A fluorescent layer coating process for a chip package substrate

Abstract

The application provides a fluorescent layer coating process of a chip packaging substrate, relates to the field of integrated chip coating processing technology, and comprises the following steps: S1, collecting coating stability characteristic data; S2, constructing a relational expression of the coating stability characteristic data to obtain a flow hanging risk coefficient; S3, setting a flow hanging risk threshold and an initial single coating thickness, and comparing the flow hanging risk coefficient with the flow hanging risk threshold; S4, in a coating thickness optimization process, calculating and obtaining a coating thickness optimization coefficient; and S5, constructing a correction coupling formula to calculate and obtain a single coating correction thickness of the fluorescent layer. The application constructs a flow hanging risk quantitative evaluation model, so that the flow hanging risk can be accurately predicted before coating operation. The self-adaptive thickness adjustment effect based on real-time working conditions, which cannot be achieved in a conventional scheme, is realized, the efficiency loss caused by blind reduction of the thickness is avoided, and flow hanging defects caused by environmental fluctuations or material batch differences are effectively eliminated.

Description

Technical Field

[0001] This invention relates to the field of integrated chip coating technology, specifically to a phosphor coating process for a chip packaging substrate. Background Technology

[0002] In the phosphor coating process of chip packaging substrates, sagging refers to the downward flow of liquid phosphor paste due to gravity after coating but before curing, resulting in uneven coating thickness distribution, edge accumulation, or localized exposure. This not only damages the optical uniformity and thickness consistency of the phosphor layer but also affects the luminous efficiency and color temperature stability of the chip. In severe cases, it can even lead to a decrease in the adhesion between the substrate and the phosphor layer, causing reliability issues such as delamination or peeling, ultimately reducing the yield and long-term lifespan of the packaged device. Currently, the mainstream methods to avoid sagging mainly focus on optimizing process parameter windows. For example, adjusting the viscosity and thixotropy of the phosphor paste to enhance its anti-sagging ability, strictly controlling the thermosetting temperature profile to accelerate solvent evaporation and cross-linking reaction, reducing the humidity in the coating environment to suppress slow solvent evaporation, and roughening or enhancing the substrate surface to improve coating adhesion.

[0003] In the prior art, CN101582482A discloses a method for coating a phosphor layer on the surface of an LED chip. This technology includes the following steps: a phosphor coating step, in which phosphor is uniformly sprayed onto the chip surface to form several phosphor dots; a hot air leveling step, in which hot air is blown onto the phosphor dots on the chip surface, causing each phosphor dot to level under the action of the hot air, forming a smooth phosphor layer; and a phosphor curing step, in which the chip with the phosphor layer is dried to cure the phosphor layer on the chip surface. The nozzle is a dot-matrix nozzle, which has several spray holes arranged in a dot pattern. The above method, by performing hot air leveling after coating the phosphor layer, greatly improves the flatness of the phosphor layer and makes the phosphor layer thickness more uniform. This results in high luminous flux, uniform color, and good reliability of the device, which can be widely used in LCD backlighting, household lighting, industrial lighting, flashlight light sources, mining lamp light sources, automotive lighting, and other fields.

