PI-based high-heat-resistance flexible copper-clad plate and gradient curing process thereof

Abstract

The invention relates to the technical field of copper-clad plate curing processes, in particular to a PI-based high-heat-resistance flexible copper-clad plate and a gradient curing process thereof. The process comprises the following steps: dividing sub-windows to analyze change deviation of infrared data, quantifying thermal stress accumulation indexes, and combining thermal stress accumulation indexes among the sub-windows to form sub temperature control areas through iterative combination; and the change correlation sensitivity between the deformation data and the thermal stress accumulation index is introduced to obtain the temperature adjustment amplitude of the sub temperature control area. And the lag time between thermal stress and deformation is quantified through cross-correlation, adjustment is carried out in combination with the temperature adjustment amplitude and the adjacent infrared thermal reaction deviation direction, adjustment temperature data is generated, and a curing temperature curve is obtained. By dynamically subdividing the surface temperature control area, analyzing the hysteresis influence of local thermal stress and deformation, regulating and controlling the temperature data of different local spatial positions, inhibiting thermal stress accumulation on the interface of the copper foil and the PI base material, and improving the performance stability and reliability of the high-heat-resistance flexible copper-clad plate.

Description

Technical Field

[0001] The present invention relates to the technical field of copper clad laminate curing processes, and in particular to a PI-based highly heat-resistant flexible copper clad laminate and a gradient curing process thereof. Background Art

[0002] PI-based high-heat-resistant flexible copper-clad laminates are copper-clad laminates with a polyimide (PI) substrate, offering excellent high-temperature heat resistance, flexibility, and electrical properties. The stability and reliability of traditional copper-clad laminates in high-temperature, high-frequency, and high-humidity environments no longer meet the demands of modern electronic devices. Therefore, the development of highly heat-resistant and flexible copper-clad laminates is becoming increasingly important.

[0003] The gradient curing process is a new approach proposed to optimize the preparation of PI-based copper-clad laminates. Due to the significant difference in thermal expansion coefficients between the polyimide substrate and the copper foil, existing curing processes typically employ a single heating / cooling rate. This lacks precise control of the temperature field, leading to significant thermal stress within different regions of the product and severe warping after curing. This makes it difficult to achieve both sufficient curing of the high-heat-resistant resin system and thermal protection of the polyimide substrate, impacting subsequent product yields. Summary of the Invention

[0004] In order to solve the above technical problems in the prior art, the purpose of the present invention is to provide a PI-based high heat-resistant flexible copper clad laminate and its gradient curing process, and the technical solutions adopted are as follows: The present invention provides a gradient curing process for a PI-based high-heat-resistant flexible copper-clad laminate, the process comprising: During the curing process of the copper clad laminate, infrared data and deformation data of the surface are obtained at each detection moment; the surface of the copper clad laminate is divided into sub-windows; According to the degree of deviation of the infrared data change between each sub-window and the local sub-window, the thermal stress accumulation index of each sub-window at the detection time is obtained; according to the position distribution and the size of the thermal stress accumulation index between the sub-windows, they are combined into sub-temperature control areas; The temperature adjustment amplitude of each sub-temperature control area at the current detection moment is obtained by correlating the deformation data of each sub-temperature control area at the current detection moment with the change of the overall thermal stress accumulation index; Determine the deformation lag time based on the temporal correlation between the overall thermal stress accumulation index and the deformation data in each sub-temperature control area within the preset time analysis window. According to the direction of 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 adjusted temperature data of the sub-temperature control area; based on the adjusted temperature data of all sub-temperature control areas, the curing temperature curve of the copper clad laminate is obtained.

[0005] Further, the method for obtaining the thermal stress accumulation index comprises: For any one sub-window, the average of the pixel values of all infrared data in the sub-window at the current detection moment is taken as the thermal stress value of the sub-window; after calculating the thermal stress value difference between the sub-window and each other sub-window in 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 moment; The change rate of the local thermal conduction resistance coefficient of the sub-window between the previous detection moment and the current detection moment is calculated to obtain the resistance change coefficient of the sub-window at the current detection moment; The thermal stress accumulation index of the sub-window at the current detection moment is obtained by combining the local thermal conduction resistance coefficient and the resistance change coefficient of the sub-window at the current detection moment.

