With the advancement of emerging technologies such as AI, Cloud and Block Chain, the electronics manufacturing industry is entering a new era of smart manufacturing. More and more Electronics Manufacturing Service providers (EMS) are investing in data and deep AI capabilities as part of their smart factory effort to improve production efficiency, process capability and quality. These data and deep AI capabilities are often implemented through enterprise hybrid clouds to achieve high availability, high scalability and low IT operational cost. 

This paper discusses current status and trends of smart manufacturing implementation in the EMS industry, specifically focusing on quality management, as there are plenty of use cases of data and AI in quality management that are good candidates for smart factory implementation. It elaborates details with examples of several quality management use cases involving data, AI and enterprise integration. In this paper, we also discuss the current maturity level and future trending and challenges in technology adoption and integration for smart factory in EMS industry.

Keyword: Quality Management, Data, Smart Factory, EMS

The Factory of the Future or a Lights out Factory will require significant automation, including many functions that are challenging and are still performed manually due to the complexity and human dexterity needed. One such example is SMT screen printer changeover process, which has been one of the most labor-intensive tasks running a SMT production line. This paper will focus on the application requirements of an automated changeover process including the need for progressive steps towards full automation. The specific changeover actions reviewed here will include solder paste cartridges, the solder paste bead, support tooling requirements, squeegees, stencils, and a necessary delivery system for the printer along with the traceability and verification systems to support an automated printer changeover. In addition, general solder management will be discussed as this becomes a potential issue when an operator is removed from the changeover process. An automated changeover will allow for fast and consistent changeovers, reduced operator requirements and errors, resulting in a higher machine utilization and yield.

Key words: Factory of the Future, Smart Factory, Automation, Robotics, Industry 4.0

The IPC Connected Factory Exchange (IPC-CFX) standard provides genuine plug-and-play IIoT data exchange between machines and supervisory systems across the shop floor. To realise the benefit of IPC-CFX, however, the whole value chain of production stations should be considered, as any missing link becomes a blind-spot for even the most basic visibility and control.

The smart factory project for Electronics Manufacturing initiative was developed to create a sandbox 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 reviews 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 are 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.

Further the paper introduces and details the CFX standards-based open-hardware interface project, which is designed to enable existing machines that cannot support CFX natively through software alone, to become part of the CFX communication infrastructure. We explore use-cases ranging from the building of “home-grown” boxes by end-users through to pre-built solutions provided by original machine vendors, and we discuss how this impacts Smart Factory Industry 4.0 realization, the additional opportunity for machine vendors, as well as how the standards-based approach with open-source community development and support works to the mutual benefit of all those in the industry.

With the Industry 4.0 transformation going full steam ahead, analytics have transformed the industry from being process-driven to now data-driven, where data speaks for itself. Conventionally, only failed measurement data are logged for repair and troubleshooting purposes. Now, all measurements regardless of whether they are pass or fail, are recorded for further advanced analytics like anomaly detection and predictive analysis.

These data type includes Meta Data, Measurement Data and Environmental Data. All these big chunks of granular data alone provide great analytics potential, but even greater potential when these data are analyzed collectively with correlation. For example: higher temperature will cause resistance measurement to be slightly higher as well.

In this presentation, we will be using PathWave Manufacturing Analytics (PMA) as an example to discuss this topic on “Single Source of Truth.” With the data agnostic nature of PMA, data are transferred to PMA seamlessly where data analytics are performed at diverse levels and perspective, with several different analytics results. Different users ranging from Original Equipment Manufacturers (OEM) monitoring multiple Contract Manufacturers (CM); Factory managers overseeing factory operation; Engineers handling In-Circuit Test or Functional station to Technicians maintaining system uptime, can tap on data analytics results from this “Single Source of Truth.”

There is no ‘data’ ambiguity between the stakeholders as it all comes from a single source. When low First Pass Yield (FPY) is observed by OEM, the factory manager sees the same information as the rest. Engineers can be triggered to review and drill down to the next level analytics to identify root causes such as false failure, degradation anomaly (situation where probes are worn-out and affecting the measurement) and the degraded probes can be easily replaced by the manufacturing stuffs with the aid from the Probe Heatmap (a graphically view of the probes locations).

“Data does not lie.” With the actual data comes together collectively, uncovering insights and identify actual opportunities to improve manufacturing efficiencies and product quality in a brief time becomes a reality.

This case analysis evaluates the thermal profile of 10 FETs rated at 48V, 250A under 100 secs soak time with all the thermally enhanced mechanical attachments installed. The goal was for the MOSFETs to operate at 8W peak power output, maintaining a temperature of no greater than 60°C – applying all the thermal handling enhancements. RSENSE installed targets to have the minimum allowable temperature after MOSFET’s thermal analysis. The setup is a household thermal mechanism to handle average power level systems that involve mounting of heatsink on the bottom side, attaching the RSENSE, and employing filled vias. Thermal simulations were conducted using 2D Solver for several power ramp cases and CFD (Computational Fluid Dynamics) to analyze the Airflow Application. 2D Solver investigated the electrical and thermal performance over a sweep of conditions. Boundary conditions were met at approximately 50°C measured as the highest measured temp on the range of installed MOSFETs. Thermal performance was furtherly improved by 5°C less after employing the airflow mechanism. Airflow design was modeled and simulated using CFD Solver and yielded the best thermal setup results.