[0004] However, in the aforementioned existing technologies, the coating process relies on fixed process parameters and human experience for setting, making it impossible to quantitatively predict the risk of sagging. Therefore, when faced with material batch fluctuations, environmental changes, or equipment status drift, a conservative fixed single coating thickness is often adopted, resulting in limited production efficiency or frequent sagging defects. Conventional solutions lack proactive intervention mechanisms and cannot dynamically optimize and adjust the coating thickness when the risk increases, making it difficult to achieve the best balance between quality and efficiency. In addition, each coating process in conventional solutions is isolated, making process optimization dependent on repeated trial and error, making it difficult to continuously improve the stability of coating quality and adaptability to different working conditions.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a phosphor coating process for chip packaging substrates to solve the problems mentioned in the background art. This invention constructs a quantitative assessment model for sagging risk coefficients, thereby enabling accurate prediction of sagging risks before the coating process. It achieves an adaptive thickness adjustment effect based on real-time operating conditions, which is unattainable in conventional solutions. This avoids efficiency losses caused by blindly reducing thickness and effectively eliminates sagging defects caused by environmental fluctuations or batch-to-batch material differences, significantly improving the stability, consistency, and adaptability to complex operating conditions of the coating quality.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A phosphor coating process for a chip packaging substrate includes the following steps: S1: Collect coating stability characteristic data, including coating viscosity, thermosetting temperature, ambient humidity and substrate surface roughness; S2: Preprocess the coating stability characteristic data, construct the relational expression of the coating stability characteristic data, and input the coating viscosity, thermosetting temperature and ambient humidity and substrate surface roughness under the current working conditions into the relational expression to obtain the sagging risk coefficient; S3: Set the sagging risk threshold and the initial single coating thickness, compare the sagging risk coefficient with the sagging risk threshold. If the sagging risk coefficient is less than or equal to the sagging risk threshold, the control logic will maintain the original strategy and process directly according to the initial single coating thickness. If the sagging risk coefficient is greater than the sagging risk threshold, the coating thickness optimization process will be triggered, and the active intervention and correction stage will be entered. S4: In the coating thickness optimization process, optimization parameters are collected, including solvent evaporation rate, fluorescent paste settling rate and UV curing light intensity. Based on the sagging risk coefficient and optimization parameters, the coating thickness optimization coefficient is calculated and obtained. The coating thickness optimization coefficient is used to quantify the degree of reduction of the initial single coating thickness. S5: Establish a correction coupling formula, input the obtained coating thickness optimization coefficient and initial single coating thickness into the correction coupling formula, calculate the single coating correction thickness of the fluorescent layer, and use the single coating correction thickness of the fluorescent layer for coating process production.

[0008] Furthermore, in step S1, during the preparation phase before the coating operation begins, multiple sets of coating stability characteristic data related to coating quality are continuously collected. All collected coating stability characteristic data are aggregated into the process database, wherein: The viscosity of the coating is used to reflect the flow characteristics of the fluorescent paste at the current moment. It is obtained by real-time monitoring of the paste circulating from the feeding system. The change in the coating viscosity value is directly related to the spreadability of the paste on the substrate surface. The thermosetting temperature is obtained through an array of thermocouples distributed within the curing oven, which is used to characterize the uniformity of the temperature distribution and its consistency with the set value. Ambient humidity refers to the relative humidity of the air in the enclosed space where the coating operation is carried out. Fluctuations in ambient humidity affect the evaporation rate of the solvent in the slurry. The substrate surface roughness is recorded in the system after random sampling of the surface micromorphology of the current batch of substrates. The substrate surface roughness represents the interfacial characteristics of the substrate and the phosphor layer.

[0009] Furthermore, it also includes a process for preprocessing the coating stability characteristic data: First, the integrity of the multi-source data collected from the production site is verified. Outliers caused by instantaneous interference or collection failure are removed by setting a threshold range. For white noise or periodic fluctuations in the time series data, digital filtering algorithms are used for smoothing. High-frequency components are eliminated by moving average or low-pass filtering. The cleaned data is normalized to map it to a uniform numerical range.

[0010] Furthermore, the formula for normalizing the cleaned data is as follows:

[0011] in: The collected coating stability characteristic data; This is to normalize the coating stability characteristic data; Minimum baseline value for each coating stability feature data; The maximum baseline value for each coating stability feature data; During the normalization process, a corresponding static reference value is selected for each coating stability feature data. The process for obtaining the static reference value is as follows: retrieve the real-time datasets of the coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness that have been cleaned under the current working conditions; for each coating stability feature data, traverse its data sequence to determine the maximum and minimum reference values ​​under the historical stable process window; and establish a dynamic verification mechanism for the reference value, updating the reference value periodically based on the distribution characteristics of historical data.

[0012] Furthermore, the formula used to obtain the sag risk factor is as follows:

[0013] in: S represents the risk coefficient of sag. H represents the normalized ambient humidity, with a value range of [0, 1]. η is the normalized viscosity of the coating, with a value range of [0, 1]. T is the normalized thermosetting temperature, with a value range of [0, 1]. R is the normalized substrate surface roughness, with a value range of [0, 1]. α, β, γ, and δ are the weighting coefficients for the normalized coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness, respectively. Based on the physical mechanism of the coating process and practical experience, the influence of each weighting coefficient on the risk of sagging varies, and the weighting coefficients satisfy the following relationship: β > γ > α > δ.