[0006] Further, the method for obtaining the sub-temperature control region comprises: When the difference in the thermal stress accumulation index between each sub-window and the adjacent sub-window is less than the preset deviation threshold, the sub-windows are combined as a sub-region, and the combination is stopped until it cannot 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 taken as each sub-temperature control region.

[0007] Further, the method for obtaining the temperature adjustment amplitude comprises: For any one sub-temperature control region, the average of the thermal stress accumulation indexes of all sub-windows included in the sub-temperature control region is taken as the region accumulation index of the sub-temperature control region; The numerical difference between the deformation data in the sub-temperature control region between the current detection moment and the previous detection moment is taken as the deformation time sequence difference value of the sub-temperature control region; the numerical difference between the region accumulation index of the sub-temperature control region between the current detection moment and the previous detection moment is taken as the thermal stress time sequence difference value of the sub-temperature control region; The ratio of the deformation time sequence difference value to the thermal stress time sequence difference value of the sub-temperature control region is taken as the deformation sensitivity coefficient of the sub-temperature control region; The product of the value of the deformation sensitivity coefficient of the sub-temperature control region after negative correlation mapping and the region accumulation index is normalized to obtain the temperature adjustment amplitude of the sub-temperature control region.

[0008] Further, the method for obtaining the deformation hysteresis time comprises: For any one sub-temperature control region, the average of the thermal stress accumulation indexes of all sub-windows included in the sub-temperature control region is taken as the region accumulation index of the sub-temperature control region; In the preset time analysis window of the current detection moment, a cross-correlation function is constructed by the mean deviation of the deformation data at each detection moment and the mean deviation of the regional cumulative index before the detection moment, and the thermal stress-deformation cross-correlation function is obtained; 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.

[0009] Furthermore, 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 moment and the mean of all deformation data in the preset time analysis window is used as the deformation deviation degree at each detection moment; The window before the time difference of the preset time analysis window is used as the stress analysis window, and the difference between the regional cumulative index before the time difference at each detection moment and the mean of the cumulative index of all regions in the stress analysis window is used as the preceding stress deviation degree at each detection moment; In the preset time analysis window, the product sum of the deformation deviation at the detection moment and the previous stress deviation 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 indicators of all regions in the stress analysis window are combined as the denominator to obtain the thermal stress-deformation cross-correlation function.

[0010] Furthermore, the method for obtaining the adjusted temperature data includes: The average pixel value of the infrared data in each sub-temperature control area is used as the regional calorific value of each sub-temperature control area. For any sub-temperature control area, the difference between the regional calorific value of the sub-temperature control area and each adjacent sub-temperature control area is calculated, and the sum of all 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 moment 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 amplitude 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 moment; When the neighborhood change value is negative, the product of the temperature to be adjusted and the temperature adjustment amplitude is used as the temperature increase adjustment value, and the sum of the temperature to be adjusted and the temperature decrease adjustment value is used as the adjustment temperature data of the current detection moment; When the neighborhood change value is zero, no temperature data adjustment is performed.

[0011] Furthermore, obtaining the curing temperature curve of the copper clad laminate based on the adjusted temperature data of all sub-temperature control areas includes: The adjustment temperature data of each sub-temperature control area is used as the target temperature. The curing heating system is used to adjust the temperature instruction of each sub-temperature control area and output the curing temperature curve of the entire surface of the copper clad laminate.

[0012] Furthermore, the method for obtaining the sub-window includes: The preset size is used as the side length of the divided window, and the divided window is slid from left to right and from top to bottom on the surface of the copper clad board with a preset step size to obtain each sub-window.

[0013] The present invention also provides a PI-based high-heat-resistant flexible copper-clad laminate, including a curing and heating system, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the gradient curing process of the above-mentioned PI-based high-heat-resistant flexible copper-clad laminate are implemented.

