Automatic industrial control system for HDI board solder mask exposure process

Abstract

An HDI board solder mask exposure process automation industrial control system of the present invention belongs to the industrial automation control technology field, and comprises a controller which is used for executing the following steps: step a: collecting process disturbance data and process core state data; step b, based on process disturbance data and process core state data; c, cooperatively controlling an energy execution unit and a vacuum execution unit; and d, collecting process quality data representing exposure quality after exposure, and correcting the decision logic of the target compensation energy and the dynamic vacuum stable target generated in the step b based on the process quality data. The cooperative control model of the scheme ensures that the exposure energy is increased, and meanwhile, the adhesion force is maintained or enhanced by synchronously improving the vacuum level, so that the contradiction between the heat effect and the vacuum stability is solved from a physical mechanism, and the bubble defect is effectively prevented from being formed.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation control, in particular to an automated industrial control system for an HDI board solder mask exposure process. Background Art

[0002] High-density interconnect (HDI) boards are key components of modern electronic products. In their manufacturing, the solder mask exposure process plays a decisive role in the quality of the final product. This process uses ultraviolet light to cure solder mask ink to form a pattern that protects the circuit. Existing technologies usually use an open-loop control method with fixed exposure energy and vacuum level. This method has significant defects when facing a changing production environment.

[0003] When the ambient temperature and humidity in a production workshop rise and the air cleanliness deteriorates, the HDI substrate expands and contracts with heat, the exposed film deforms due to moisture absorption, and dust contamination causes light energy attenuation. To compensate for these effects, operators typically increase the exposure energy, but this generates more heat, exacerbating the thermal expansion difference between the substrate and the film, thereby disrupting the established vacuum environment and causing vacuum fluctuations. This "energy increase-vacuum fluctuation" contradiction ultimately prevents the substrate and film from fitting tightly together, forming tiny bubbles, resulting in blurred edges of the exposed pattern and a sharp increase in the line defect rate from 0.3% to 1.5%, seriously affecting product yield. Therefore, the field urgently needs a closed-loop control system that can actively adapt to environmental changes and resolve this contradiction.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The present invention aims to provide an automated industrial control system for the HDI board solder mask exposure process that can overcome the defects of the prior art and solve the problems of reduced exposure quality and yield rate caused by environmental disturbances.

[0006] The technical solution of the present invention is: it includes a controller, and a sensor, an energy execution unit and a vacuum execution unit electrically connected to the controller, and the controller is used to perform the following steps: Step a: collecting process disturbance data and process core state data, wherein the process disturbance data is used to characterize the disturbance factor of the exposure environment, and the process core state data is used to characterize the operating state in an exposure chamber; Step b: generating a target compensation energy for compensating the disturbance factor and a dynamic vacuum stabilization target for coordinating the target compensation energy based on the process disturbance data and the process core state data; Step c: cooperatively controlling the energy execution unit and the vacuum execution unit so that the actual integrated energy during the exposure process approaches the target compensation energy, and the vacuum degree of the exposure chamber is stabilized at a dynamic vacuum stabilization target; Step d: After exposure, process quality data characterizing the exposure quality is collected, and based on the process quality data, the decision logic for generating the target compensation energy and the dynamic vacuum stabilization target in step b is corrected.

[0007] The process disturbance data includes ambient temperature, humidity and air dust concentration that characterize the exposure environment; the process core state data includes substrate surface temperature and chamber vacuum pressure collected by sensors deployed in the exposure chamber.

[0008] In this embodiment, in step b, the controller is specifically configured to: Sb1: Based on the process disturbance data and the process core state data, generating a comprehensive disturbance index for quantifying the degree to which the current operating condition deviates from the ideal state; Sb2: Adjusting a preset baseline exposure energy based on the comprehensive disturbance index to generate the target compensation energy, and adjusting a preset baseline vacuum target based on the difference between the target compensation energy and the baseline exposure energy to generate the dynamic vacuum stabilization target.

[0009] In this embodiment, the comprehensive disturbance index is generated by weighted summing at least three disturbance factors: a thermal deformation factor calculated based on the difference between the substrate surface temperature and a preset ideal substrate temperature; a moisture-induced deformation factor calculated based on the ambient temperature and humidity; and a dust shielding factor calculated based on the air dust concentration.

[0010] In this embodiment, the generation of the dynamic vacuum stabilization target utilizes a preset energy-vacuum coupling coefficient to determine the vacuum degree adjustment amount corresponding to the increment of the target compensation energy.