[0014] Furthermore, in S3, the overflow risk threshold is set as follows: The initial single coating thickness is set to The sag risk coefficient S obtained from S2 is compared with the sag risk threshold. The logic for the comparison is as follows: like This indicates that the risk of sagging under the current operating conditions is within a controllable range and no intervention is required. At this time, the control logic maintains the original strategy and directly applies the initial single-coat thickness. Perform coating processing; like If the risk of sagging is too high, the coating thickness optimization process is initiated, and the system enters the active intervention and correction stage, immediately collecting optimization parameters.

[0015] Furthermore, this also includes the risk threshold for condensation. Acquisition process: Multiple sets of coating stability characteristic data and corresponding sagging observation results from historical process production were collected as samples to construct a sample set including normal operating conditions and critical sagging conditions. After normalizing and preprocessing the sample data, the data were substituted into the relational expression in step S2 to calculate the sagging risk coefficient of each sample. And statistical analysis of critical flow conditions The minimum value is used as the initial reference threshold. A safety margin is introduced to correct the reference threshold, and the effectiveness of the reference threshold is verified through multiple experiments. After the effectiveness verification, the reference threshold is used for real-time comparison. value.

[0016] Furthermore, the thickness of the single coating... The acquisition process is as follows: Based on historical coating data in the process database, the optimal coating thickness under standard operating conditions is extracted as a benchmark value. The benchmark value must meet the process requirements of uniform fluorescent layer coverage and no sagging defects. The benchmark value is compensated and calibrated to eliminate the influence of batch differences. The coating effect of the optimal coating thickness is verified through coating experiments.

[0017] Furthermore, the formula used to calculate the coating thickness optimization coefficient is as follows:

[0018] in: k is the coating thickness optimization coefficient; V is the solvent evaporation rate. This is the safety boundary threshold for the solvent evaporation rate; U represents the settling velocity of the fluorescent paste. This represents the safety boundary threshold for the settling velocity of fluorescent paste. I represents the UV curing light intensity. The safe boundary threshold for UV curing light intensity; Based on process experiments, these represent the minimum allowable values ​​for each optimized parameter while ensuring coating quality.

[0019] Furthermore, the corrective coupling formula is as follows:

[0020] in, The corrected thickness for a single coating of the fluorescent layer is determined by the value of k. A larger k value indicates a greater reduction in the initial single coating thickness, resulting in a smaller corrected thickness. If k is close to 1, the corrected thickness for a single coating of the fluorescent layer is close to the initial single coating thickness. The corrected thickness for a single coating of the fluorescent layer is input into the production control unit as an execution parameter of the actual coating process to guide the sequential coating operation under the current working conditions. At the same time, the system automatically records the corrected thickness for a single coating of the fluorescent layer and its corresponding sagging risk coefficient and optimization parameters, and incorporates them into the process database as historical data.

[0021] Compared with the prior art, the beneficial effects of the present invention are: This invention collects multi-source characteristic data such as coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness in real time, and constructs a quantitative assessment model for sagging risk coefficient. This allows for accurate prediction of sagging risk before coating operations. When the risk is too high, the optimization process is proactively triggered. The thickness of each coating is dynamically adjusted by combining key parameters such as solvent evaporation rate, fluorescent paste settling speed, and UV curing light intensity. This achieves an adaptive thickness adjustment effect based on real-time operating conditions that is impossible in conventional solutions. It avoids efficiency losses caused by blindly reducing thickness and effectively eliminates sagging defects caused by environmental fluctuations or material batch differences. At the same time, by automatically recording the results of each optimization and corresponding parameters to the process database, a data-driven closed-loop optimization system is formed. This allows the process window to continuously improve itself with the accumulation of historical data, significantly improving the stability, consistency, and adaptability of coating quality to complex operating conditions. Attached Figure Description