[0014] The present invention has the following beneficial effects: The present invention divides the sub-windows, reflects the degree and change of heat conduction resistance through the change deviation of infrared data, and constructs a thermal stress accumulation index to more accurately characterize the accumulation degree of local strain energy. The thermal stress accumulation index between sub-windows is iteratively subdivided and combined into sub-temperature control areas, and the change-related sensitivity between deformation data and thermal stress accumulation index is introduced to adjust the thermal stress accumulation index of the sub-temperature control area, increase the degree of regulation demand for temperature accumulation in the thermal stress area, make the regulation scale affect the deformation less likely, and make subsequent regulation more appropriate. Further use the cross-correlation to quantify the lag time between thermal stress and deformation, realize the compensation regulation analysis of thermal stress and deformation lag, based on the lag time and temperature adjustment amplitude, adjust the temperature data in the direction of the deviation of adjacent infrared thermal response, generate the regulated temperature data, and output the curing temperature curve of the full surface gradient curing of the copper clad laminate for dynamic temperature regulation independently performed for each sub-temperature control area. The present invention dynamically subdivides the surface temperature control area, analyzes the hysteresis effect of local thermal stress and deformation, regulates the temperature data of different local spatial positions, suppresses the accumulation of thermal stress at the interface between copper foil and PI substrate, and improves the performance stability and reliability of high heat-resistant flexible copper clad laminates. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1A flowchart of the steps of a gradient curing process for a PI-based high-heat-resistant flexible copper-clad laminate provided by one embodiment of the present invention; Figure 2 A local schematic diagram of infrared data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of the present invention to achieve its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a PI-based, highly heat-resistant, flexible copper-clad laminate and its gradient curing process according to the present invention. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, 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 belongs.

[0019] The specific scheme of a PI-based high heat-resistant flexible copper-clad laminate and its gradient curing process provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of the steps of a gradient curing process of a PI-based high heat-resistant flexible copper-clad laminate provided by one embodiment of the present invention, the method comprising the following steps: S1: Acquire infrared data and deformation data of the surface at each detection moment during the curing process of the copper clad laminate; divide the surface of the copper clad laminate into sub-windows.

[0021] In the embodiment of the present invention, during the curing process of the copper clad laminate, an infrared thermal imaging and high-precision thermocouple sensor array is placed inside the curing equipment, i.e., a high-precision curing furnace. The sensor deployment can require a spatial resolution of ≤1mm and a thermal sensitivity of ≤0.05°C. The sensor records the spatial temperature distribution data in real time, and simultaneously collects the time-temperature curve inside the curing equipment and the infrared data image of the surface. The sampling frequency is not less than 1Nz to ensure the accurate capture of temperature gradient changes. Figure 2 , which shows a local schematic diagram of infrared data provided by an embodiment of the present invention.

[0022] In the curing process of copper clad laminates, temperature control usually adopts the method of zone temperature control. Due to factors such as material thickness, shape and thermal conductivity, the heat distribution in the center area, transition area and edge area will be different. Therefore, under normal circumstances, the surface of the copper clad laminate will be initially divided into the center area, transition area and edge area and temperature control will be performed separately.

[0023] At the same time, the non-contact laser displacement sensor installed on the copper-clad laminate is used to detect the Z-axis warpage displacement and in-plane strain distribution throughout the curing process in real time, that is, the deformation of the plate in the XY plane. The displacement measurement accuracy is controlled at ±0.1μm, and the sum of the Z-axis warpage displacement and the deformation in the XY plane is used as the deformation data.

[0024] The acquisition frequency of deformation data is kept consistent with that of infrared data, and infrared data and deformation data are obtained at each detection moment. This is also to establish a time synchronization controller to align the acquisition timestamps of infrared data and deformation data. A missing data compensation algorithm based on cubic spline interpolation is used to reconstruct a complete data set using the gradient features of adjacent area data, and the collected data is processed for storage, management, and analysis. It should be noted that the deployment and acquisition of equipment for dynamic monitoring during the curing process is a technical means well known to those skilled in the art. Implementers can adjust and control it according to the specific implementation scenario, and there is no restriction here.

[0025] When the copper-clad laminate is solidified, shear stress is generated at the interface between the copper foil and the PI substrate due to the difference in thermal expansion, resulting in insufficient bonding between the copper foil and the PI substrate. The uneven temperature distribution at the interface will aggravate stress concentration, further increasing the risk of material deformation and damage. Therefore, the initially differentiated temperature control area cannot meet the analysis of the thermal state of the micro-area.

