A neural network-based prediction model for fine pitch stencil-printing quality in surface mount assembly

The soldering problems in surface mount assembly can represent considerable production cost increases and yield loss. About 60% of the soldering defect problems can be attributed to the solder paste stencil printing process. This paper proposes to solve a solder-paste stencil-printing quality problem by a neural network approach. Employment of a neuro-computing approach allows multiple inputs to the generation of multiple outputs. In this study, the inputs are composed of eight important factors in modeling the nonlinear behavior of the stencil-printing process for predicting deposited paste volumes. A 3^8-3 fractional factorial experimental design is conducted to efficiently collect structured data used for neural network training and testing. The results show that the proposed neural-network model is effective in solving a practical application.     

A wavelet-based multi-dimensional temporal recurrent neural network for stencil printing performance prediction

The solder paste printing (SPP) is a critical procedure in a surface mount technology (SMT) based assembly line, which is one of the major attributes to the defect of the printed circuit boards (PCBs). The quality of SPP is influenced by multiple factors, such as the squeegee speed, pressure, the stencil separation speed, cleaning frequency, and cleaning profile. During printing, the printer environment is dynamically varying due to the physical change of solder paste, which can result in a dynamic variation of the relationships between the printing results and the influential factors. To reduce the printing defects, it is critical to understand such dynamic relationships. This research focuses on determining the printing performance during printing by implementing a wavelet filtering-based temporal recurrent neural network. To reduce the noise factor in the solder paste inspection (SPI) data, this research applies a three-dimensional dual-tree complex wavelet transformation for low-pass noise filtering and signal reconstruction. A recurrent neural network is utilized to model the performance prediction with low noise interference. Both printing sequence and process setting information are considered in the proposed recurrent network model. The proposed approach is validated using practical dataset and compared with other commonly used data mining approaches. The results show that the proposed wavelet-based multi-dimensional temporal recurrent neural network can effectively predict the printing process performance and can be a high potential approach in reducing the defects and controlling cleaning frequency. The proposed model is expected to advance the current research in the application of smart manufacturing in surface mount technology.     

Modeling and optimization of stencil printing operations: A comparison study

This paper presents a comparison study for the optimization of stencil printing operations using hybrid intelligence technique and response surface methodology (RSM). An average 60% of soldering defects are attributed to solder paste stencil printing process in surface mount assembly (SMA). The manufacturing costs decrease with increasing first-pass yield in the stencil printing process. This study compares two hybrid intelligence approaches with RSM as methods of solving the stencil printing optimization problem that involves multiple performance characteristics. The optimization process is threefold. A data set obtained from an experimental design following data preprocessing process provides an accurate data source for RSM study and training neural networks to formulate the nonlinear model of the stencil printing process with/without combining multiple performance characteristics into a single desirability value, followed by a genetic algorithm searching the trained neural networks for obtaining the optimal parameter sets. The empirical defect-per-million-opportunities (DPMO) measurements demonstrate that the two hybrid intelligence methods can provide satisfactory performance for stencil printing optimization problem.     

Improving the fine-pitch stencil printing capability using the Taguchi method and Taguchi fuzzy-based model

This paper presents industrial applications for improving the capability of the fine-pitch stencil printing process (SPP) based on the DMAIC framework and using Taguchi-based methodologies. SPP is widely recognized as the main contributor of soldering defects in a surface mount assembly (SMA). An inadequate volume of solder paste deposition or poor printing quality can cause soldering defects and lead to significant reworking and repairing costs. In practice, both the desired amount of solder paste volume (quantitative index) and printing quality (qualitative index) are preferably used to monitor the SPP for the reduction of soldering defects during the statistical control process (SPC), particularly for a fine-pitch solder paste printing operation. To continuously improve SPP capability, the DMAIC framework is followed and Taguchi-based methodologies are proposed under the considerations of single characteristic performance index (SCPI) and multiple characteristic performance indices (MCPI). The SCPI is optimized using the conventional Taguchi method. Then, a Taguchi fuzzy-based model is developed to optimize the SPP with the MCPI property. Optimizing a multi-response problem by the Taguchi method involves the engineer's judgment which tends to increase the degree of uncertainty. The performance of these two approaches is compared through the process capability metric, and the material and factors significantly affecting the fine-pitch SPP performance are reported.     