[0022] Figure 1 This is a flowchart of a phosphor coating process for a chip packaging substrate according to the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0024] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] Example: Please see Figure 1 The present invention provides the following technical solutions: A phosphor coating process for a chip packaging substrate includes the following steps: S1: Collect coating stability characteristic data, including coating viscosity, thermosetting temperature, ambient humidity and substrate surface roughness; During the preparation phase before the coating operation begins, multiple sets of coating stability characteristic data related to coating quality are systematically collected. These data comprehensively reflect the key factors that may affect the uniformity and adhesion of the phosphor layer during the current operating conditions. This process is widely used in integrated circuit manufacturing, especially suitable for phosphor layer coating on packaging substrates of application-specific integrated circuits (ASICs), application-specific large-scale integrated circuits (ASICs), and application-specific integrated chips, ensuring their optical performance and long-term reliability. All collected raw data are aggregated into the process database in real time, serving as the basis for subsequent analysis and control, thereby ensuring data traceability and the scientific nature of process adjustments. Specifically: The viscosity of the coating is used to accurately reflect the flow characteristics of the fluorescent paste at a given moment. This parameter is obtained by real-time monitoring of the paste circulating from the supply system, allowing for dynamic tracking of changes in the paste's state. Fluctuations in the coating viscosity directly affect the wetting behavior and spreading ability of the paste on the substrate surface, thus impacting the continuity and thickness uniformity of the fluorescent layer.

[0026] The thermosetting temperature is collected in real time at multiple points using a thermocouple array distributed inside the curing oven to ensure the uniformity of the temperature distribution and its good match with the process settings. This temperature condition directly determines the degree of cross-linking reaction and density of the fluorescent layer during the curing process, and affects the adhesion and internal stress distribution of the final coating.

[0027] Ambient humidity refers to the relative humidity of the air in the enclosed space where the coating operation takes place, which is continuously monitored by a high-precision humidity sensor. Changes in ambient humidity affect the evaporation rate of the solvent in the slurry, thereby interfering with the flow properties of the coating and its physical state in the early stage of curing. Excessive humidity may cause defects on the coating surface, affecting the yield.

[0028] Substrate surface roughness is recorded into the system after sampling and inspecting the surface microstructure of the current batch of substrates. This parameter is typically measured using a stylus profilometer or a laser confocal microscope. Substrate surface roughness directly reflects the interfacial bonding characteristics between the substrate and the phosphor layer. Appropriate roughness helps enhance coating adhesion, but excessive or insufficient roughness may affect the uniformity and stability of the coating.

[0029] S2: Systematically preprocess the collected coating stability characteristic data. The fundamental purpose of preprocessing is to eliminate noise and anomalies that may be mixed in the raw data, improve the accuracy and consistency of the data, and thus provide high-quality and reliable input for the subsequent construction of relational expressions. The relational expressions for the coating stability characteristic data are constructed by inputting the coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness under the current operating conditions into the relational expressions to obtain the sagging risk coefficient; Specifically, this includes the process of preprocessing coating stability characteristic data: First, the integrity of multi-source data collected from the production site is verified. By combining process experience and statistical methods, a reasonable threshold range is set to remove outliers caused by transient electromagnetic interference, sensor failure, or communication anomalies. If these outliers are not identified and processed, they will seriously distort the subsequent analysis results. For parameters such as viscosity, temperature, and humidity that change over time, considering that they are often affected by white noise or periodic fluctuations, digital filtering algorithms are used for smoothing. For example, moving averages or low-pass filters are used to effectively filter out high-frequency random components, so that the true dynamic trend of the data sequence can be clearly presented. The cleaned data is normalized to map it to a uniform numerical range in order to eliminate the influence of units.

[0030] The formula for normalizing the cleaned data is:

[0031] in: The collected coating stability characteristic data; This is to normalize the coating stability characteristic data; This serves as the minimum baseline value for coating stability characteristic data; This represents the maximum baseline value for coating stability characteristic data; During the normalization process, a corresponding static reference value needs to be selected for each coating stability characteristic data involved in the calculation. This ensures that data of different dimensions can be uniformly mapped to the same scale range, thereby eliminating the influence caused by differences in units or orders of magnitude. The static reference value refers to the standard reference range determined based on the data distribution within a historical stable process window. The specific acquisition process is as follows: First, retrieve the real-time datasets of coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness that have been cleaned under the current operating conditions. Ensure that the data used for statistics comes from a period of stable process performance and is sufficiently representative.