[0026] Therefore, in an embodiment of the present invention, for the image data of the surface of the copper clad laminate in each detection period, that is, the infrared data, the preset size is used as the side length of the dividing window, and the upper left corner of the image is used as the origin. The dividing window is slid from left to right and from top to bottom on the surface of the copper clad laminate with a preset step size to obtain multiple overlapping divided sub-windows, among which the preset size is 3, that is, the dividing window size is 3×3, and the step size is 1. The specific numerical value can be adjusted by the implementer at his own discretion and is not limited here.

[0027] At this point, the dynamic subdivision of the temperature control zone during the curing process is achieved.

[0028] S2: Based on the degree of deviation of the infrared data change between 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 position distribution between the sub-windows and the size of the thermal stress accumulation index, they are combined into sub-temperature control areas.

[0029] The essence of thermally induced warping of copper clad laminates during the curing process is thermal stress at the interface of different materials, and thermal stress is essentially the accumulation of elastic strain energy caused by uneven distribution of thermal energy at the interface. Therefore, the deviation of infrared data changes between each sub-window and the local sub-window reflects the resistance to heat transfer within the material during the curing process. The greater the deviation change, the more serious the accumulation of resistance to heat exchange between the micro-region and the surrounding area, which may cause situations such as material defects or interface thermal resistance, and increase local thermal stress.

[0030] In an embodiment of the present invention, a method for obtaining a thermal stress accumulation index includes: For any subwindow, the average pixel value of all infrared data within that subwindow at the current detection moment is used as the thermal stress value for that subwindow, representing the temperature of the micro-region at that moment. After calculating the difference in thermal stress between that subwindow and every other subwindow within a preset local range, the average of all thermal stress value differences is used as the local thermal conduction resistance coefficient for that subwindow at the current detection moment.

[0031] In an embodiment of the present invention, the preset local range is set to the eight-neighborhood range of the sub-window, that is, within the eight adjacent sub-windows around the sub-window, the differences in the thermal stress values ​​are calculated respectively and then the average is obtained to obtain the local heat conduction resistance coefficient, which reflects the heat exchange capacity between the micro-region and the surrounding area. The larger the local heat conduction resistance coefficient, the more significant the inability of the current heat energy to diffuse in time.

[0032] Furthermore, the rate of change of the local thermal conduction resistance coefficient of the sub-window between the previous detection moment and the current detection moment is calculated to obtain the resistance variation coefficient of the sub-window at the current detection moment. In an embodiment of the present invention, the difference between the local thermal conduction resistance coefficient at the current detection moment and the local thermal conduction resistance coefficient at the previous detection moment is used as the numerator, and the time difference between the previous detection moment and the current detection moment is used as the denominator to obtain the resistance variation coefficient of the sub-window at the current detection moment, reflecting the changing trend of the thermal conduction resistance of the micro-area material over time. The larger the resistance variation coefficient, the higher the risk of local thermal runaway, such as excessive cross-linking of the resin to release heat or the enhanced exothermic effect of the thermal reaction.

[0033] Finally, the local heat conduction resistance coefficient and the resistance variation coefficient of the sub-window at the current detection moment are combined to obtain the thermal stress accumulation index of the sub-window at the current detection moment. In an embodiment of the present invention, the product of the local heat conduction resistance coefficient and the resistance variation coefficient of the sub-window at the current detection moment is used as the thermal stress accumulation index of the sub-window at the current detection moment. When the local heat conduction resistance coefficient and the resistance variation coefficient are larger, it means that the heat accumulation in the micro-area is more significantly accelerated, and the accumulation degree is higher. The poor heat energy diffusion effect leads to conversion into mechanical stress (thermal stress), which is very likely to cause material warping or delamination.

[0034] Therefore, the thermal stress accumulation index can be used to measure the presence of potential warpage origins in micro-regions, indicating the need for focused intervention in subsequent temperature control. The thermal stress accumulation index allows for more detailed division of temperature control zones, and through the combination of micro-regions, sub-temperature control zones can be determined for dynamic regional temperature field regulation.