Experimental design with fuzzy levels

A novel technique is introduced for planning experimental design employing fuzzy rule-based systems. The significant aspect of the proposed experimental design with fuzzy levels (EDFL) is assigning a membership function for each level of variable factors. Consequently, the design matrix and observed responses can be represented in a set of fuzzy rules. Several examples are presented to clarify the proposed idea and the results are compared with the conventional Taguchi methodology. We have specifically planned an L18 orthogonal array EDFL for the solder paste printing stage of surface mount printed circuit board assembly to provide a model for the process and to optimize the selection of variable factors.     

Neurofuzzy modelling of the reflow thermal profile for surface mount assembly

Troubleshooting and production set-up time are often the major contributors for productivity loss in surface mount assembly. The solder reflow process is one of the least understood process segments. The research proposes a neurofuzzy reflow thermal profile control system for a convection oven. An incompatible temperature profile can result in a poor soldering quality and reduced system throughput. This paper proposes a neurofuzzy-based solder reflow thermal profile control system that responds to facilitate the production set-up process. The model is trained and constructed using data from both an experimental design and from historical production records. Customized computer code is used to generate a user-friendly human–machine interface and to link between neurofuzzy-reasoning rules and reflow oven set-up parameters for thermal profile planning and control. Empirical results illustrate the effectiveness and efficiency of the proposed system to solve a practical application.     

Development of an integrated reflow soldering control system using incremental hybrid process knowledge

In this paper, we present an industrial application of intelligent knowledge-based reflow soldering control system to economically determine process parameters and assess the outputs for surface mount assembly. The system performance of surface mounting is significantly affected by three major interconnected process segments (including solder paste application, component placement, and solder reflow) and in combination. Many surface mount manufacturers have suffered both quality and productivity losses due to the lacks of effective line setup procedures, rapid process troubleshooting, and appropriate corrective action. This proposed system develops an integrated process control scheme, the framework of hybrid process knowledge extraction through neuro-fuzzy data training, and a knowledge-based system with a GUI man–machine interface for predicting and controlling the given system performance. The efficiency and effectiveness of the proposed system are illustrated using DPMO measurement and productivity analysis.     

SMT焊膏印刷质量AOI技术的研究

本文主要介绍了在微电子表面组装技术(SMT)中,对焊膏印刷质量进行检测的自动光学检测技术(AOI)及其系统的基本概况,讨论了无铅化对焊膏印刷AOI的影响,并对SMT焊膏印刷质量的AOI检测技术的发展趋势进行了分析.     

Residue buildup predictive modeling for stencil cleaning profile decision-making using recurrent neural network

This research proposes a novel framework to control the stencil cleaning profile selection in the stencil printing process (SPP). The SPP is a major contributor to yield loss in surface mount technology (SMT). Enhancement in SPP performance is critical to improving the printed circuit board (PCB) assembly line. The selection of a solvent-based or a dry-based cleaning profile is challenging, but the choice determines the effectiveness and efficiency of the stencil cleaning operation. The amount of residue buildup under the stencil is the main criterion used to decide the appropriate cleaning profile in SPP. In this research, a multi-dimensional temporal recurrent neural network (RNN) approach is used to accurately predict the amount of residue buildup on the underneath surface of the stencil in real-time. Specifically, the long short-term memory (LSTM) architecture is trained using actual residue buildup data. The proposed LSTM prediction model is compared with other state-of-the-art regression models such as multilayer perceptron (MLP) and ensemble learning models. Experimental results show the proposed LSTM model outperforms the state-of-the-art regression models and accurately predicts the stencil status. The proposed research aids decision-makers in the SPP line to select the appropriate stencil cleaning profile adaptively and in real-time. As a result, the overall SPP performance is improved.     

