Smart Factories require continuous and reliable operation of all equipment. Therefore, equipment maintenance is becoming more and more important. A simple approach for equipment maintenance, such as “let it break – then fix / repair it”, does not fulfill the needs of a continuously and reliably operating Smart Factory: It leads to unexpected, not plannable downtimes of equipment and, thus, impacts production in an unacceptable way. A preventive maintenance approach can avoid this problem of unplanned production downtime due to breaking down machines. A predictive or prescriptive maintenance approach may improve planning of maintenance even further. All these approaches have in common that they need data from operation of equipment. A Smart Factory needs Smart Maintenance, i.e., data driven maintenance.

This presentation provides an overview of an ongoing examination of test methodologies within the ESA, supported by IPC, for the evaluation of stacked and staggered (offset) microvia structures, including air-to-air thermal shock, convection reflow assembly simulation and current induced thermal cycling.

As EMS Providers such as Contract Manufacturers look forward to Industry 4.0, their need for complex data analysis to inform manufacturing decisions takes on even more significance. Managing manufacturing data has become as important to operational excellence as managing the solder paste and electronic components placed on each circuit board. However, while there are many examples of small-scale, operational data collection systems that inform real-time factory operations, there are relatively few examples of large-scale historical machine data collection systems. In this paper we describe one such system and discuss both the advantages it brings as well as the practical implementation challenges to build and maintain it at scale. It is expected that in the years to come many EMS factories will find use for such systems as increasingly proven use cases involving complex machine learning, predictive maintenance and even artificial intelligence become more common in the industry. Each of these advanced Industry 4.0 applications requires as a prerequisite the kind of large, centralized, historical machine dataset that a system like what we describe collects and manages.

The smart factory project for Electronics Manufacturing initiative was developed to create a sandpit to carry out industry 4.0 use cases working with industrial members and partners. The Manufacturing Technology Centre (MTC) based in Coventry UK have carried out a project that outlines the implementation of IPC CFX to demonstrate the connectivity between the machine, the broker and the dashboard. The main objective focuses on the implementation elements for the Adaptor machines and the RabbitMQ server. This includes configuration requirements and user guides around maintaining the delivered implementation.

The paper will review the above example of the successful connecting using IPC CFX, a SMT DEK printer and the Ersa Reflow 10 zone oven which are both classed as legacy machine due to its age and the operating system. The benefits to this is to allow legacy machines used in the electronics manufacturing industry to communicate, transfer operational data for track/trace and monitoring while, in future add-ons, allow them to be agile and have autonomous ability to change parameters based on the live shop floor situation. This will eventually lead to the ‘lights out’ capabilities in the low volume/high mix electronics manufacturing industry.

Why use robotics?

Precision -> improved quality

Consistency -> improved quality

Traceability -> improved quality

Dexterity -> to handle the parts

Robust -> reliability for multi shift operations

Reconfigurable -> flexibility

This paper discusses the automation of inspection using Augmented Reality (AR).   Machine vision is used to find small components since it reduces the amount of time needed for inspection.  Augmented reality overlays the information from the design onto the PCB under the lens.  It then assists the operator in seeing the small components on the board.  The visuals are also uploaded to a network.  Augmented Reality increases speed, quality, and reduced cost.  

This presentation provides an overview of an ongoing examination of test methodologies within the ESA, supported by IPC, for the evaluation of stacked and staggered (offset) microvia structures, including air-to-air thermal shock, convection reflow assembly simulation and current induced thermal cycling.

IPC/IMEC/ESA Microvia TV CITC Outline

 CITC Coupon and Test Plan Overview

 CITC Test Introduction

 Temperature Coefficient of Resistance (TCR) Test and Results

 Relative Life of the 3 HDI microvia structures provided

 CITC Life Curves, Nf vs Temperature

 Reliability Calculations by type and temperature

The industry has been openly discussing the concern about weak microvia interfaces after IR reflow and the potential for an undetected open or latent defect that can escape after expensive components have been soldered to the board. A specific concern is for the reliability of stacked microvia designs on very complex panels that are often built-in low volumes. This type of build is typical of American and European OEMs who are using large and expensive BGA components in mission critical electronics. Due to the limited number of units made, this board segment of the industry is more vulnerable to weak interface failure than the HDI boards for mobile devices that are made with high levels of automation in mass production by fabricators in Asia. Further complicating the board design impact, the metallization process that is used can have very different reliability performance from different lines in different regions.

The goal for the metallization process is to form a continuous metallurgical structure to withstand the thermo-mechanical stress of IR reflow during assembly. The best condition consists of epitaxial growth of a thin electroless copper deposit on the target pad with a grain structure that recrystallizes with temperature and becomes indistinguishable from the target pad and electrolytic copper structures. There are multiple factors that influence the ability to form this recrystallized structure, which in turn affects the strength of the microvia interface. These include the circuit design, laminate material selection, type and settings of laser via formation, post-laser conditioning of the target pad copper, the desmear and electroless copper process processes, and the electrolytic copper via fill plating processes.

Through extensive auditing as a supplier of primary metallization and electrolytic copper via fill chemistries, and cooperative work with PCB fabricator customers to improve microvia reliability, a wide range of studies were conducted. Presented in this paper are potential areas of concern for microvia reliability with a specific focus on metallization processes and the factors stated above as well as testing on improvements. The approach taken includes low level DOE testing for process improvement as measured by a test panel using IPC TM-650 2.6.26A and TM-650 2.6.27, otherwise known as IST and simulated IR reflow testing. Experience in failure analysis techniques, limitations on some commonly utilized inspection methods, and a review of overall best practices for plating are also discussed.

Testing was augmented with SEM, FIB, and broad-beam Ion Milling techniques to evaluate various the various structures. Current induced or air to air thermal cycling were utilized to determine the level of microvia survivability and judge process improvements.