[0035] In an embodiment of the present invention, a method for obtaining a sub-temperature control area includes: When the difference between the thermal stress accumulation indexes of each sub-window and the adjacent sub-window is less than the preset deviation threshold, the sub-windows are combined as a sub-region until no combination is possible. The smaller the difference between the thermal stress accumulation indexes of the sub-windows, the closer the thermal stress states of the micro-regions, which are suitable for the same temperature regulation, and thus the combination is performed. In the embodiment of the present application, the preset deviation threshold is set to 0.3, and the specific value can be adjusted by the implementer.

[0036] When no combination is possible, the threshold value is re-increased to ensure that all micro-regions are combined to obtain a plurality of sub-temperature control regions. After the deviation threshold is increased 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 taken as each sub-temperature control region. In the embodiment of the present application, the preset size can be set to 0.1, and the specific value is not limited herein.

[0037] S3: Obtain the temperature regulation amplitude of each sub-temperature control region at the current detection time by correlating the deformation data of each sub-temperature control region at the current detection time with the change of the overall thermal stress accumulation index.

[0038] In the sub-temperature control region, when there is a higher thermal stress accumulation, a larger temperature regulation amplitude is usually required to reduce the thermal stress accumulation and avoid the deformation of the region material. The goal of temperature regulation is to offset the stress and avoid warping, but temperature regulation itself can cause new deformation, such as material shrinkage caused by rapid cooling, and the sensitivity of the corresponding unit thermal stress change to the deformation of the region is different when the center region and the edge region of the copper-clad plate are adjusted in temperature. Therefore, the sensitivity of the deformation of each sub-temperature control region to the thermal accumulation is analyzed, and the regulation amplitude is corrected to ensure the stability of the material regulation of each region.

[0039] Preferably, in the embodiment of the present application, the method for obtaining the temperature regulation amplitude comprises: For any one sub-temperature control region, the mean value of the thermal stress accumulation indexes of all sub-windows in the sub-temperature control region is taken as the region accumulation index of the sub-temperature control region, which reflects the average stress accumulation of the sub-temperature control region. The thermal stress accumulation degrees of different sub-temperature control regions are different, resulting in different degrees of instantaneous deformation of the thermal stress when the temperature changes.

[0040] Further, the numerical difference between the deformation data of the sub-temperature control region at the current detection time and the previous detection time is taken as the deformation time sequence difference value of the sub-temperature control region, and the numerical difference between the region accumulation index of the sub-temperature control region at the current detection time and the previous detection time is taken as the thermal stress time sequence difference value of the sub-temperature control region. It can be understood that when the deformation time sequence difference value or the thermal stress time sequence difference value is zero, the correlation sensitivity between the instantaneous deformation and the stress is not high, and the correction requirement is low, so the deformation sensitivity coefficient can be directly set to 1 without adjustment of the regulation amplitude.

[0041] Otherwise, the ratio of the deformation time series difference to the thermal stress time series difference of the sub-temperature control area is used as the deformation sensitivity coefficient of the sub-temperature control area, reflecting the scale of the change caused by the unit thermal stress change in the corresponding sub-temperature control area. The larger the deformation sensitivity coefficient, the greater the impact of the unit change of thermal stress in the current sub-temperature control area on the regional deformation. The essence of the thermal stress change in the sub-temperature control area is the change in the regional temperature, so the adjustment amplitude needs to be reduced.

[0042] Finally, the product of the negatively correlated deformation sensitivity coefficient of the sub-temperature control region and the regional accumulation index is normalized and used as the temperature adjustment amplitude for the sub-temperature control region. The temperature adjustment amplitude reflects the required temperature correction scale for the sub-temperature control region. A larger temperature adjustment amplitude indicates that the sub-temperature control region has high stress and low sensitivity, requiring a larger temperature adjustment. A smaller temperature adjustment amplitude indicates that the sub-temperature control region has high stress and high sensitivity, requiring a smaller temperature adjustment to avoid excessive deformation.

[0043] S4: Determine the deformation lag time based on the temporal correlation between the overall thermal stress accumulation index and the deformation data in each sub-temperature control area in the preset time analysis window.

[0044] In actual gradient curing processes, degradation phenomena such as warping of copper-clad laminates are primarily caused by temperature-driven stress accumulation. Due to the viscoelastic properties of copper-clad laminates, there is a significant time lag between the accumulation of thermal stress and macroscopic deformation. This means that thermal stress typically occurs before macroscopic deformation, with a time delay between the two.