[0011] In this embodiment, in step c, the controller is specifically used to: adopt a PID control algorithm, use the dynamic vacuum stabilization target as a set value, use the cavity vacuum pressure as a process value, and perform closed-loop control on the vacuum execution unit.

[0012] In this embodiment, the process quality data is obtained by performing online optical inspection on the HDI board after exposure, which at least includes the bubble area used to characterize the tightness of the bonding and the pattern edge blur used to characterize the pattern accuracy.

[0013] In this embodiment, in step d, the controller is specifically used to: when the process quality data exceeds a preset quality threshold, correct the weight coefficients of the thermal deformation factor, moisture-induced deformation factor and dust shielding factor, and correct the energy-vacuum coupling coefficient.

[0014] The present invention provides a method for improving the prior art, which has the following improvements and advantages compared with the prior art: The system is generating At the same time, according to the formula , using the preset energy-vacuum coupling coefficient Calculate the vacuum adjustment amount.

[0015] like If the vacuum target is 0.1kPa / (mJ / cm²), the vacuum target needs to be reduced by 0.1*(120-100)=2kPa. The system automatically sets the vacuum target from the baseline of -95kPa to -97kPa. The vacuum control unit drives the actuator based on this new target, generating a greater negative pressure by increasing the air pump. The adsorption force generated by this negative pressure is sufficient to offset the expansion effect caused by the added heat. The collaborative control model of this solution ensures that the bonding force is maintained or enhanced by simultaneously increasing the vacuum level while increasing the exposure energy. This physically resolves the contradiction between thermal effects and vacuum stability and effectively prevents the formation of bubble defects.

[0016] In step d, the system detects the fuzziness of the edge of the graphics by continuously acquiring process quality data online. There is a trend of continuously exceeding the threshold. The controller determines that this is a systematic drift and immediately starts the optimization algorithm to adjust the relevant parameters in the decision model, such as the weight coefficient of each factor or the energy-vacuum coupling coefficient. , automatically correct the subsequent calculations Improve systematically to compensate for the light decay of the lamp.

[0017] The existing technology is an execution system that lacks real-time perception and learning capabilities. This solution is an intelligent control system that integrates real-time perception, feedforward decision-making, collaborative execution and feedback optimization capabilities. Through the internal and external double closed-loop structure, it can not only respond to instantaneous changes in working conditions, but also perform self-correction through long-term data accumulation, thus achieving process adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further explained below in conjunction with the accompanying drawings and Examples: Figure 1 The present invention is a flowchart of an automated industrial control system for an HDI board solder mask exposure process. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments. Example

[0020] See also Figure 1 The present invention provides a technical solution: an automated industrial control system for the HDI board solder mask exposure process, comprising a controller, and a sensor, an energy execution unit, and a vacuum execution unit electrically connected to the controller, wherein the controller is configured to perform the following steps: Step a: collecting process disturbance data and process core state data, wherein the process disturbance data is used to characterize the disturbance factor of the exposure environment, and the process core state data is used to characterize the operating state in an exposure chamber; This process forms the perception foundation of the entire closed-loop control system. Sensors collect two key data types in real time: "process disturbance data" representing changes in the external environment and "process core status data" representing the internal state of the equipment. This transforms traditional technologies, which rely on manual experience and fixed parameters, by providing the system with comprehensive, real-time, quantitative understanding of current operating conditions. This beneficial effect is that it provides precise, real-time input for subsequent predictive compensation, a prerequisite for transitioning from passive response to proactive preventive control.

[0021] Step b: generating a target compensation energy for compensating the disturbance factor and a dynamic vacuum stabilization target for coordinating the target compensation energy based on the process disturbance data and the process core state data; Step b is the core decision-making process of this invention, embodying the principle of feedforward control. Based on the data collected in step a, the controller predicts the impact of environmental disturbances on the exposure process, such as energy attenuation and thermal deformation, and proactively calculates two coordinated control objectives.

[0022] Target compensation energy: used to actively compensate for energy loss caused by factors such as dust obstruction and medium absorption.

[0023] Dynamic vacuum stabilization target: This is the key to resolving the core contradiction between "energy boost and vacuum fluctuation." It is not a fixed value, but a vacuum target that is dynamically adjusted based on the increment of "target compensation energy."

[0024] The two originally conflicting control variables, energy and vacuum, are transformed into a synergistic relationship. While increasing the energy to ensure sufficient exposure, the vacuum degree is actively deepened to combat the risk of unstable fitting caused by thermal effects, thereby achieving decoupling control of complex coupling effects.

