] Furthermore, the method for obtaining the sub-temperature control area includes:

[0016] When the difference in thermal stress accumulation index between each sub-window and its adjacent sub-window is less than a preset deviation threshold, the sub-windows are combined into a sub-region until they can no longer be combined.

[0017] After increasing the deviation threshold by a preset size, the remaining uncombined sub-windows are iteratively combined until all sub-windows are combined into sub-regions, and each sub-region is used as a sub-temperature control region.

[0018] Furthermore, the method for obtaining the temperature adjustment range includes:

[0019] For any sub-temperature control zone, the average value of the thermal stress accumulation index of all sub-windows contained in the sub-temperature control zone is used as the regional accumulation index of the sub-temperature control zone.

[0020] The difference in deformation data within the sub-temperature control area between the current detection time and the previous detection time is taken as the deformation time difference value of the sub-temperature control area; the difference in the regional cumulative index of the sub-temperature control area between the current detection time and the previous detection time is taken as the thermal stress time difference value of the sub-temperature control area.

[0021] The ratio of the deformation time difference to the thermal stress time difference in the sub-temperature control region is used as the deformation sensitivity coefficient of the sub-temperature control region.

[0022] The product of the deformation sensitivity coefficient of the sub-temperature control region after negative correlation mapping and the region accumulation index is normalized and used as the temperature adjustment range of the sub-temperature control region.

[0023] Furthermore, the method for obtaining the deformation hysteresis time includes:

[0024] For any sub-temperature control zone, the average value of the thermal stress accumulation index of all sub-windows contained in the sub-temperature control zone is used as the regional accumulation index of the sub-temperature control zone.

[0025] Within the preset time analysis window of the current detection time, a cross-correlation function is constructed using the mean deviation of deformation data at each detection time and the mean deviation of cumulative index in the region before the time difference at the detection time, thus obtaining the thermal stress-deformation cross-correlation function; the time difference is the independent variable.

[0026] The time difference at the maximum value of the thermal stress-deformation cross-correlation function is taken as the deformation lag time.

[0027] Furthermore, the method for obtaining the thermal stress-deformation cross-correlation function includes:

[0028] In the preset time analysis window, the difference between the deformation data at each detection time and the mean value of all deformation data in the preset time analysis window is taken as the deformation deviation at each detection time.

[0029] The window before the time difference in the preset time analysis window is used as the stress analysis window. The difference between the cumulative index of the region before the time difference at each detection time and the mean of the cumulative index of all regions in the stress analysis window is used as the preceding stress deviation at each detection time.

[0030] In the preset time analysis window, the sum of the product of the deformation deviation and the preceding stress deviation at the detection time is used as the numerator, and the standard deviation of all deformation data in the preset time analysis window and the standard deviation of the cumulative index of all regions in the stress analysis window are used as the denominator to obtain the thermal stress-deformation cross-correlation function.

[0031] Furthermore, the method for acquiring the adjusted temperature data includes:

[0032] The average pixel value of the infrared data in each sub-temperature control area is used as the regional heat value of each sub-temperature control area. For any sub-temperature control area, the difference between the regional heat value of the sub-temperature control area and each of the adjacent sub-temperature control areas is calculated, and the sum of all the differences is used as the neighborhood change value of the sub-temperature control area. The original temperature data of the sub-temperature control area at the current time after the deformation lag time is used as the temperature to be adjusted.

[0033] When the neighborhood change value is positive, the product of the temperature to be adjusted and the temperature adjustment range is used as the cooling adjustment value; the difference between the temperature to be adjusted and the cooling adjustment value is used as the adjustment temperature data at the current detection time.

[0034] When the neighborhood change value is negative, the product of the temperature to be adjusted and the temperature adjustment range is used as the temperature rise adjustment value, and the sum of the temperature to be adjusted and the temperature drop adjustment value is used as the temperature adjustment data at the current detection time.

[0035] No temperature data adjustment is performed when the neighborhood change value is zero.

[0036] Furthermore, obtaining the curing temperature profile of the copper-clad laminate based on the adjusted temperature data of all sub-temperature control zones includes:

[0037] Using the adjusted temperature data of each sub-temperature control zone as the target temperature, the curing heating system issues temperature adjustment commands to each sub-temperature control zone, outputting the curing temperature curve of the entire surface of the copper-clad laminate.