[0045] If temperature control is performed directly based on the adjustment amplitude, uncompensated hysteresis can lead to premature or delayed intervention. For example, premature cooling before thermal stress is fully converted can inhibit normal resin cross-linking, or adjustment is made after deformation has already occurred, causing irreversible damage. Therefore, it is necessary to analyze the optimal delay between pressure and deformation to provide a time basis for subsequent early temperature adjustment. By correlating the accumulated thermal stress with the change in deformation over time, the time delay that best matches the desired match can be determined.

[0046] Preferably, in an embodiment of the present invention, the method for obtaining the deformation lag time includes: For any sub-temperature control area, within the preset time analysis window at the current detection moment, a cross-correlation function is constructed using the mean offset of the deformation data at each detection moment and the mean offset of the cumulative regional index before the detection moment, resulting in a thermal stress-deformation cross-correlation function, with the time difference as the independent variable. Using this cross-correlation function, the most correlated changes between the accumulated thermal stress and the lagged deformation data are identified. The lag time determined at this point best reflects the actual lag relationship between the two.

[0047] 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 used as the deformation deviation at each detection moment. The window preceding the preset time analysis window with the time difference is used as the stress analysis window. The time difference is used to shift the window forward as a whole to analyze the deformation caused by the hysteresis effect. The difference between the regional cumulative index at each detection moment before the time difference and the mean of the regional cumulative index within the stress analysis window is used as the preceding stress deviation at each detection moment.

[0048] Considering the matching of data change trends, the interference of overall data offset is eliminated through mean deviation, retaining only the change relative to the mean at each moment for change correlation analysis. In this embodiment of the present invention, the preset time analysis window size is set to a range of 10 seconds before the detection moment. The specific value can be adjusted by the implementer based on the specific implementation scenario and is not limited here.

[0049] In the preset time analysis window, the product of the deformation deviation at the detection moment and the previous stress deviation is used as the numerator, and the standard deviation of all deformation data in the preset time analysis window and the standard deviation of all regional cumulative indicators in the stress analysis window are combined as the denominator to obtain the thermal stress-deformation cross-correlation function. The denominator is obtained by multiplying the standard deviation of the deformation data with the standard deviation of the regional cumulative indicators to avoid order of magnitude effects. As an example, the expression of the thermal stress-deformation cross-correlation function is: Where, Expressed as time difference, time difference is the independent variable, It is expressed as the total number of detection moments in the preset time analysis window. Indicates the first A detection moment, Expressed as The deformation data at each detection moment, It is expressed as the mean value of deformation data in the preset time analysis window. Expressed as The time difference between the detection moments The regional cumulative index at the detection time before, It is expressed as the mean value of the regional cumulative index in the stress analysis window, Expressed as the standard deviation of the cumulative index of all regions in the stress analysis window, Expressed as the standard deviation of all deformation data in the preset time analysis window, It is a preset adjustment factor, which is set to 0.001 in the embodiment of the present invention, in order to prevent the denominator from being zero, which makes the formula meaningless.

[0050] The numerator reflects the sympathetic relationship between the deformation trend and the thermal stress trend τ seconds prior by summing the deviation products. The denominator is used to normalize the magnitude, eliminating magnitude differences and only comparing trend synchrony. 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 strongest correlation between the two data series at this time shift. The time difference at this point is used as the deformation lag, representing the time delay between thermal stress accumulation and macroscopic deformation in the corresponding sub-temperature control region.

[0051] S5: According to the direction of the infrared data deviation between each sub-temperature control area and the adjacent sub-temperature control area, the original temperature data of the deformation lag time is adjusted by the temperature adjustment amplitude to obtain the adjusted temperature data of the sub-temperature control area; based on the adjusted temperature data of all sub-temperature control areas, the curing temperature curve of the copper clad laminate is obtained.

[0052] The adjustment direction is determined by the degree of temperature deviation between the sub-temperature control area and the adjacent sub-temperature control area. Temperature control is performed in the adjustment direction based on the analyzed temperature adjustment amplitude 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 direction and intensity coupling.