Manufacturing an environmentally friendly PCB using existing industrial processes and equipment

This paper investigates the possibility of utilising an additive screen printing process with conductive ink and adhesive together with a degradable substrate to identify whether this process offers a viable alternative to current subtractive methods of PCB manufacture. Existing manufacturing equipment and production process were adopted in order to establish the compatibility of a sustainable and environmental friendly PCB with these processes. Experimental trials have shown that by using a stainless steel stencil 150 mm thick in conjunction with an MPM Ultraprint machine with a printing speed of 89 mm/s, a squeegee pressure of 0.97 bar and a downstop of 1.9 mm successful printing of the electrically conductive adhesive was achieved. It was also shown that the substrate was compatible with electronic placement technology and convection oven. Comparisons of the electronic assemblies to an industrial IPC standard illustrated that it is possible to develop a circuit, which is deemed acceptable under this standard. It can be concluded therefore that a screen printed conductive ink pattern, when printed on a degradable substrate can offer a viable alternative to current printed circuit boards and can be manufactured using existing technology and manufacturing processes.     

Orthogonal Fuzzy Rule-Based Systems: Selection of Optimum Rules

In this paper, the concept of orthogonal fuzzy rule-based systems is introduced. Orthogonal rules are an extension to the definition of orthogonal vectors when the vectors are vectors of membership functions in the antecedent part of rules. The number and combination of rules in a fuzzy rule-based system will be optimised by applying orthogonal rules. The number of rules, and subsequently the complexity of the fuzzy rule-based systems, are directly associated with the number of input variables and distinguishable membership functions for each individual input variable. A subset of rules can be used if it is known which subset provides closer behaviour to the case when all rules are used. Orthogonal fuzzy rule-based systems are proposed as a judgment as to whether the optimal rules are selected. The application of orthogonal fuzzy rules becomes essential when fuzzy rule-based systems containing many inputs are used. An illustrative example is presented to create a model for the solder paste printing stage of surface mount tech-nology.     

冲击载荷下BGA焊点应有限元模拟

从动力学角度分析了PBGA组件在高加速度冲击载荷下的响应.采用解析法与有限元分析相结合的方式考察了PBGA组件的结构参数和材料特性等因素对焊点应力的影响,并运用参量分析法比较了这些因素对焊点应力的影响程度.分析结果表明:在冲击载荷下,焊点位置对焊点应力的影响最大,应力峰值出现在PBGA器件的最外端焊点上,而焊点高度、BGA模量以及PCB模量对焊点应力影响较小.增大焊点直径或减小焊点模量均可有效减小焊点的应力,选择适当的PCB厚度也可降低焊点的应力.     

Quantum dots printing paste for display backlights: Preparation,characterization, and applications

Quantum dots (QDs) are one kind of photoluminescent materials with excellent properties, such as adjustable spectrum, narrow full width at half maximum (FWHM), and high color gamut. In this paper, a photoluminescent QDs printing paste suitable for light guide dots array applied in the backlight for liquid crystal display (LCD) was presented. The QDs printing paste was first prepared and characterized with different kinds of solvents. Then, triphenyl phosphite (TPP), a kind of antioxygen, was added into the QDs printing paste to improve the stability of the QDs printing paste. Finally, a QDs light guide plate (LGP) was fabricated by screen printing with QDs printing paste added with silicon dioxide (SiO₂) light scattering particles. The color gamut of QDs LGP arrived 131.6%NTSC due to light scattering interaction between blue LED, red QDs, green QDs, and SiO₂ particles. The results showed that the QDs-chloroform printing paste had uniform light output and the highest photoluminescent peak intensity. Therefore, the composite screen printing paste has wide application prospects in the backlight of LCD, with the advantages of simple process, high production efficiency, and low cost.     

回流焊接温度曲线控制研究

随着集成电路产业的飞速发展,高集成度、高可靠性已经成为行业的新潮流。在这种趋势的推动下,SMT(表面贴装技术)在中国也得到了进一步的推广和发展。很多公司在生产和研发中已经大量应用了SMT工艺和表面贴装元器件。在SMT工艺中回流焊接是核心工艺,因为表面组装PCB设计,焊膏印刷和元器件贴装产生的缺陷,最终都将集中表现在焊接中。因此,如果没有合理可行的回流焊接工艺,前面任何工艺控制都将失去意义。     

Evaluation of thermo-mechanical damage and fatigue life of solar cell solder interconnections