[0038] Furthermore, the method for obtaining the child window includes:

[0039] Using a preset size as the side length of the dividing window, slide the dividing window from left to right and from top to bottom on the copper-clad board surface with a preset step size to obtain each sub-window.

[0040] The present invention also provides a PI-based high heat-resistant flexible copper-clad laminate, including a curing heating system. The curing heating system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the gradient curing process of the PI-based high heat-resistant flexible copper-clad laminate described above.

[0041] The present invention has the following beneficial effects:

[0042] This invention constructs a thermal stress accumulation index by dividing the data into sub-windows and using variations in infrared data to reflect the degree and changes in thermal conduction resistance, thus more accurately characterizing the accumulation of local strain energy. The thermal stress accumulation index between sub-windows is iteratively subdivided and combined to form sub-temperature control regions. The correlation sensitivity between deformation data and the thermal stress accumulation index is introduced to adjust the thermal stress accumulation index in the sub-temperature control regions, increasing the degree of temperature regulation required in the thermal stress region. This minimizes the impact of adjustment scale on deformation, making subsequent adjustments more appropriate. Furthermore, cross-correlation is used to quantify the lag time between thermal stress and deformation, enabling compensation and adjustment analysis of thermal stress and deformation lag. Based on the lag time and temperature adjustment amplitude, temperature data is adjusted in the direction of adjacent infrared thermal response deviation, generating regulated temperature data for independent dynamic temperature adjustment of each sub-temperature control region. The resulting curing temperature curve for the full-surface gradient curing of the copper-clad laminate is then output. This invention analyzes the hysteretic effects of local thermal stress and deformation by dynamically subdividing the surface temperature control area, and regulates the temperature data at different local spatial locations to suppress the accumulation of thermal stress at the interface between the copper foil and the PI substrate, thereby improving the performance stability and reliability of the high heat-resistant flexible copper clad laminate. Attached Figure Description

[0043] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating the steps of a gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate, as provided in an embodiment of the present invention.

[0045] Figure 2 This is a partial schematic diagram of infrared data provided in one embodiment of the present invention. Detailed Implementation

[0046] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a PI-based high-heat-resistant flexible copper-clad laminate and its gradient curing process according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] The following description, in conjunction with the accompanying drawings, details the specific scheme of a PI-based high heat-resistant flexible copper-clad laminate and its gradient curing process provided by the present invention.

[0049] Please see Figure 1 The diagram illustrates a step-by-step flow chart of a gradient curing process for a PI-based high-heat-resistant flexible copper-clad laminate according to an embodiment of the present invention. The method includes the following steps:

[0050] S1: Acquire infrared and deformation data of the surface at each detection moment during the copper clad laminate curing process; divide the copper clad laminate surface into sub-windows.

[0051] In this embodiment of the invention, during the copper-clad laminate curing process, an infrared thermal imaging and high-precision thermocouple sensor array is installed inside the curing equipment, i.e., a high-precision curing oven. The sensor deployment requires a spatial resolution ≤1mm and a thermal sensitivity ≤0.05℃ to record spatial temperature distribution data in real time, simultaneously acquiring time-temperature curves within the curing equipment, as well as infrared data images of the surface. The sampling frequency is not less than 1 Hz to ensure accurate capture of temperature gradient changes. Please refer to [link to relevant documentation]. Figure 2 This illustration shows a partial schematic diagram of infrared data provided in an embodiment of the present invention.

[0052] In the copper clad laminate curing process, temperature control is usually achieved by using a zoned temperature control method. Due to factors such as the thickness, shape, and thermal conductivity of the material, the heat distribution in the central area, transition area, and edge area will be different. Therefore, under normal circumstances, the surface of the copper clad laminate is initially divided into a central area, a transition area, and an edge area, and temperature control is performed separately for each area.

[0053] Meanwhile, a non-contact laser displacement sensor installed on the copper-clad board is used to detect the Z-axis warping displacement and in-plane strain distribution throughout the curing process in real time, i.e. the deformation of the board in the XY plane. The displacement measurement accuracy is controlled within ±0.1μm, and the sum of the Z-axis warping displacement and the deformation in the XY plane is used as the deformation data.

