An automated industrial control system for the solder mask exposure process of HDI boards

Abstract

This invention discloses an automated industrial control system for the HDI board solder resist exposure process, belonging to the field of industrial automation control technology. It includes a controller that executes the following steps: Step a: acquiring process disturbance data and core process state data; Step b: based on the process disturbance data and core process state data; Step c: coordinating the control of the energy execution unit and the vacuum execution unit; Step d: acquiring process quality data characterizing the exposure quality after exposure, and correcting the decision logic for generating the target compensation energy and dynamic vacuum stability target in step b based on the process quality data. The cooperative control model of this solution ensures that while increasing the exposure energy, the adhesion force is maintained or enhanced by simultaneously increasing the vacuum level, thus resolving the contradiction between thermal effects and vacuum stability from a physical mechanism perspective and effectively preventing the formation of bubble defects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial automation control, in particular to an HDI board anti-soldering exposure process automation industrial control system. BACKGROUND

[0002] High-density interconnection (HDI) board is a key component of modern electronic products, and in its manufacturing, the anti-soldering exposure process plays a decisive role in the quality of the final product. This process uses ultraviolet light to cure anti-soldering ink to form a pattern that protects the circuit. The existing technology usually uses an open-loop control method that sets a fixed exposure energy and vacuum degree. This method has significant defects when faced with changing production environments.

[0003] When the environmental temperature and humidity of the production workshop increase and the air cleanliness deteriorates, it can cause thermal expansion and contraction of the HDI substrate, moisture absorption deformation of the exposure film, and light energy attenuation caused by dust pollution. To compensate for these effects, operators usually 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 fluctuations in the vacuum degree. This "energy increase-vacuum fluctuation" contradiction ultimately prevents the substrate and the film from being closely attached, forming small air bubbles that cause the edges of the exposed pattern to be blurred and the line defect rate to rise sharply from 0.3% to 1.5%, severely affecting product yield. Therefore, there is an urgent need in the art for a closed-loop control system that can actively adapt to environmental changes and solve the above-mentioned contradictions.

[0004] The above information disclosed in the above BACKGROUND section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application aims to provide an HDI board anti-soldering exposure process automation industrial control system that can overcome the defects of the prior art to solve the problem of exposure quality degradation and yield reduction caused by environmental disturbances.

[0006] The technical solution of the present application is as follows: it includes a controller, and a sensor, an energy execution unit and a vacuum execution unit electrically connected to the controller, the controller is used to execute the following steps:

[0007] Step a: collect process disturbance data and process core state data, the process disturbance data is used to represent the disturbance factors of the exposure environment, and the process core state data is used to represent the running state in an exposure cavity;

[0008] Step b: based on the process disturbance data and the process core state data, generate a target compensation energy for compensating for the disturbance factors, and a dynamic vacuum stabilization target for coordinating the target compensation energy;

[0009] Step c: cooperatively control the energy execution unit and the vacuum execution unit to make the actual integral energy in the exposure process approach the target compensation energy and to stabilize the vacuum degree of the exposure cavity at the dynamic vacuum stabilization target;

[0010] Step d: collect process quality data representing exposure quality after exposure and modify the decision logic of generating the target compensation energy and the dynamic vacuum stabilization target in step b based on the process quality data.

[0011] The process disturbance data includes ambient temperature and humidity and air dust concentration representing the exposure environment; and the process core state data includes substrate surface temperature and cavity vacuum pressure collected by sensors deployed in the exposure cavity.

[0012] In the embodiment, in the step b, the controller is specifically configured to:

[0013] Sb1: generate a comprehensive disturbance index for quantifying the degree of deviation of the current working condition from the ideal state based on the process disturbance data and the process core state data;

[0014] Sb2: adjust a preset reference exposure energy based on the comprehensive disturbance index to generate the target compensation energy, and adjust a preset reference vacuum target based on the difference between the target compensation energy and the reference exposure energy to generate the dynamic vacuum stabilization target.

[0015] In the embodiment, the comprehensive disturbance index is generated by weighted summation of at least three disturbance factors, including a thermal deformation factor calculated based on the difference between the substrate surface temperature and a preset ideal substrate temperature, a humidity-induced deformation factor calculated based on the ambient temperature and humidity, and a dust shielding factor calculated based on the air dust concentration.

[0016] In the embodiment, the generation of the dynamic vacuum stabilization target uses a preset energy-vacuum coupling coefficient to determine the vacuum degree adjustment amount corresponding to the increment of the target compensation energy.