[0025] Step c: cooperatively controlling the energy execution unit and the vacuum execution unit so that the actual integrated energy during the exposure process approaches the target compensation energy, and the vacuum degree of the exposure chamber is stabilized at a dynamic vacuum stabilization target; Step c is the execution phase of the decision. It emphasizes the coordinated control of the energy and vacuum subsystems, ensuring the precise and synchronous achievement of the two dynamic targets generated in step b. The controller sends instructions to the energy and vacuum execution units, respectively, ensuring that the actual integrated energy and chamber vacuum level strictly follow the dynamic target values ​​throughout the exposure process.

[0026] Step c ensures the effective implementation of feedforward decisions, avoids control failures due to execution lag or incoordination, and is a physical guarantee for ensuring the final exposure quality.

[0027] Step d: After exposure, process quality data characterizing the exposure quality is collected, and based on the process quality data, the decision logic for generating the target compensation energy and the dynamic vacuum stabilization target in step b is corrected.

[0028] Step d forms the system's outer optimization loop, endowing it with self-learning and adaptive capabilities. After each exposure cycle, the system obtains the actual quality results of that exposure through online testing and correlates them with control decisions. If the quality results are unsatisfactory, the system reverses the decision logic from step b, specifically the model parameters.

[0029] Step d enables the system to adapt to slowly changing operating conditions such as equipment aging and material batch differences, and ensures the long-term stability and accuracy of the control system through continuous self-optimization.

[0030] The process disturbance data includes ambient temperature, humidity and air dust concentration that characterize the exposure environment; the process core state data includes substrate surface temperature and chamber vacuum pressure collected by sensors deployed in the exposure chamber.

[0031] The decision-making process consists of two steps. First, a model aggregates multi-dimensional input data—process disturbance data and core process status data—into a single, quantifiable metric: the comprehensive disturbance index. This index intuitively reflects the severity of the current operating conditions. Second, this index is used to adjust the preset baseline exposure energy and baseline vacuum targets to generate the final control target.

[0032] This two-step approach structures and models the complex multivariable decision-making process. It begins with state assessment, generates indices, and then adjusts the target. This approach is logically clear, easy to implement and adjust, and provides a clear target for subsequent adaptive optimization.

[0033] In this embodiment, in step b, the controller is specifically configured to: Sb1: Based on the process disturbance data and the process core state data, generating a comprehensive disturbance index for quantifying the degree to which the current operating condition deviates from the ideal state; Sb2: Adjusting a preset baseline exposure energy based on the comprehensive disturbance index to generate the target compensation energy, and adjusting a preset baseline vacuum target based on the difference between the target compensation energy and the baseline exposure energy to generate the dynamic vacuum stabilization target.

[0034] In this embodiment, the comprehensive disturbance index is generated by weighted summing at least three disturbance factors: a thermal deformation factor calculated based on the difference between the substrate surface temperature and a preset ideal substrate temperature; a moisture-induced deformation factor calculated based on the ambient temperature and humidity; and a dust shielding factor calculated based on the air dust concentration.

[0035] The calculation steps of the comprehensive disturbance index are as follows: The index is obtained by weighted summing the three factors that are strongly related to the physical scene: thermal deformation factor, moisture deformation factor and dust shielding factor.

[0036] Thermal deformation factor: , quantified the bonding risk caused by substrate overheating.

[0037] Hygroscopic deformation factor: , quantifying the lamination risk caused by moisture deformation of the backsheet and demonstrating the exacerbating effect of high temperature.

[0038] Dust obscuration factor: , quantified the energy compensation requirements caused by dust pollution.

[0039] Formula and parameters: The calculation formula of the comprehensive disturbance index is: in: : comprehensive disturbance index (dimensionless).

[0040] : The calculation results of the above three factors are normalized and are dimensionless values.

[0041] : correspond to the weight coefficients of the three factors respectively), whose sum is 1, and is corrected by the optimization logic of step d.

[0042] : These are all model coefficients preset or calibrated by the system.

[0043] This approach avoids complex, general-purpose mathematical models and instead employs a parameterized model that is strongly tied to the physical scenario. Each factor has a clear physical meaning, making the model highly interpretable, and its weight coefficients can be optimized in subsequent steps, achieving model adaptability.