[0054] The frequency of deformation data acquisition is kept consistent with that of infrared data acquisition, obtaining infrared and deformation data at each detection moment. This establishes a time synchronization controller to align the acquisition timestamps of infrared and deformation data. A missing data compensation algorithm based on cubic spline interpolation is employed to reconstruct a complete dataset using the gradient features of adjacent regions. The acquired data is then processed, stored, managed, and analyzed. It should be noted that the equipment deployment for dynamic monitoring during the solidification process is a well-known technique in the field, and implementers can adjust it according to the specific implementation scenario; no restrictions are imposed here.

[0055] During the curing of copper-clad laminates, the difference in thermal expansion between the copper foil and the PI substrate generates shear stress at the interface, resulting in insufficient bonding between the copper foil and the PI substrate. Uneven temperature distribution at the interface exacerbates stress concentration, further increasing the risk of material deformation and damage. Therefore, the initial temperature control area cannot meet the requirements for the analysis of the thermal state of micro-areas.

[0056] Therefore, in this embodiment of the invention, for the image data of the copper-clad laminate surface, i.e., infrared data, during each detection period, a preset size is used as the side length of the dividing window. With the upper left corner of the image as the origin, the dividing window is slid from left to right and from top to bottom on the copper-clad laminate surface with a preset step size to obtain multiple overlapping sub-windows. The preset size is 3, i.e., the dividing window size is 3×3, and the step size is 1. The specific values ​​can be adjusted by the implementer and are not limited here.

[0057] This achieves dynamic subdivision of the temperature control zone during the curing process.

[0058] S2: Based on the degree of deviation between the infrared data of each sub-window and the local sub-window, obtain the thermal stress accumulation index of each sub-window at the detection time; and combine them into sub-temperature control areas according to the positional distribution between sub-windows and the magnitude of the thermal stress accumulation index.

[0059] The thermally induced warpage of copper-clad laminates during the curing process is essentially thermal stress at the interface between different materials. Thermal stress is essentially the accumulation of elastic strain energy caused by uneven distribution of interfacial thermal energy. Therefore, the deviation of infrared data changes between each sub-window and local sub-windows reflects the resistance to heat transfer inside the material during the curing process. The greater the deviation, the more severe the accumulation of heat exchange between the micro-area and the surrounding area, which may lead to material defects or interfacial thermal resistance, resulting in increased local thermal stress.

[0060] In this embodiment of the invention, the method for obtaining the thermal stress accumulation index includes:

[0061] For any given sub-window, the average pixel value of all infrared data within that sub-window at the current detection time is taken as the thermal stress value of that sub-window, representing the temperature of the micro-region at the current detection time. After calculating the difference in thermal stress values ​​between this sub-window and each other sub-window within a preset local range, the average of all thermal stress value differences is taken as the local thermal conduction resistance coefficient of that sub-window at the current detection time.

[0062] In this embodiment of the invention, the preset local range is set to the range of the eight neighboring sub-windows. That is, the difference in thermal stress values ​​is calculated and the average value is obtained within the range of the eight adjacent sub-windows around the sub-window. The local thermal conduction resistance coefficient reflects the heat exchange capacity between the micro-region and the surrounding area. The larger the local thermal conduction resistance coefficient, the more significant the situation that the current heat energy cannot diffuse in time.

[0063] Furthermore, the rate of change of the local thermal conduction resistance coefficient of the sub-window between the previous detection time and the current detection time is calculated to obtain the resistance change coefficient of the sub-window at the current detection time. In this embodiment of the invention, the difference between the local thermal conduction resistance coefficient at the current detection time and the local thermal conduction resistance coefficient at the previous detection time is used as the numerator, and the time difference between the previous detection time and the current detection time is used as the denominator to obtain the resistance change coefficient of the sub-window at the current detection time, which reflects the trend of change of thermal conduction resistance of the micro-area material over time. The larger the resistance change coefficient, the higher the risk of local thermal runaway, such as the exothermic effect of excessive resin cross-linking or the enhanced exothermic effect of thermal reaction.