[0017] In the embodiment, in the step c, the controller is specifically configured to: use a PID control algorithm to take the dynamic vacuum stabilization target as a set value, take the cavity vacuum pressure as a process value, and perform closed-loop control on the vacuum execution unit.

[0018] In the embodiment, the process quality data is obtained by online optical detection of the HDI board after exposure, and at least includes bubble area for representing close-fitting density and pattern edge blur for representing pattern accuracy.

[0019] In the step d, the controller is specifically configured to correct the weight coefficients of the thermal deformation factor, the humidity deformation factor and the dust shielding factor, and correct the energy-vacuum coupling coefficient when the process quality data exceeds the preset quality threshold.

[0020] The application improves the HDI board anti-welding exposure process automation industrial control system, and has the following improvements and advantages compared with the prior art.

[0021] The system generates at the same time, according to the formula , the adjustment amount of the vacuum degree is calculated by using the preset energy-vacuum coupling coefficient , wherein is a dynamic vacuum stability target, is a reference vacuum target, is a target compensation energy, is a reference exposure energy.

[0022] If is 0.1 kPa / (mJ / cm²), the vacuum degree target needs to be reduced by 0.1*(120-100)=2 kPa. The system automatically sets the vacuum target from the reference-95 kPa to-97 kPa. The vacuum control unit drives the actuator according to the new target, and generates greater negative pressure by enhancing the air exhaust, and the adsorption force generated by the negative pressure is sufficient to offset the expansion effect caused by the increased heat. The synergistic control model of the scheme ensures that the exposure energy is increased while the vacuum level is simultaneously improved to maintain or enhance the adhesion, which physically solves the contradiction between thermal effect and vacuum stability, and effectively prevents the formation of air bubble defects.

[0023] In step d, the system continuously acquires process quality data by online detection, and identifies that the graphic edge blur exists a trend of continuously exceeding the threshold. The controller determines that this is a systematic drift, and then starts the optimization algorithm to automatically correct the related parameters in the decision model, such as the weight coefficients of various factors or the energy-vacuum coupling coefficient , so that the subsequent calculation of systematically improves, thereby compensating for the light decay of the lamp tube.

[0024] The prior art is an execution system lacking real-time perception and learning ability, and the present scheme is an intelligent control system integrating real-time perception, feedforward decision, synergistic execution and feedback optimization capabilities. Through the internal and external double closed-loop structure, not only can it cope with instantaneous working condition changes, but also can be self-corrected through long-term data accumulation, realizing self-adaptation of the process. BRIEF DESCRIPTION OF DRAWINGS

[0025] The application will be further explained in connection with the accompanying drawings and embodiments:

[0026] Figure 1 is a flow chart of an HDI board anti-soldering exposure process automation industrial control system of the application. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions and advantages of the application more clear and explicit, the application will be further explained in detail below in connection with specific embodiments.

[0028] Embodiment 1:

[0029] Please refer to Figure 1 The application provides a technical solution: an HDI board anti-soldering exposure process automation industrial control system, which comprises a controller, and a sensor, an energy execution unit and a vacuum execution unit electrically connected with the controller, and the controller is used for executing the following steps:

[0030] Step a: collecting process disturbance data and process core state data, the process disturbance data is used for representing disturbance factors of an exposure environment, and the process core state data is used for representing an operating state in an exposure cavity;

[0031] Step a is the perception basis of the whole closed-loop control system. It collects two types of key data in real time through the sensor: "process disturbance data" representing external environment changes and "process core state data" representing the internal state of the equipment. This changes the mode of relying on artificial experience and fixed parameters in traditional technology, and provides the system with comprehensive and real-time quantitative cognition of the current working condition. Its beneficial effects are that it provides accurate and real-time input for subsequent predictive compensation, and is the premise of realizing from passive response to active prevention control.

[0032] Step b: based on the process disturbance data and the process core state data, generating target compensation energy for compensating the disturbance factors, and a dynamic vacuum stability target for coordinating the target compensation energy;

[0033] Step b is the decision core of the application, which embodies the idea of feedforward control. The controller predicts the influence of environmental disturbance on the exposure process, such as energy attenuation, thermal deformation, etc., based on the data collected in step a, and calculates two mutually coordinated control targets in advance.

[0034] Target compensation energy: used for actively compensating energy loss caused by factors such as dust shielding and medium absorption.

[0035] Dynamic vacuum stability target: this is the key to solving the core contradiction of "energy improvement-vacuum fluctuation". It is not a fixed value, but a vacuum degree target that is dynamically adjusted according to the increment of "target compensation energy".