[0032] For each coating stability characteristic data point, its data sequence is iterated one by one. Statistical analysis is used to determine the maximum and minimum reference values ​​within the historical stable process window. These two values ​​constitute the upper and lower boundaries of the normalization conversion. This embodiment considers that process conditions may slowly drift with seasonal changes, equipment aging, or batch variations. A dynamic benchmark verification mechanism is also established, periodically re-evaluating and updating the benchmark values ​​based on the latest accumulated historical data distribution characteristics. This ensures that the normalization process always accurately reflects the current process environment and avoids data distortion due to outdated benchmark values.

[0033] The formula used to obtain the sag risk factor is:

[0034] in: S represents the risk coefficient of sag. H represents the normalized ambient humidity, with a value range of [0, 1]. η is the normalized viscosity of the coating, with a value range of [0, 1]. T is the normalized thermosetting temperature, with a value range of [0, 1]. R is the normalized substrate surface roughness, with a value range of [0, 1]. α, β, γ, and δ are the weighting coefficients for the normalized coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness, respectively. Furthermore, based on the physical mechanism of the coating process and practical experience, each weighting coefficient has a different degree of influence on the risk of sagging. β: Coating viscosity is the most direct factor determining flowability and is directly related to the coating's resistance to deformation under gravity. Even small changes in viscosity can lead to significant fluctuations in the risk of sagging, making it the most sensitive factor.

[0035] γ: The curing temperature shortens the coating flow time by accelerating curing and plays an important role in suppressing sagging. However, the curing process is also affected by factors such as heat conduction and coating thickness, and its effect is slightly lower than that of viscosity.

[0036] α: Ambient humidity indirectly changes the rheological properties of the coating by affecting the solvent evaporation rate. Its effect is relatively mild, and in actual production, humidity is usually controlled within a narrow range, so its sensitivity is low.

[0037] δ: Substrate surface roughness mainly contributes to adhesion, but adhesion has limited auxiliary effect in resisting gravitational flow, and roughness is often relatively stable during the process with a small range of variation, so its sensitivity is the lowest. Therefore, the weighting coefficients should satisfy the following order: β>γ>α>δ.

[0038] S3: Set the sagging risk threshold and initial single-coat thickness. Strictly compare the sagging risk coefficient calculated under current conditions with the sagging risk threshold to assess whether the current production conditions are within a safe and controllable range. If the sagging risk coefficient is less than or equal to the sagging risk threshold, it indicates that the combined influence of key parameters such as coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness is insufficient to cause sagging defects. The control logic will maintain its original strategy, without interfering with process parameters, and will directly process according to the initial single-coat thickness to ensure production efficiency and process stability.

[0039] If the sagging risk coefficient is greater than the sagging risk threshold, it means that the current working condition has exceeded the safety boundary and there is a high probability of sagging. At this time, the system will automatically trigger the coating thickness optimization process and enter the active intervention and correction stage. By dynamically adjusting the process parameters, the coating thickness of a single coating is reduced, thereby avoiding quality risks and ensuring the uniformity and reliability of the fluorescent layer coating. Set the sag risk threshold as The initial single coating thickness is set to As a baseline parameter during normal production, the sag risk coefficient S obtained from S2 is compared with the sag risk threshold. The logic for the comparison is as follows: like This indicates that the combined effects of key indicators such as coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness are within the allowable fluctuation range of the process design. At this point, the risk of sagging during the coating process is low and under control, requiring no adjustment of the coating parameters. In this situation, the control logic will maintain the original production strategy and directly apply the initial single-coat thickness. Perform coating processing; like If the current conditions exceed the safety limits and there is a high probability of sagging, the system will immediately interrupt the regular coating process and automatically initiate a coating thickness optimization process, entering the active intervention and correction phase. During this phase, the control system will immediately collect the current optimization parameters to provide a basis for subsequent dynamic adjustments, thereby reducing the thickness of each coating pass to avoid sagging defects and ensure product quality.

[0040] Including convective risk threshold Acquisition process: Multiple sets of coating stability characteristic data from historical process production were collected, and the actual sagging observation results corresponding to each set of data were recorded, including samples with normal coating completion and samples that reached the critical sagging state but had not yet formed obvious defects. These data were then aggregated to construct a sample set that comprehensively covers normal and critical sagging conditions, ensuring the comprehensiveness and representativeness of subsequent statistical analysis. All data in the sample set were preprocessed by normalization to eliminate the influence of dimensional differences between different parameters. These data were then substituted into the relational expression in step S2 to calculate the sagging risk coefficient S corresponding to each sample set.