[0064] Finally, by combining the local thermal conduction resistance coefficient and resistance change coefficient of the sub-window at the current detection time, the thermal stress accumulation index of the sub-window at the current detection time is obtained. In this embodiment of the invention, the product of the local thermal conduction resistance coefficient and resistance change coefficient of the sub-window at the current detection time is used as the thermal stress accumulation index of the sub-window at the current detection time. When the local thermal conduction resistance coefficient and resistance change coefficient are larger, it indicates that the heat accumulation in the micro-region is more accelerated and the accumulation degree is higher. The poor heat energy diffusion effect leads to the conversion into mechanical stress (thermal stress), which is very likely to cause material warping or delamination.

[0065] Therefore, the thermal stress accumulation index can be used to measure the potential for warping origins within a micro-region, requiring focused intervention in subsequent temperature control. The thermal stress accumulation index also allows for more detailed division of regional temperature control areas, and the combination of micro-regions can determine sub-temperature control areas for dynamic temperature field adjustment within those areas.

[0066] In this embodiment of the invention, the method for obtaining the sub-temperature control region includes:

[0067] When the difference in thermal stress accumulation index between each sub-window and its adjacent sub-window is less than a preset deviation threshold, the sub-windows are combined into a sub-region until they cannot be combined again. The smaller the difference in thermal stress accumulation index between sub-windows, the closer the thermal stress state of the micro-regions, making them suitable for control using the same temperature, and therefore they are merged. In this embodiment of the invention, the preset deviation threshold is set to 0.3, but the implementer can adjust the specific value as needed.

[0068] When combination is not possible, the threshold is increased again to ensure that all micro-regions are combined to obtain several sub-temperature control areas. After increasing the deviation threshold by a preset size, the remaining uncombined sub-windows are iteratively combined until all sub-windows are combined into sub-regions. Each sub-region is used as a sub-temperature control area. In this embodiment of the invention, the preset size can be set to 0.1, and the specific value is not limited here.

[0069] S3: By correlating the deformation data of each sub-temperature control area at the current detection time with the changes in the overall thermal stress accumulation index, the temperature adjustment range of each sub-temperature control area at the current detection time can be obtained.

[0070] In sub-temperature control zones, when there is high thermal stress accumulation, a larger temperature adjustment range is usually required to reduce thermal stress accumulation and avoid deformation of the material in that area. The goal of temperature adjustment is to counteract stress and prevent warping, but temperature adjustment itself may induce new deformations, such as material shrinkage due to excessively rapid cooling. Furthermore, the sensitivity of the unit thermal stress change to regional deformation differs between the central and edge areas of the copper-clad laminate during temperature adjustment. Therefore, it is necessary to analyze the sensitivity of each sub-temperature control zone to the impact of thermal accumulation on deformation and correct the adjustment range to ensure the stability of the material in each zone during adjustment.

[0071] Preferably, in this embodiment of the invention, the method for obtaining the temperature adjustment range includes:

[0072] For any given sub-temperature control region, the average value of the thermal stress accumulation index of all sub-windows contained within that sub-temperature control region is used as the regional accumulation index for that sub-temperature control region. The regional accumulation index reflects the average stress accumulation in the sub-temperature control region. Different sub-temperature control regions have different degrees of thermal stress accumulation, causing their thermal stress to produce different degrees of instantaneous deformation when the temperature changes.

[0073] Furthermore, the difference in deformation data within the sub-temperature control area between the current detection time and the previous detection time is used as the deformation time difference value of the sub-temperature control area, and the difference in the regional cumulative index of the sub-temperature control area between the current detection time and the previous detection time is used as the thermal stress time difference value of the sub-temperature control area. It can be understood that when there is a deformation time difference value or the thermal stress time difference value is zero, it indicates that the correlation sensitivity between instantaneous deformation and stress is not high, the correction requirement is low, and the deformation sensitivity coefficient can be directly set to 1 without adjustment range correction.

[0074] Otherwise, the ratio of the deformation time difference to the thermal stress time difference of the sub-temperature control region is used as the deformation sensitivity coefficient of the sub-temperature control region, which reflects the scale of change caused by a unit change in thermal stress in the corresponding sub-temperature control region. The larger the deformation sensitivity coefficient, the greater the impact of a unit change in thermal stress in the current sub-temperature control region on the regional deformation. Since the essence of the change in thermal stress in the sub-temperature control region is the change in regional temperature, the adjustment range needs to be reduced.