[0036] The two conflicting control variables, energy and vacuum, are transformed into a synergistic relationship, where the energy is increased to ensure sufficient exposure while the vacuum is actively deepened to counteract the risk of instability caused by thermal effects, thereby achieving decoupled control of complex coupling effects.

[0037] Step c: synergistically control the energy execution unit and the vacuum execution unit to make the actual integral energy in the exposure process approach the target compensation energy and stabilize the vacuum degree of the exposure cavity at the dynamic vacuum stabilization target;

[0038] Step c is the execution part of the decision. It emphasizes the synergistic control of the energy and vacuum subsystems to ensure that the two dynamic targets generated in step b can be accurately and synchronously implemented. The controller sends instructions to the energy and vacuum execution units, respectively, so that the actual integral energy and the cavity vacuum degree strictly follow the dynamic target values throughout the exposure process.

[0039] Step c ensures the effective landing of the feedforward decision, avoiding control failure due to execution lag or incoordination, and is the physical guarantee to ensure the final exposure quality.

[0040] Step d: collect process quality data representing exposure quality after exposure, and modify the decision logic of generating target compensation energy and dynamic vacuum stabilization target in step b based on process quality data.

[0041] Step d constitutes the outer optimization loop of the system, giving the system the ability of self-learning and self-adaptation. After the end of one exposure cycle, the system obtains the actual quality result of this exposure through online detection, and associates it with the control decision. If the quality result is not ideal, the system will reverse the decision logic in step b, i.e. the model parameters.

[0042] Step d enables the system to adapt to slow-changing process drifts such as equipment aging and material batch differences, and through continuous self-optimization, it ensures the long-term stability and accuracy of the control system.

[0043] The process disturbance data includes environmental temperature and humidity and air dust concentration representing the exposure environment; the process core state data includes the substrate surface temperature and cavity vacuum pressure collected by the sensors deployed in the exposure cavity.

[0044] The decision-making process consists of two steps. First, multiple dimensions of input data, i.e. process disturbance data and process core state data, are aggregated into a single, quantifiable index, the comprehensive disturbance index, through a model, which intuitively reflects the severity of the current working condition; second, using this index, the preset baseline exposure energy and baseline vacuum target are adjusted to generate the final control target.

[0045] This two-step method structures and models the complex multivariate decision-making process. First, the state is evaluated to generate an index, and then the target is adjusted. This method is logically clear, easy to implement and adjust, and provides a clear target for subsequent adaptive optimization.

[0046] In this embodiment, in the step b, the controller is specifically configured to:

[0047] Sb1: based on the process disturbance data and the process core state data, generate a comprehensive disturbance index for quantifying the degree of deviation of the current working condition from the ideal state;

[0048] Sb2: adjust a preset reference exposure energy based on the comprehensive disturbance index to generate the target compensation energy, and adjust a preset reference vacuum target based on the difference between the target compensation energy and the reference exposure energy to generate the dynamic vacuum stabilization target.

[0049] In this embodiment, the generation of the comprehensive disturbance index is obtained by weighted summation of 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 humidity-induced deformation factor calculated based on the ambient temperature and humidity; and a dust shielding factor calculated based on the air dust concentration.

[0050] Comprehensive disturbance index calculation step description: the index is obtained by weighted summation of the thermal deformation factor, the humidity-induced deformation factor and the dust shielding factor, which are strongly related to the physical scene.

[0051] Thermal deformation factor: quantifies the risk of adhesion caused by overheating of the substrate, wherein is the substrate surface temperature.

[0052] Humidity-induced deformation factor: quantifies the risk of adhesion caused by moisture absorption and deformation of the substrate, and reflects the aggravating effect of high temperature, wherein is the ambient humidity, is the ambient temperature.

[0053] Dust shielding factor: quantifies the energy compensation demand caused by dust pollution, wherein is the air dust concentration.

[0054] Formula and parameters: the calculation formula of the comprehensive disturbance index is: wherein:

[0055] : comprehensive disturbance index (dimensionless).

[0056] : The three factors above, whose results are normalized, are dimensionless values.

[0057] : The weight coefficients of the three factors respectively, whose sum is 1, are modified by the optimization logic in step d.

[0058] : All are model coefficients preset or calibrated by the system.

[0059] This scheme avoids complex general mathematical models and instead uses parameterized models that are strongly related to the physical scene. Each factor has a clear physical meaning, making the model highly interpretable, and its weight coefficient can be optimized by subsequent steps, achieving model adaptation.