The soldering process of interconnecting crystalline silicon solar cells to form photovoltaic (PV) module is a key manufacturing process. However, during the soldering process, stress is induced in the solar cell solder joints and remains in the joint as residual stress after soldering. Furthermore, during the module service life time, thermo-mechanical degradation of the solder joints occurs due to thermal cycling of the joints which induce stress, creep strain and strain energy. The resultant effect of damage on the solder joint is premature failure, hence shortened fatigue life. This study seeks to determine accumulated thermo-mechanical damage and fatigue life of solder interconnection in solar cell assembly under thermo-mechanical cycling conditions. In this investigation, finite element modelling (FEM) and simulations are carried out in order to determine nonlinear degradation of SnAgCu solder joints. The degradation of the solder material is simulated using Garofalo-Arrhenius creep model. A three dimensional (3D) geometric model is subjected to six accelerated thermal cycles (ATCs) utilising IEC 61215 standard for photovoltaic panels. The results demonstrate that induced stress, strain and strain energy impacts the solder joints during operations. Furthermore, the larger the accumulated creep strain and creep strain energy in the joints, the shorter the fatigue life. This indicates that creep strain and creep strain energy in the solder joints significantly impacts the thermo-mechanical reliability of the assembly joints. Regions of solder joint with critical stress, strain and strain energy values including their distribution are determined. Analysis of results demonstrates that creep energy density is a better parameter than creep strain in predicting interconnection fatigue life. The use of six ATCs yields significant data which enable better understanding of the response of the solder joints to the induced loads. Moreover, information obtained from this study can be used for improved design and better-quality fabrication of solder interconnections in solar cell assembly for enhanced thermo-mechanical reliability.     

非线性动态系统神经模糊建模与内模/PID双重控制系统设计

非线性动态系统的内模控制要求建立精确的对象正模型和逆模型,这对于大多数实际对象是难以做到.提出了基于一类神经模糊模型的非线性动态系统建模方法,并在此基础上研究了基于神经模糊模型的非线性系统的内模控制设计.基于输入输出数据辨识的对象正模型和逆模型存在着模型失配问题,导致神经模糊内模控制范围变窄和控制鲁棒性降低,为了改善系统的性能,提出了神经模糊内模控制与PID控制结合的双重控制策略.对CSTR的反应物浓度控制研究表明,双重控制策略能有效地拓宽系统可控范围,改善系统性能.仿真结果证明该控制策略简单而有效.     

Internal Model Control Based on a Neurofuzzy System for Network Applications. A Case Study on the High-Performance Drilling Process

This paper presents the design and implementation of a neurofuzzy system for modeling and control of a high-performance drilling process in a networked application. The neurofuzzy system considered in this work is an adaptive-network-based fuzzy inference system (ANFIS), where fuzzy rules are obtained from input/output data. The design of the control system is based on the internal model control paradigm. The results obtained are significant both in simulation as well as the real-time application of networked control of the cutting force during high-performance drilling processes.     

Variable selection algorithm for the construction of MIMO operatingpoint dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm     

The production scheduling problem in a multi-page invoice printing system

This paper addresses the production scheduling problem in a multi-page invoice printing system. The system comprises three stages: the stencil preparation stage, the page printing stage and the invoice assembly stage. Since each page can be considered as a component and the invoice as the finished product, the production system for multi-page invoices can be treated as an assembly-type flowshop with parallel machines at the last two stages. Moreover, two types of sequence-dependent setup operations are considered at the second stage. The objective is to minimize the makespan for all the invoice orders. We first formulate this problem into a mixed-integer linear programming (MILP). Then a hybrid genetic algorithm (HGA) is proposed for solving it due to its NP-hardness. To evaluate the performance of the HGA heuristic, a lower bound for the makespan is developed. Numerical experiment indicates that our algorithm can solve the problem efficiently and effectively.     

PCB 锡膏图像采集参数的研究

利用图像处理技术对锡膏进行检测是锡膏检测中的一种新方法,图像质量是确保图像检测精度的关键之一。图像采集对于保障图像质量来说有着至关重要的作用,是最终获取图像有用信息所进行的一系列处理步骤中关键的第一步。重点研究了影响锡膏图像采集的焦距、曝光、增益三个主要参数,并通过实验确定了焦距、曝光和增益等三个主要参数的合理取值范围以及快门速度与增益之间的函数关系。为锡膏图像采集参数的调整提供了依据,也为后续研究锡膏图像采集的参数自动调整奠定了基础。