[0075] Finally, the product of the deformation sensitivity coefficient of the sub-temperature control region after negative correlation mapping and the region accumulation index is normalized and used as the temperature adjustment range of the sub-temperature control region. The temperature adjustment range reflects the temperature correction scale required for the sub-temperature control region. The larger the temperature adjustment range, the higher the stress and the lower the sensitivity of the sub-temperature control region, requiring a larger temperature adjustment. The smaller the temperature adjustment range, the higher the stress and the higher the sensitivity of the sub-temperature control region, requiring a smaller temperature adjustment to avoid excessive deformation.

[0076] S4: Determine the deformation lag time based on the cross-correlation between the overall thermal stress accumulation index and deformation data in each sub-temperature control area on the preset time analysis window.

[0077] In actual gradient curing processes, the warping and other degradation phenomena of copper-clad laminates are mainly caused by temperature-driven stress accumulation. Due to the viscoelastic properties of copper-clad laminate materials, there is a significant time lag effect between the accumulation of thermal stress and macroscopic deformation. That is, the generation of thermal stress usually precedes the occurrence of macroscopic deformation, and there is a certain time delay between the two.

[0078] If temperature control is directly implemented based on the adjustment range, premature or delayed intervention will occur due to the lack of compensation for hysteresis effects. This could result in premature cooling before thermal stress has fully converted, inhibiting normal resin cross-linking, or adjustment only after deformation has occurred, leading to irreversible damage. Therefore, it is necessary to analyze the optimal time delay between pressure and deformation to provide a time basis for subsequent advance temperature adjustment. By analyzing the cross-correlation between thermal stress accumulation and deformation changes over time, the time delay under the most similar matching condition can be determined.

[0079] Preferably, in this embodiment of the invention, the method for obtaining the deformation hysteresis time includes:

[0080] For any sub-temperature control zone, within the preset time analysis window of the current detection time, a cross-correlation function is constructed using the mean deviation of deformation data at each detection time and the mean deviation of the cumulative index of the region before the time difference at the detection time. This yields the thermal stress-deformation cross-correlation function, where the time difference is the independent variable. By using the cross-correlation function, the most correlated relationship between accumulated thermal stress and the change in lag deformation data is found. The lag time determined at this point can reflect the actual lag relationship between the two to the greatest extent possible.

[0081] In one specific embodiment of the present invention, within a preset time analysis window, the difference between the deformation data at each detection moment and the mean of all deformation data within the preset time analysis window is taken as the deformation deviation at each detection moment. The window before the time difference in the preset time analysis window is taken as the stress analysis window. By shifting the time difference forward by an overall time window, the deformation caused by the hysteresis effect is analyzed. The difference between the cumulative index of the region before the time difference at each detection moment and the mean of the cumulative index of all regions within the stress analysis window is taken as the preceding stress deviation at each detection moment.

[0082] Considering the matching of data change trends, the interference of overall data offset is eliminated by the mean deviation, and only the change in value relative to the average value at each moment is retained for change correlation analysis. In this embodiment of the invention, the preset time analysis window size is set to the range of 10 seconds before the detection time. The specific value can be adjusted by the implementer according to the specific implementation scenario, and is not limited here.

[0083] In the preset time analysis window, the sum of the product of the deformation deviation and the preceding stress deviation at the detection moment is used as the numerator. This sum is combined with the standard deviations of all deformation data in the preset time analysis window and the standard deviations of the cumulative indices for all regions in the stress analysis window, forming the denominator. This yields the thermal stress-deformation cross-correlation function. The denominator avoids the influence of orders of magnitude by multiplying the standard deviations of the deformation data with the standard deviations of the regional cumulative indices. As an example, the expression for the thermal stress-deformation cross-correlation function is:

[0084] In the formula, This is expressed as the time difference, with the time difference being the independent variable. This represents the total number of detection times in the preset time analysis window. Represented as the first in the preset time analysis window Each detection moment, Represented as the first Deformation data at each detection time. This represents the mean value of deformation data in the preset time analysis window. Represented as the first Each detection moment is in time difference The cumulative regional index at the previous detection time, This is represented as the mean of the cumulative indices within the stress analysis window. This is expressed as the standard deviation of the cumulative index across all regions in the stress analysis window. This is represented as the standard deviation of all deformation data in the preset time analysis window. The preset adjustment factor is set to 0.001 in this embodiment of the invention to prevent the formula from being meaningless when the denominator is zero.