[0041] Statistical analysis was conducted on samples under critical sagging conditions to extract the minimum sagging risk coefficient among all critical samples. This minimum value was used as the initial reference threshold, representing the lowest boundary of the critical state. Subsequently, based on specific process quality standards and product reliability requirements, a certain safety margin was introduced to correct the initial reference threshold, ensuring sufficient buffer space in actual production to avoid entering the critical zone due to minor fluctuations.

[0042] The corrected threshold was verified through multiple production experiments, observing its warning accuracy and false alarm rate under different operating conditions. Based on the verification results, fine-tuning was performed until it was confirmed that the threshold could stably and effectively identify high-risk operating conditions, thus determining the final threshold to be used for real-time comparison. value.

[0043] Single coating thickness The acquisition process is as follows: Based on historical coating data in the process database, the optimal coating thickness under standard operating conditions is extracted as a benchmark value. The benchmark value must meet the process requirements of uniform fluorescent layer coverage and no sagging defects. Combined with the specifications of the target product and the material properties of the fluorescent paste, the benchmark value is compensated and calibrated to eliminate the impact of batch differences. The coating effect is verified through coating experiments.

[0044] Specifically, the selected benchmark value must meet the process requirements of uniform phosphor layer coverage and no drip defects. That is, when coating at this thickness, the phosphor paste should form a continuous, flat thin layer on the substrate surface with neat edges, and no localized accumulation or missed coating due to paste flow. Furthermore, the benchmark value needs to be compensated and calibrated based on the target product's chip size, substrate shape, final phosphor layer thickness requirements, and the actual material properties of the current batch of phosphor paste to eliminate the impact of raw material fluctuations or product differences between different production batches.

[0045] The initial single-coat thickness after compensation and calibration was verified through small-batch coating experiments. The actual coating effect was observed to ensure it met expectations, including coating uniformity, adhesion, and the presence of sagging defects. Based on the experimental results, the thickness value was fine-tuned until it consistently met quality requirements, at which point it could be used as a formal production parameter.

[0046] S4: In the coating thickness optimization process, optimization parameters are collected, including solvent evaporation rate, fluorescent paste settling velocity, and UV curing light intensity. The collection of these parameters relies on high-precision sensors and specialized equipment, representing an extension of the manufacturing of specialized semiconductor device equipment such as lithography machines and etching machines. This ensures the controllability and consistency of the coating process at the microscale. Based on the sagging risk coefficient and optimization parameters, a coating thickness optimization coefficient is calculated. This coefficient quantifies the degree to which the initial single-coat thickness is reduced. The formula used to calculate the coating thickness optimization coefficient is as follows:

[0047] in: k is the coating thickness optimization coefficient; V is the solvent evaporation rate. This is the safety boundary threshold for the solvent evaporation rate; U represents the settling velocity of the fluorescent paste. This represents the safety boundary threshold for the settling velocity of fluorescent paste. I represents the UV curing light intensity. The safe boundary threshold for UV curing light intensity; Based on process experiments, these represent the minimum allowable values ​​for each optimized parameter while ensuring coating quality.

[0048] S5: Establish a correction coupling formula, input the obtained coating thickness optimization coefficient and initial single coating thickness into the correction coupling formula, calculate the single coating correction thickness of the fluorescent layer, and use the single coating correction thickness of the fluorescent layer for coating process production.