[0044] The index is calculated before exposure. Based on this index, the system can predict impending production risks, such as insufficient energy and loose fit, and proactively compensate for them. This constitutes the system's feedforward control loop, enabling a shift from passive response to proactive prevention. It provides a single input for subsequent decision-making: complex environmental data is "reduced" to a single number, the ICD, greatly simplifying subsequent control logic. The subsequent energy and vacuum targets will be adjusted based on this unified index.

[0045] In this embodiment, the generation of the dynamic vacuum stabilization target utilizes a preset energy-vacuum coupling coefficient to determine the vacuum degree adjustment amount corresponding to the increment of the target compensation energy.

[0046] The calculation steps of the dynamic vacuum stabilization target are as follows: The calculation of this target value introduces the core parameter energy-vacuum coupling coefficient ( ).

[0047] Formula and parameters: The calculation formula is: in: : Dynamic vacuum stability target, unit: Pa.

[0048] : Reference vacuum target, unit: Pa.

[0049] : Target compensation energy, unit: mJ / cm².

[0050] : Base exposure energy, unit: mJ / cm².

[0051] : Energy-vacuum coupling coefficient, unit: Pa / (mJ / cm²).

[0052] This formula is a mathematical embodiment of the solution to the "energy increase-vacuum fluctuation" contradiction. It accurately quantifies the destructive effect of energy increment on vacuum and gives the corresponding compensation amount, so that the deepening of vacuum is proportional to the energy increase, thus achieving the coordinated control of the two. The same optimization can be performed by step d.

[0053] like If the vacuum target is 0.1kPa / (mJ / cm²), the vacuum target needs to be lowered by 0.1*(120-100)=2kPa. The system automatically sets the vacuum target from the baseline of -95kPa to -97kPa. The vacuum control unit drives the actuator based on this new target, increasing the vacuum to generate a greater negative pressure. This negative pressure generates a sufficient suction force to offset the expansion effect caused by the added heat.

[0054] In this embodiment, in step c, the controller is specifically used to: adopt a PID control algorithm, use the dynamic vacuum stabilization target as a set value, use the cavity vacuum pressure as a process value, and perform closed-loop control on the vacuum execution unit.

[0055] The specific implementation of vacuum control in step c is detailed below: The classic PID control algorithm is used. The "dynamic vacuum stability target" calculated in step b is used as the set value of the PID controller, and the "cavity vacuum pressure" collected in real time by the sensor is used as the process value. The vacuum actuator is continuously adjusted by the algorithm.

[0056] PID is a mature, stable, and reliable control algorithm that enables fast, precise tracking of targets without overshoot. Its application ensures that the vacuum level is consistently and stably controlled near the dynamically changing target value throughout the exposure process, ensuring the effectiveness of coordinated control.

[0057] In this embodiment, the process quality data is obtained by performing online optical inspection on the HDI board after exposure, which at least includes the bubble area used to characterize the tightness of the bonding and the pattern edge blur used to characterize the pattern accuracy.

[0058] In this embodiment, in step d, the controller is specifically used to: when the process quality data exceeds a preset quality threshold, correct the weight coefficients of the thermal deformation factor, moisture-induced deformation factor and dust shielding factor, and correct the energy-vacuum coupling coefficient.

[0059] Detailed description of the content of "process quality data" in step d: This data specifically refers to the bubble area and graphic edge blur obtained through online optical detection.

[0060] These two indicators directly correspond to the core defects described in the background technology: bubbles and blurred edges, making the optimization goal of step d very concrete. The system can clearly know whether the fitting problem or insufficient / uneven energy problem is dominant, thus providing a clear and targeted direction for subsequent model corrections.

[0061] When the detected process quality data exceeds the preset threshold, the controller will start the optimization algorithm to fine-tune two key parameters in the upstream decision model: one is the weight coefficient of the three major factors , and the second is the energy-vacuum coupling coefficient This constraint details the implementation of the adaptive optimization closed loop. It enables the system to intelligently adjust its internal model based on the final product quality. For example, if the system detects that the bubble area exceeds the standard, it will determine that thermal effects are the main cause and increase the weight of the thermal deformation factor. and the energy-vacuum coupling coefficient , so that the system can make more accurate compensation when facing similar working conditions in the future.