[0085] The numerator, summed by the product of deviations, reflects the simultaneity of the deformation trend with the thermal stress trend τ seconds prior. The denominator is used for standardization of magnitude, eliminating magnitude differences and comparing only the trend synchronicity. By varying the time difference, the cross-correlation function can calculate a representative value from samples at multiple time points, avoiding bias at a single time point. The maximum value corresponds to the point where the two data sequences reach their strongest correlation at this time shift. This time difference is used as the deformation lag time, representing the time delay between thermal stress accumulation and macroscopic deformation in the corresponding temperature-controlled region.

[0086] S5: Based on the direction of the infrared data deviation between each sub-temperature control area and the adjacent sub-temperature control area, the temperature adjustment amplitude is adjusted on the original temperature data of the deformation lag time to obtain the adjustment temperature data of the sub-temperature control area; based on the adjustment temperature data of all sub-temperature control areas, the curing temperature curve of the copper-clad laminate is obtained.

[0087] The adjustment direction is determined by the temperature deviation between the sub-temperature control area and the adjacent sub-temperature control area. Temperature is then adjusted in the adjustment direction based on the analyzed temperature adjustment range and lag time. The temperature is adjusted in advance through the lag compensation mechanism to avoid control failure due to viscoelastic lag. At the same time, the local thermal stress state is accurately matched through the coupling of direction and intensity.

[0088] Preferably, in this embodiment of the invention, the method for obtaining temperature data includes:

[0089] First, the average pixel value of the infrared data in each sub-temperature control area is used as the regional heat value of each sub-temperature control area, reflecting the heat distribution of the area. For any sub-temperature control area, the difference between the regional heat value of this sub-temperature control area and each of its adjacent sub-temperature control areas is calculated. The sum of all differences is used as the neighborhood variation value of this sub-temperature control area. When the neighborhood variation value is positive, it indicates that the thermal stress in this sub-temperature control area comes from locally excessively high temperature, and the curing temperature needs to be reduced. Conversely, if the neighborhood variation is negative, it indicates that the thermal stress is caused by locally excessively low temperature, and the curing temperature needs to be increased.

[0090] The original temperature data of the current temperature control area at the time after the deformation lag time is used as the temperature to be adjusted. The original temperature data is obtained from the time-temperature data of the initial temperature control area and is used as the basic curing temperature data.

[0091] Furthermore, when the neighborhood change value is positive, the product of the temperature to be adjusted and the temperature adjustment range is used as the cooling adjustment value for reduction adjustment. The difference between the temperature to be adjusted and the cooling adjustment value is used as the adjustment temperature data at the current detection time. By controlling the temperature layout in advance, the occurrence of hysteresis deformation is reduced.

[0092] When the neighborhood change value is negative, the product of the temperature to be adjusted and the temperature adjustment range is used as the temperature increase adjustment value for adjustment. The sum of the temperature to be adjusted and the temperature decrease adjustment value is used as the adjusted temperature data at the current detection time. In particular, when the neighborhood change value is zero, it indicates that the current region is in a dynamic equilibrium state, and the current equilibrium state will not be further disrupted, so no temperature data adjustment is performed.

[0093] In the gradient curing process of PI-based high heat-resistant flexible copper clad laminate, the high-precision curing oven uses multiple heaters, such as electric heating tubes and hot oil circulation, to transfer heat to different areas of the copper clad laminate. Each heater works independently according to the set temperature range. In this embodiment of the invention, based on the current adjustment, the adjusted temperature data of each sub-temperature control area is used as the target temperature. The multi-curing heating system of the curing oven independently executes the temperature commands of each sub-temperature control area. The dynamic adjustment temperature commands of all sub-temperature control areas are integrated according to spatial position, and the output is the optimal curing temperature curve that covers the entire surface of the copper clad laminate for gradient curing.

[0094] In this embodiment of the invention, closed-loop optimization of the process is achieved through dynamic temperature control. The curing temperature curve is input into a high-precision curing oven control system, and the temperature of each sub-temperature control zone is independently controlled by the curing heating system. Key tests are performed on the cured copper-clad laminate, including warpage detection, interfacial adhesion testing, and thermal stability verification. If any test fails to meet the standard, the temperature data is re-analyzed, and the temperature control amplitude is locally adjusted based on the curing temperature curve until the test meets the standard. This ensures that every step of the curing process is precisely controlled, ultimately achieving the optimal copper-clad laminate curing effect.