[0053] Preferably, in an embodiment of the present invention, the method for obtaining the adjustment temperature data includes: 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 regional heat distribution. For any sub-temperature control area, the difference between the regional heat value of this sub-temperature control area and each adjacent sub-temperature control area is calculated. The sum of all these differences is used as the neighborhood change value of the sub-temperature control area. A positive neighborhood change value indicates that the thermal stress in the sub-temperature control area is caused by locally too high a temperature, and the curing temperature needs to be lowered. Conversely, a negative neighborhood change value indicates that the thermal stress is caused by locally too low a temperature, and the curing temperature needs to be increased.

[0054] The original temperature data of the sub-temperature control area at the current moment after the deformation lag time is used as the temperature to be adjusted. The original temperature data is obtained based on the time-temperature data of the initial temperature control area and is used as the basic curing temperature data.

[0055] Furthermore, when the neighborhood change value is positive, the product of the temperature to be adjusted and the temperature adjustment amplitude is used as the cooling adjustment value for downward 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 moment. By controlling the temperature layout in advance, the occurrence of hysteresis deformation is reduced.

[0056] When the neighborhood change value is negative, the product of the target temperature and the temperature adjustment range is used as the temperature increase adjustment value, and the adjustment is performed. The sum of the target temperature and the temperature decrease adjustment value is used as the adjustment temperature data for the current detection time. In particular, when the neighborhood change value is zero, it indicates that the current area is in a dynamic equilibrium state. No further disruption of this equilibrium is required, so no temperature adjustment is performed.

[0057] In the gradient curing process of PI-based high-heat-resistant flexible copper clad laminates, heat is transferred to different areas of the copper clad laminate inside a high-precision curing furnace through multiple heaters, such as electric heating tubes, hot oil circulation, etc. Each heater works independently according to the set temperature range. In an embodiment of the present invention, based on the current adjustment situation, the adjustment temperature data of each sub-temperature control area is used as the target temperature. The temperature instructions of each sub-temperature control area are independently executed through the multi-curing heating system of the curing furnace, and the dynamic adjustment temperature instructions of all sub-temperature control areas are integrated according to the spatial position, and the output is the optimal curing temperature curve for gradient curing covering the entire surface of the copper clad laminate.

[0058] In an embodiment of the present invention, closed-loop process optimization is achieved by dynamically controlling the temperature. The curing temperature curve is input into a high-precision curing furnace 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, interface adhesion test, and thermal stability verification. If any test fails to meet the standard, the temperature data analysis is readjusted, and the temperature control amplitude is locally adjusted based on this curing temperature curve until the test meets the standard, ensuring that each link in the curing process is precisely controlled, and ultimately achieving the optimal copper clad laminate curing effect.

[0059] In summary, the present invention divides sub-windows, reflects the degree and change of heat conduction resistance through the change deviation of infrared data, and constructs a thermal stress accumulation index to more accurately characterize the degree of accumulation of local strain energy. The thermal stress accumulation index between sub-windows is iteratively subdivided and combined into sub-temperature control areas, and the change-related sensitivity between deformation data and thermal stress accumulation index is introduced to adjust the thermal stress accumulation index of the sub-temperature control area, increase the degree of regulation demand for temperature accumulation in the thermal stress area, make the regulation scale affect the deformation less likely, and make subsequent regulation more appropriate. Further, the cross-correlation is used to quantify the lag time between thermal stress and deformation, realize the compensation regulation analysis of thermal stress and deformation lag, and based on the lag time and temperature adjustment amplitude, adjust the temperature data in the direction of the deviation of adjacent infrared thermal response, generate the regulated temperature data, and output the curing temperature curve of the full surface gradient curing of the copper clad laminate for dynamic temperature regulation independently performed for each sub-temperature control area. The present invention dynamically subdivides the surface temperature control area, analyzes the hysteresis effect of local thermal stress and deformation, regulates the temperature data of different local spatial positions, suppresses the accumulation of thermal stress at the interface between copper foil and PI substrate, and improves the performance stability and reliability of high heat-resistant flexible copper clad laminates.

[0060] The present invention also provides a PI-based high-heat-resistant flexible copper-clad laminate, including a curing and heating system, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the gradient curing process of the above-mentioned PI-based high-heat-resistant flexible copper-clad laminate are implemented.