[0062] The existing system will continue to operate according to the preset, fixed exposure energy and vacuum parameters. Due to the mismatch between the actual working conditions and the set parameters, the substrate and film will deform due to heat and moisture effects, and dust particles will cause energy attenuation. This will lead to widespread circuit defects in this batch of products caused by poor bonding and insufficient exposure, and the defect rate will increase from 0.3% to 1.5%. In this solution, the system collects real-time data through sensors before the process begins. 、 and The controller executes step b and calculates a higher comprehensive disturbance index based on the process disturbance data. Based on this, the controller generates a target compensation energy higher than the reference value To compensate for energy attenuation and simultaneously generate a dynamic vacuum stability target with a lower absolute pressure value To combat deformation, this batch of products was exposed using dynamically calculated compensatory process parameters tailored to the current operating conditions. This solution proactively adjusts process parameters through feedforward control before the physical conditions for defect formation arise, ensuring consistent and accurate exposure under varying environmental conditions. This ensures a stable line defect rate below the target of 0.3%.

[0063] It should be noted that all calculation formulas in this application document utilize, including but not limited to, regression analysis within machine learning algorithms to deeply analyze the collected parameters and identify their natural trends and interrelationships. Professional software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Model performance is then objectively evaluated through methods such as cross-validation, combined with continuous feedback and optimization to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their validity and accuracy, and ensuring that the calculation process complies with the constraints of natural laws rather than being based on artificially set rules.

[0064] The technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0065] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An HDI board solder mask exposure process automation industrial control system, comprising a controller, and a sensor, an energy execution unit and a vacuum execution unit electrically connected to the controller, characterized in that: The controller is configured to perform the following steps: Step a: collecting process disturbance data and process core state data, wherein the process disturbance data is used to characterize the disturbance factor of the exposure environment, and the process core state data is used to characterize the operating state in an exposure chamber; Step b: generating a target compensation energy for compensating the disturbance factor and a dynamic vacuum stabilization target for coordinating the target compensation energy based on the process disturbance data and the process core state data; Step c: cooperatively controlling the energy execution unit and the vacuum execution unit so that the actual integrated energy during the exposure process approaches the target compensation energy, and the vacuum degree of the exposure chamber is stabilized at a dynamic vacuum stabilization target; Step d: After exposure, process quality data characterizing the exposure quality is collected, and based on the process quality data, the decision logic for generating the target compensation energy and the dynamic vacuum stabilization target in step b is corrected.

2. The HDI board solder mask exposure process automation industrial control system according to claim 1, characterized in that: The process disturbance data includes ambient temperature, humidity and air dust concentration that characterize the exposure environment; the process core state data includes substrate surface temperature and chamber vacuum pressure collected by sensors deployed in the exposure chamber.

3. The HDI board solder mask exposure process automation industrial control system according to claim 2, characterized in that: In step b, the controller is specifically configured to: Sb1: Based on the process disturbance data and the process core state data, generating a comprehensive disturbance index for quantifying the degree to which the current operating condition deviates from the ideal state; Sb2: Adjusting a preset baseline exposure energy based on the comprehensive disturbance index to generate the target compensation energy, and adjusting a preset baseline vacuum target based on the difference between the target compensation energy and the baseline exposure energy to generate the dynamic vacuum stabilization target.

4. The HDI board solder mask exposure process automation industrial control system according to claim 3, characterized in that: The comprehensive disturbance index is generated by weighted summing at least three disturbance factors: a thermal deformation factor calculated based on the difference between the substrate surface temperature and a preset ideal substrate temperature; and a moisture deformation factor calculated based on the ambient temperature and humidity. and a dust obscuration factor calculated based on the airborne dust concentration.

5. The HDI board solder mask exposure process automation industrial control system according to claim 3, characterized in that: The generation of the dynamic vacuum stabilization target utilizes a preset energy-vacuum coupling coefficient to determine the vacuum degree adjustment amount corresponding to the increment of the target compensation energy.

6. The HDI board solder mask exposure process automation industrial control system according to claim 3, characterized in that: In step c, the controller is specifically configured to: adopt a PID control algorithm, use the dynamic vacuum stabilization target as a set value, and use the cavity vacuum pressure as a process value to perform closed-loop control on the vacuum execution unit.

7. The HDI board solder mask exposure process automation industrial control system according to claim 4, characterized in that: The process quality data is obtained by performing online optical inspection on the HDI board after exposure, and includes at least the bubble area used to characterize the tightness of the bonding and the pattern edge blur used to characterize the pattern accuracy.

8. The HDI board solder mask exposure process automation industrial control system according to claim 7, characterized in that: In step d, the controller is specifically used to: when the process quality data exceeds a preset quality threshold, correct the weight coefficients of the thermal deformation factor, moisture-induced deformation factor and dust shielding factor, and correct the energy-vacuum coupling coefficient.

Images