[0095] In summary, this invention constructs a thermal stress accumulation index by dividing the data into sub-windows and reflecting the degree and changes in thermal conduction resistance through variations in infrared data. This index more accurately characterizes the accumulation of local strain energy. The thermal stress accumulation index between sub-windows is iteratively subdivided and combined to form sub-temperature control regions. The correlation sensitivity between deformation data and the thermal stress accumulation index is introduced to adjust the thermal stress accumulation index in the sub-temperature control regions, increasing the degree of temperature regulation required in the thermal stress region. This minimizes the impact of adjustment scale on deformation, making subsequent adjustments more suitable. Furthermore, cross-correlation is used to quantify the lag time between thermal stress and deformation, enabling compensation and adjustment analysis of thermal stress and deformation lag. Based on the lag time and temperature adjustment amplitude, temperature data is adjusted in the direction of adjacent infrared thermal response deviation, generating regulated temperature data for independent dynamic temperature adjustment of each sub-temperature control region. This outputs a curing temperature curve for the full-surface gradient curing of the copper-clad laminate. This invention analyzes the hysteretic effects of local thermal stress and deformation by dynamically subdividing the surface temperature control area, and regulates the temperature data at different local spatial locations to suppress the accumulation of thermal stress at the interface between the copper foil and the PI substrate, thereby improving the performance stability and reliability of the high heat-resistant flexible copper clad laminate.

[0096] The present invention also provides a PI-based high heat-resistant flexible copper-clad laminate, including a curing heating system. The curing heating system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the gradient curing process of the PI-based high heat-resistant flexible copper-clad laminate described above.

[0097] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A gradient curing process for PI-based high heat-resistant flexible copper-clad laminates, characterized in that, The process includes: Infrared and deformation data of the surface are acquired at each detection moment during the copper clad laminate curing process; the surface of the copper clad laminate is divided into sub-windows; Based on the degree of deviation between the infrared data of each sub-window and the local sub-window, the thermal stress accumulation index of each sub-window at the detection time is obtained; based on the positional distribution between sub-windows and the magnitude of the thermal stress accumulation index, they are combined into sub-temperature control regions. By correlating the deformation data of each sub-temperature control zone at the current detection moment with the changes in the overall thermal stress accumulation index, the temperature adjustment range of each sub-temperature control zone at the current detection moment can be obtained. Based on the temporal cross-correlation between the overall thermal stress accumulation index and deformation data in each sub-temperature control area of ​​the preset time analysis window, the deformation lag time is determined. Based on the direction of the infrared data deviation between each sub-temperature control zone and its adjacent sub-temperature control zone, the temperature adjustment amplitude is adjusted on the original temperature data of the deformation lag time to obtain the adjusted temperature data of the sub-temperature control zone; based on the adjusted temperature data of all sub-temperature control zones, the curing temperature curve of the copper-clad laminate is obtained.

2. The gradient curing process for a PI-based high-heat-resistant flexible copper-clad laminate according to claim 1, characterized in that, The method for obtaining the thermal stress accumulation index includes: For any sub-window, the average pixel value of all infrared data in the sub-window at the current detection time is taken as the thermal stress value of the sub-window; after calculating the difference in thermal stress value between the sub-window and each other sub-window within the preset local range, the average of all thermal stress value differences is taken as the local thermal conduction resistance coefficient of the sub-window at the current detection time. Calculate the rate of change of the local heat conduction resistance coefficient of the sub-window between the previous detection time and the current detection time, and obtain the resistance change coefficient of the sub-window at the current detection time; By combining the local thermal conduction resistance coefficient and resistance change coefficient of the sub-window at the current detection time, the thermal stress accumulation index of the sub-window at the current detection time can be obtained.

3. The gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that, The method for obtaining the sub-temperature control area includes: When the difference in thermal stress accumulation index between each sub-window and its adjacent sub-window is less than a preset deviation threshold, the sub-windows are combined into a sub-region until they can no longer be combined. After increasing the deviation threshold by a preset size, the remaining uncombined sub-windows are iteratively combined until all sub-windows are combined into sub-regions, and each sub-region is used as a sub-temperature control region.