[0060] The index is calculated before exposure. Based on this index, the system can predict upcoming production risks such as insufficient energy and poor adhesion, and make compensations in advance, which constitutes the feedforward control loop of the system, realizing the transition from "passive response" to "active prevention". The complex environmental data is "reduced" to the ICD number, which greatly simplifies the subsequent control logic. The energy and vacuum targets of the next step will be adjusted based on this unified index.

[0061] In this embodiment, the generation of the dynamic vacuum stabilization target uses the preset energy-vacuum coupling coefficient to determine the amount of vacuum adjustment corresponding to the increment of target compensation energy.

[0062] Dynamic vacuum stabilization target calculation step: The calculation of this target value introduces the core parameter energy-vacuum coupling coefficient (K) ).

[0063] Formula and parameters: The calculation formula is: Where:

[0064] : Dynamic vacuum stabilization target, unit: Pa.

[0065] : Reference vacuum target, unit: Pa.

[0066] : Target compensation energy, unit: mJ / cm².

[0067] : Reference exposure energy, unit: mJ / cm².

[0068] Energy-vacuum coupling coefficient, unit: Pa / (mJ / cm²).

[0069] The formula is a mathematical embodiment of solving the contradiction between "energy increase-vacuum fluctuation", which accurately quantifies the damage effect of energy increment on vacuum, and gives the corresponding compensation amount, so that the deepening of vacuum degree is proportional to the increase of energy, thereby realizing the collaborative control of the two. The coupling coefficient Similarly, it can be optimized by step d.

[0070] If If the coupling coefficient is 0.1 kPa / (mJ / cm²), the vacuum degree target needs to be reduced by 0.1*(120-100)=2 kPa. The system automatically sets the vacuum target from the baseline-95 kPa to-97 kPa. The vacuum control unit drives the actuator according to the new target, generates greater negative pressure by enhancing air exhaust, and the adsorption force generated by the negative pressure is sufficient to offset the expansion effect caused by the increase in heat.

[0071] In this embodiment, in the step c, the controller is specifically configured to: adopt a PID control algorithm, take the dynamic vacuum stabilization target as a set value, take the cavity vacuum pressure as a process value, and perform closed-loop control on the vacuum execution unit.

[0072] The specific implementation of vacuum control in step c is described in detail: a classic PID control algorithm is adopted. The "dynamic vacuum stabilization target" calculated in step b is taken as the set value of the PID controller, and the "cavity vacuum pressure" collected by the sensor in real time is taken as the process value, and the vacuum execution unit is continuously adjusted through the algorithm.

[0073] PID is a mature, stable and reliable control algorithm, which can realize fast and non-overshoot accurate tracking of the target. When it is applied here, it can ensure that the vacuum degree can be stably controlled near the dynamically changing target value during the entire exposure process of temperature rise, and ensure the execution effect of collaborative control.

[0074] In this embodiment, the process quality data is obtained by online optical detection of the HDI plate after exposure, which at least includes bubble area for characterizing close-fitting density and pattern edge blur for characterizing pattern accuracy.

[0075] In this embodiment, in the step d, the controller is specifically configured to: when the process quality data exceeds the preset quality threshold, correct the weight coefficients of the thermal deformation factor, the humidity deformation factor and the dust shielding factor, and correct the energy-vacuum coupling coefficient.

[0076] The details of the "process quality data" in step d are as follows: This data specifically refers to the bubble area and the blurriness of the graphic edges obtained through online optical inspection.

[0077] These two indicators directly correspond to the core defects described in the background technology: bubbles and blurred edges, making the optimization objective of step d very concrete. The system can clearly know whether the problem is a bonding problem or an energy deficiency / unevenness problem, thus providing a clear and targeted direction for subsequent model correction.

[0078] When the detected process quality data exceeds a preset threshold, the controller will activate an optimization algorithm to fine-tune two types of key parameters in the upstream decision-making model: one is the weight coefficients of the three major factors. Secondly, the energy-vacuum coupling coefficient. This specification details the specific 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 excessive bubble area, it will determine that thermal effects are the primary cause, thereby increasing the weight of the thermally induced deformation factor. and energy-vacuum coupling coefficient This will enable the system to make more accurate compensations when facing similar working conditions in the future.