[0049] The formula for correcting coupling is:

[0050] in, The corrected thickness for a single coating of the fluorescent layer is a key process parameter used to guide actual production. A larger k value indicates a more unfavorable combined effect from optimized parameters such as solvent evaporation rate, fluorescent paste settling speed, and UV curing light intensity, or a relatively higher risk of sagging. Therefore, the initial single coating thickness needs to be adjusted downwards accordingly, resulting in a calculated corrected thickness. The smaller the size, the better to effectively avoid the occurrence of drip defects; If k is close to 1, it means that the current optimization parameters are in an ideal state, and the risk of sagging has little constraint on the thickness. At this time, the single coating correction thickness of the fluorescent layer will be close to the initial single coating thickness, and almost no adjustment is needed. The single-coat correction thickness of the phosphor layer is used as the execution parameter of the actual coating process and directly input into the production control unit. This precisely guides the sequential coating operation under the current conditions, ensuring that each substrate can be processed under dynamically optimized parameters. This process embodies the goal of methods or equipment specifically designed for manufacturing or processing semiconductors or solid-state devices or their components, providing stable and reliable process assurance for high-precision packaged products such as dedicated chips. The calculated single-coat correction thickness of the phosphor layer, its corresponding sagging risk coefficient, and various optimized parameters are automatically recorded and included in the process database as historical data archives. This provides reliable data support for rapid response to similar conditions and continuous improvement of the process window in the future.

[0051] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0052] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0053] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0054] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A phosphor layer coating process for a chip packaging substrate, characterized in that, Includes the following steps: S1: Collect coating stability characteristic data, including coating viscosity, thermosetting temperature, ambient humidity and substrate surface roughness; S2: Preprocess the coating stability characteristic data, construct the relational expression of the coating stability characteristic data, and input the coating viscosity, thermosetting temperature and ambient humidity and substrate surface roughness under the current working conditions into the relational expression to obtain the sagging risk coefficient; S3: Set the sagging risk threshold and the initial single coating thickness, compare the sagging risk coefficient with the sagging risk threshold. If the sagging risk coefficient is less than or equal to the sagging risk threshold, the control logic will maintain the original strategy and process directly according to the initial single coating thickness. If the sagging risk coefficient is greater than the sagging risk threshold, the coating thickness optimization process will be triggered, and the active intervention and correction stage will be entered. S4: In the coating thickness optimization process, optimization parameters are collected, including solvent evaporation rate, fluorescent paste settling rate and UV curing light intensity. Based on the sagging risk coefficient and optimization parameters, the coating thickness optimization coefficient is calculated and obtained. The coating thickness optimization coefficient is used to quantify the degree of reduction of the initial single coating thickness. S5: Establish a correction coupling formula, input the obtained coating thickness optimization coefficient and initial single coating thickness into the correction coupling formula, calculate the single coating correction thickness of the fluorescent layer, and use the single coating correction thickness of the fluorescent layer for coating process production.

2. The phosphor layer coating process for a chip packaging substrate according to claim 1, characterized in that: In step S1, during the preparation phase before the coating operation begins, multiple sets of coating stability characteristic data related to coating quality are continuously collected. All collected coating stability characteristic data are aggregated into the process database, wherein: The viscosity of the coating is used to reflect the flow characteristics of the fluorescent paste at the current moment. It is obtained by real-time monitoring of the paste circulating from the feeding system. The change in the coating viscosity value is directly related to the spreadability of the paste on the substrate surface. The thermosetting temperature is obtained through an array of thermocouples distributed within the curing oven, which is used to characterize the uniformity of the temperature distribution and its consistency with the set value. Ambient humidity refers to the relative humidity of the air in the enclosed space where the coating operation is carried out. Fluctuations in ambient humidity affect the evaporation rate of the solvent in the slurry. The substrate surface roughness is recorded in the system after random sampling of the surface micromorphology of the current batch of substrates. The substrate surface roughness represents the interfacial characteristics of the substrate and the phosphor layer.

3. The phosphor layer coating process for a chip packaging substrate according to claim 2, characterized in that: It also includes a process for preprocessing coating stability characteristic data: First, the integrity of the multi-source data collected from the production site is verified. Outliers caused by instantaneous interference or collection failure are removed by setting a threshold range. For white noise or periodic fluctuations in the time series data, digital filtering algorithms are used for smoothing. High-frequency components are eliminated by moving average or low-pass filtering. The cleaned data is normalized to map it to a uniform numerical range.

4. The phosphor layer coating process for a chip packaging substrate according to claim 3, characterized in that: The formula for normalizing the cleaned data is: in: The collected coating stability characteristic data; This is to normalize the coating stability characteristic data; Minimum baseline value for each coating stability feature data; The maximum baseline value for each coating stability feature data; During the normalization process, a corresponding static reference value is selected for each coating stability feature data. The process for obtaining the static reference value is as follows: retrieve the real-time datasets of the coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness that have been cleaned under the current working conditions; for each coating stability feature data, traverse its data sequence to determine the maximum and minimum reference values ​​under the historical stable process window; and establish a dynamic verification mechanism for the reference value, updating the reference value periodically based on the distribution characteristics of historical data.