4. The gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that, The method for obtaining the temperature adjustment range includes: For any sub-temperature control zone, the average value of the thermal stress accumulation index of all sub-windows contained in the sub-temperature control zone is used as the regional accumulation index of the sub-temperature control zone. The difference in deformation data within the sub-temperature control area between the current detection time and the previous detection time is taken as the deformation time difference value of the sub-temperature control area; the difference in the regional cumulative index of the sub-temperature control area between the current detection time and the previous detection time is taken as the thermal stress time difference value of the sub-temperature control area. The ratio of the deformation time difference to the thermal stress time difference in the sub-temperature control region is used as the deformation sensitivity coefficient of the sub-temperature control region. The product of the deformation sensitivity coefficient of the sub-temperature control region after negative correlation mapping and the region accumulation index is normalized and used as the temperature adjustment range of the sub-temperature control region.

5. The gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that, The method for obtaining the deformation hysteresis time includes: For any sub-temperature control zone, the average value of the thermal stress accumulation index of all sub-windows contained in the sub-temperature control zone is used as the regional accumulation index of the sub-temperature control zone. Within the preset time analysis window of the current detection time, a cross-correlation function is constructed using the mean deviation of deformation data at each detection time and the mean deviation of cumulative index in the region before the time difference at the detection time, thus obtaining the thermal stress-deformation cross-correlation function; the time difference is the independent variable. The time difference at the maximum value of the thermal stress-deformation cross-correlation function is taken as the deformation lag time.

6. The gradient curing process for a PI-based high-heat-resistant flexible copper-clad laminate according to claim 5, characterized in that, The method for obtaining the thermal stress-deformation cross-correlation function includes: In the preset time analysis window, the difference between the deformation data at each detection time and the mean value of all deformation data in the preset time analysis window is taken as the deformation deviation at each detection time. The window before the time difference in the preset time analysis window is used as the stress analysis window. The difference between the cumulative index of the region before the time difference at each detection time and the mean of the cumulative index of all regions in the stress analysis window is used as the preceding stress deviation at each detection time. In the preset time analysis window, the sum of the product of the deformation deviation and the preceding stress deviation at the detection time is used as the numerator, and the standard deviation of all deformation data in the preset time analysis window and the standard deviation of the cumulative index of all regions in the stress analysis window are used as the denominator to obtain the thermal stress-deformation cross-correlation function.

7. The gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that, The method for obtaining the temperature adjustment data includes: The average pixel value of the infrared data in each sub-temperature control area is used as the regional heat value of each sub-temperature control area. For any sub-temperature control area, the difference between the regional heat value of the sub-temperature control area and each of the adjacent sub-temperature control areas is calculated, and the sum of all the differences is used as the neighborhood change value of the sub-temperature control area. The original temperature data of the sub-temperature control area at the current time after the deformation lag time is used as the temperature to be adjusted. When the neighborhood change value is positive, the product of the temperature to be adjusted and the temperature adjustment range is used as the cooling adjustment value; the difference between the temperature to be adjusted and the cooling adjustment value is used as the adjustment temperature data at the current detection time. When the neighborhood change value is negative, the product of the temperature to be adjusted and the temperature adjustment range is used as the temperature rise adjustment value, and the sum of the temperature to be adjusted and the temperature drop adjustment value is used as the temperature adjustment data at the current detection time. No temperature data adjustment is performed when the neighborhood change value is zero.

8. The gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that, The process of obtaining the curing temperature profile of the copper-clad laminate based on the temperature adjustment data of all sub-temperature control zones includes: Using the adjusted temperature data of each sub-temperature control zone as the target temperature, the curing heating system issues temperature adjustment commands to each sub-temperature control zone, outputting the curing temperature curve of the entire surface of the copper-clad laminate.

9. The gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that, The methods for obtaining the child window include: Using a preset size as the side length of the dividing window, slide the dividing window from left to right and from top to bottom on the copper-clad board surface with a preset step size to obtain each sub-window.

10. A PI-based high heat-resistant flexible copper-clad laminate, characterized in that, The invention includes a curing heating system, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate as described in any one of claims 1 to 9.

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