[0079] Existing systems continue to operate based on preset, fixed exposure energy and vacuum parameters. Due to the mismatch between actual operating conditions and set parameters, the substrate and film deform due to thermal and moisture effects, while dust particles cause energy attenuation. This leads to widespread circuit defects in this batch of products caused by poor bonding and insufficient exposure, increasing the defect rate from 0.3% to 1.5%. In contrast, this solution uses sensors to collect data in real time before the process begins. , and The controller executes step b, which, based on the process disturbance data, calculates and generates a higher comprehensive disturbance index ( ). Based on this, the controller generates a target compensation energy higher than the baseline value. To compensate for energy decay and simultaneously generate a dynamic vacuum stable target with a lower absolute pressure value ( To counteract deformation, this batch of products underwent exposure 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 occur, ensuring consistency and accuracy of the exposure process under different environmental conditions, thereby maintaining the circuit defect rate stably below the predetermined target of 0.3%.

[0080] It should be noted that all the calculation formulas in the present application file use regression analysis including but not limited to machine learning algorithms to deeply analyze the collected relevant parameters, identify their natural trends and mutual relationships. Professional software such as Python's Scikit-learn library or R language is used to automatically generate mathematical models that match the data. Then, the performance of the model is objectively evaluated through methods such as cross-validation, and combined with continuous feedback and optimization to ensure that the created formula truly reflects the inherent law of the data, thereby ensuring its effectiveness and accuracy, and ensuring that the calculation process conforms to the constraints of natural laws, rather than being based on artificially set rules.

[0081] The technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disk, etc. The computer software product includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method of each embodiment of the present application.

[0082] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can take instructions from an instruction execution system, apparatus, or device, or in conjunction with such an instruction execution system, apparatus, or device. For the 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 connection with an instruction execution system, apparatus, or device, or in conjunction with such an instruction execution system, apparatus, or device.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application 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 application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. An HDI board solder resist exposure process automation industrial control system, comprising a controller, and a sensor, an energy execution unit and a vacuum execution unit electrically connected with 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, the process disturbance data being used to represent disturbance factors of an exposure environment, and the process core state data being used to represent an operating state in an exposure chamber; Step b: generating a target compensation energy for compensating the disturbance factors and a dynamic vacuum stabilization target for cooperating with the target compensation energy based on the process disturbance data and the process core state data; Step c: cooperatively controlling an energy execution unit and the vacuum execution unit to make an actual integral energy in an exposure process approach the target compensation energy and to stabilize a vacuum degree of the exposure chamber at the dynamic vacuum stabilization target; Step d: collecting process quality data representing exposure quality after exposure, and modifying a decision logic of generating the target compensation energy and the dynamic vacuum stabilization target in step b based on the process quality data; In the step b, the controller is specifically configured to: Sb1: generating a comprehensive disturbance index for quantifying a degree of deviation of a current working condition from an ideal state based on the process disturbance data and the process core state data; Sb2: adjusting a preset reference exposure energy based on the comprehensive disturbance index to generate the target compensation energy, and adjusting a preset reference vacuum target based on a difference between the target compensation energy and the reference exposure energy to generate the dynamic vacuum stabilization target; The comprehensive disturbance index is generated by weighted summation of at least three disturbance factors, including a thermal deformation factor calculated based on a difference between a substrate surface temperature and a preset ideal substrate temperature, a humidity deformation factor calculated based on environmental temperature and humidity, and a dust shielding factor calculated based on air dust concentration. The process disturbance data includes environmental temperature and humidity and air dust concentration representing an exposure environment, and the process core state data includes a substrate surface temperature and a chamber vacuum pressure collected by a sensor arranged in the exposure chamber.

2. The HDI board solder resist exposure process automation industrial control system of claim 1, wherein, The dynamic vacuum stabilization target is generated by using a preset energy-vacuum coupling coefficient to determine a vacuum degree adjustment amount corresponding to an increment of the target compensation energy.

3. The HDI board solder resist exposure process automation industrial control system of claim 1, wherein, In the step c, the controller is specifically configured to: using a PID control algorithm, taking the dynamic vacuum stabilization target as a set value and taking the chamber vacuum pressure as a process value to perform closed-loop control on the vacuum execution unit.

4. The HDI board solder resist exposure process automation industrial control system of claim 1, wherein, The process quality data is obtained by online optical detection of an HDI board after exposure, and at least includes a bubble area representing a close-fitting degree and a pattern edge blur representing a pattern accuracy.

5. The HDI board solder resist exposure process automation industrial control system of claim 1, wherein, In the step d, the controller is specifically configured to: when the process quality data exceeds a preset quality threshold, modifying weight coefficients of the thermal deformation factor, the humidity deformation factor and the dust shielding factor, and modifying an energy-vacuum coupling coefficient.

6. The HDI board solder resist exposure process automation industrial control system of claim 5, wherein, ​

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