5. The phosphor layer coating process for a chip packaging substrate according to claim 2, characterized in that: The formula used to obtain the sag risk factor is: in: S represents the risk coefficient of sag. H represents the normalized ambient humidity, with a value range of [0, 1]. η is the normalized viscosity of the coating, with a value range of [0, 1]. T is the normalized thermosetting temperature, with a value range of [0, 1]. R is the normalized substrate surface roughness, with a value range of [0, 1]. α, β, γ, and δ are the weighting coefficients for the normalized coating viscosity, thermosetting temperature, ambient humidity, and substrate surface roughness, respectively. Based on the physical mechanism of the coating process and practical experience, the influence of each weighting coefficient on the risk of sagging varies, and the weighting coefficients satisfy the following relationship: β > γ > α > δ.

6. The phosphor layer coating process for a chip packaging substrate according to claim 5, characterized in that: In S3, the sag risk threshold is set as follows: The initial single coating thickness is set to The sag risk coefficient S obtained from S2 is compared with the sag risk threshold. The logic for the comparison is as follows: like This indicates that the risk of sagging under the current operating conditions is within a controllable range and no intervention is required. At this time, the control logic maintains the original strategy and directly applies the initial single-coat thickness. Perform coating processing; like If the risk of sagging is too high, the coating thickness optimization process is initiated, and the system enters the active intervention and correction stage, immediately collecting optimization parameters.

7. The phosphor layer coating process for a chip packaging substrate according to claim 6, characterized in that: It also includes the convective risk threshold. Acquisition process: Multiple sets of coating stability characteristic data and corresponding sagging observation results from historical process production were collected as samples to construct a sample set including normal operating conditions and critical sagging conditions. After normalizing and preprocessing the sample data, the data were substituted into the relational expression in step S2 to calculate the sagging risk coefficient of each sample. And statistical analysis of critical flow conditions The minimum value is used as the initial reference threshold. A safety margin is introduced to correct the reference threshold, and the effectiveness of the reference threshold is verified through multiple experiments. After the effectiveness verification, the reference threshold is used for real-time comparison. value.

8. The phosphor coating process for a chip packaging substrate according to claim 6, characterized in that: The thickness of a single coating The acquisition process is as follows: Based on historical coating data in the process database, the optimal coating thickness under standard operating conditions is extracted as a benchmark value. The benchmark value must meet the process requirements of uniform fluorescent layer coverage and no sagging defects. The benchmark value is compensated and calibrated to eliminate the influence of batch differences. The coating effect of the optimal coating thickness is verified through coating experiments.

9. The phosphor layer coating process for a chip packaging substrate according to claim 1, characterized in that: The formula used to calculate the coating thickness optimization coefficient is as follows: in: k is the coating thickness optimization coefficient; V is the solvent evaporation rate. This is the safety boundary threshold for the solvent evaporation rate; U represents the settling velocity of the fluorescent paste. This represents the safety boundary threshold for the settling velocity of fluorescent paste. I represents the UV curing light intensity. The safe boundary threshold for UV curing light intensity; Based on process experiments, these represent the minimum allowable values ​​for each optimized parameter while ensuring coating quality.

10. The phosphor layer coating process for a chip packaging substrate according to claim 9, characterized in that: The corrective coupling formula is: in, The corrected thickness for a single coating of the fluorescent layer is determined by the value of k. A larger k value indicates a greater reduction in the initial single coating thickness, resulting in a smaller corrected thickness. If k is close to 1, the corrected thickness for a single coating of the fluorescent layer is close to the initial single coating thickness. The corrected thickness for a single coating of the fluorescent layer is input into the production control unit as an execution parameter of the actual coating process to guide the sequential coating operation under the current working conditions. At the same time, the system automatically records the corrected thickness for a single coating of the fluorescent layer and its corresponding sagging risk coefficient and optimization parameters, and incorporates them into the process database as historical data.

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