[0061] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A gradient curing process for a PI-based high heat-resistant flexible copper-clad laminate, characterized in that: The process comprises: During the curing process of the copper clad laminate, infrared data and deformation data of the surface are obtained at each detection moment; the surface of the copper clad laminate is divided into sub-windows; According to the degree of deviation of the infrared data change between each sub-window and the local sub-window, the thermal stress accumulation index of each sub-window at the detection time is obtained; according to the position distribution and the size of the thermal stress accumulation index between the sub-windows, they are combined into sub-temperature control areas; The temperature adjustment amplitude of each sub-temperature control area at the current detection moment is obtained by correlating the deformation data of each sub-temperature control area at the current detection moment with the change of the overall thermal stress accumulation index; Determine the deformation lag time based on the temporal correlation between the overall thermal stress accumulation index and the deformation data in each sub-temperature control area within the preset time analysis window. According to the direction of 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 adjusted temperature data of the sub-temperature control area; based on the adjusted temperature data of all sub-temperature control areas, the curing temperature curve of the copper clad laminate is obtained.

2. The gradient curing process of 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 used as the thermal stress value of the sub-window; after calculating the difference in thermal stress values ​​between the sub-window and every other sub-window within the preset local range, the average of all thermal stress value differences is used 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 moment and the current detection moment, and obtain the resistance change coefficient of the sub-window at the current detection moment; The thermal stress accumulation index of the subwindow at the current detection moment is obtained by combining the local heat conduction resistance coefficient and the resistance variation coefficient of the subwindow at the current detection moment.

3. The gradient curing process of 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 the adjacent sub-window is less than a preset deviation threshold, the sub-windows are combined as a sub-region until they cannot be combined anymore; After the deviation threshold is increased 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 each sub-temperature control region.

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

5. The gradient curing process of a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that: The method for obtaining the deformation lag time includes: For any sub-temperature control area, the average value of the thermal stress accumulation index of all sub-windows in the sub-temperature control area is used as the regional cumulative index of the sub-temperature control area; In the preset time analysis window of the current detection moment, a cross-correlation function is constructed by the mean deviation of the deformation data at each detection moment and the mean deviation of the regional cumulative index before the detection moment, and the thermal stress-deformation cross-correlation function is obtained; 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 of 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 moment and the mean of all deformation data in the preset time analysis window is used as the deformation deviation degree at each detection moment; The window before the time difference of the preset time analysis window is used as the stress analysis window, and the difference between the regional cumulative index before the time difference at each detection moment and the mean of the cumulative index of all regions in the stress analysis window is used as the preceding stress deviation degree at each detection moment; In the preset time analysis window, the product sum of the deformation deviation at the detection moment and the previous stress deviation 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 indicators of all regions in the stress analysis window are combined as the denominator to obtain the thermal stress-deformation cross-correlation function.

7. The gradient curing process of a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that: The method for obtaining the adjustment temperature data includes: The average pixel value of the infrared data in each sub-temperature control area is used as the regional calorific value of each sub-temperature control area. For any sub-temperature control area, the difference between the regional calorific value of the sub-temperature control area and each adjacent sub-temperature control area is calculated, and the sum of all 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 moment 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 amplitude 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 moment; When the neighborhood change value is negative, the product of the temperature to be adjusted and the temperature adjustment amplitude is used as the temperature increase adjustment value, and the sum of the temperature to be adjusted and the temperature decrease adjustment value is used as the adjustment temperature data of the current detection moment; When the neighborhood change value is zero, no temperature data adjustment is performed.

8. The gradient curing process of a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that: The method of obtaining a curing temperature curve of the copper clad laminate based on the adjusted temperature data of all sub-temperature control areas includes: The adjustment temperature data of each sub-temperature control area is used as the target temperature. The curing heating system is used to adjust the temperature instruction of each sub-temperature control area and output the curing temperature curve of the entire surface of the copper clad laminate.

9. The gradient curing process of a PI-based high heat-resistant flexible copper-clad laminate according to claim 1, characterized in that: The method for obtaining the sub-window includes: The preset size is used as the side length of the divided window, and the divided window is slid from left to right and from top to bottom on the surface of the copper clad board 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 comprises 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, the steps of the gradient curing process of the PI-based high heat-resistant flexible copper-clad laminate as claimed in any one of claims 1 to 9 are implemented.

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