Liquid metal alloys (LMAs), GaIn, and Galinstan, have been paid considerable attention as thermal interface materials (TIM) for usage of thermal management in microprocessor packages, including CPU, GPU and HPC, in PCs and super computers. However, automating mass production of LMA for TIMs presents challenges due to the change of rheologic behavior whichis associated with the oxidation of Gallium (Ga) during deposit process. In the present paper, we will report our recent research on developing novel TIMs of LMAs containing organic additives (LMO) to enhance deposit process performance of LMAsfor thermal management in electronics devices, especially for IC modules. In this research, we prepared LMO by addition of organic additives to LMAs and investigated the influence of organic compounds on viscosity, storage (G’) and loss (G”) modulus, adhesion and oxidation of the LMAs, and deposit process performance. Viscosity of LMOs and LMAs over time was measured at various shear rates of 10 (1/s) and 100 (1/s) using an industry standard rheometer, to study influence of the organic additives on the viscosity of LMAs. Storage (G’) modulus versus strain swing at the strain range of 0.1 – 100 % strain was measured to investigate effect of the organic additives on storage (G’) and loss (G”) modulus of LMAs. Dots of LMA and LMO materials were deposited by industry standard dispensers on Cu PCB board and Cu OSP FR4 coupons and the consistency and stability of the materials were evaluated. In addition, adhesion and wetting properties of the dispensed dots of LMOs and LMAs on Cu PCB, OSP Cu FR4 coupons, glass and bare Si, respectively, were extensively investigated.. Key words: Liquid metal, EGalinstan, viscosity, modulus, thermal conductivity and resistance, thermal interface material (TIM).

Modern electronics are prone to experience electromagnetic interference (EMI) which hinders proper functioning of the devices due to the increasing number of wireless devices and the miniaturization. In applications such as advanced driving assistancesystem (ADAS) and autonomous vehicles, electronic sensors tend to use higher frequency (<80GHz) radio waves to increase the resolution and comply with the regulation. Also, as modern electronic devices become more power intensive and generate more heat, proper heat dissipation from the devices is critical to prevent failure. Therefore, it is desirable to develop EMI shielding material that absorbs high frequency radio waves and thermal interface material (TIM) that facilitates efficient heat transfer within the components. In comparison to implementing two separate EMI shielding material and thermal interface material, it is beneficial to develop a single material that has both the capabilities to reduce cost and processing time. We have implemented various dielectric and magnetic fillers to study the thermal conductivity and EMI shielding across wide wavelength range (<80 GHz). Fillers that are both thermally conductive and absorb high frequency radio waves provide a significant advantage in designing the formulation for the application. The design principle to optimize the EMI shielding (reflection/absorption) for high frequency and thermal conductivity requires good understanding of the dielectric and magnetic property of the fillers. Along with EMI shielding and thermal conductivity, proper design of the polymer-based matrix is critical to achieve proper rheological, mechanical property and temperature reliability of the material. We will present performance, manufacturing, reliability of thermally conductive EMI shielding materials.

Thermal interface materials (TIMs) are ubiquitous in electronics cooling in a wide variety of applications in industrial, automotive and consumer electronics [1]. These materials are typically comprised of a soft polymeric matrix with thermally conductive fillers. By far the majority of TIMs are based on silicone polymers due to the special characteristics of thermal stability and ability to fine tune the modulus across a wide range [2]. Silicone migration– both vapor phase (outgassing) and liquid phase (bleed) – are known concerns of polymeric TIMs. With proper analysis of the application these concerns can be addressed by designing materials specifically for such applications. Migration is dependent on both TIM microstructure and application details like power density, thermal stack up and physical layouts. We will describe the underlying cause of polymer migration, possible risks, and mitigation measures.

In a previous paper [8], solid liquid hybrids (SLHs) were presented as a combination of metals that are solid at room temperature with ones that are liquid at room temperature. Those materials were categorized into liquid metal composites (liquid metal with solid metal preforms) and hybrid liquid pastes. That paper showed that adding solid metals to liquid metal can improve the properties of TIMs to yield “leakage-free” TIM, in which none of material will be pumped out. Such materials are less prone to oxidation and be able to survive a larger number of thermal cycles. At the same time, liquid metal will provide low interface resistance, which will result in lower thermal resistance. This paper focuses on the first type of SLH TIMs, known as liquid metal composites (LMC), in which different types of solid metals in LMCs—preforms, compressible TIMs, and reinforced matrixed solder composites (preforms with a copper matrix)—can be used in different TIM applications. The solid/liquid ratio of LMCs will not just define thermal resistance of the matrix, but it will also affect the reliability of the TIM. More liquid metal in LMCs will provide better thermal performance at time 0, but LMCs with a lower amount of liquid metal will perform better in thermal cycling. Another benefit of this material will be its performance during power cycling. Thermal resistance will be highly stable or can even decrease over time. This paper will show what types of LMCs will be recommended for different types of TIMs: TIM1, TIM2, and TIM1.5 (or TIM0). Finally, the most suitable type of automated process for high-volume production will be discussed for each of these LMCs.