Computer and Electronic
Industries in the Computer and Electronic Product Manufacturing subsector group establishments that manufacture computers, computer peripherals, communications equipment, and similar electronic products, and establishments that manufacture components for such products. The Computer and Electronic Product Manufacturing industries have been combined in the hierarchy of NAICS because of the economic significance they have attained. Their rapid growth suggests that they will become even more important to the economies of all three North American countries in the future, and in addition their manufacturing processes are fundamentally different from the manufacturing processes of other machinery and equipment. The design and use of integrated circuits and the application of highly specialized miniaturization technologies are common elements in the production technologies of the computer and electronic subsector.
Assembly Line
📱 Inside the Factory Where Robots Are Building Your Next Samsung Phone
The sound of bots whirring, air gaskets blowing and mechanical arms shifting positions can be heard throughout the facility. Every once in a while, an autonomous robot will play a cute jingle to signal its arrival. These robots, known as AGVs (for automated guided vehicles), roam the factory floor shuttling materials to their designated stations, guided by aluminum tracks on the floor. I’m told there are 80 of the bots in the company’s Gumi facility, where phones like the Galaxy S23 and the new Galaxy Z Flip 5 are assembled.
A large portion of the assembly line is dedicated to quality checks. Samsung says there are about 30,000 to 50,000 inspection items for the Galaxy S23 lineup alone. That includes the S Pen connection; the charging port; near-field communication functionality (or NFC, the tech that powers contactless payments); touch screen panels; fingerprint sensors; cameras; speakers; the SIM card tray; and Wi-Fi connections. There are also checkpoints within the assembly line for chips that enable millimeter wave 5G connections and ultra wideband, the proximity-sensing tech that enables phones to more easily share files and to function as digital car keys.
How SCARA, Six-Axis, and Cartesian Pick-And-Place Robotics Optimize and Streamline Electronics Manufacturing Processes
Hastening the adoption of robotics in semiconductor manufacture are burgeoning classes of six-axis robots, selective compliance assembly robot arms (SCARAs), cartesian machinery, and collaborative robots featuring reconfigurable or modular hardware as well as unifying software to greatly simplify implementation. These robots and their supplemental equipment must be designed, rated, and installed for cleanroom settings or else risk contaminating delicate wafers with impurities. Requirements are defined by ISO 14644-1:2015, which classifies cleanroom air cleanliness by particle concentration.
Advanced cleanroom-rated robotic end-of-arm tooling (EoAT or end effectors) such as grippers are core to semiconductor production. Here, EOATs must have high dynamics and the ability to execute tracing, placing, and assembling with exacting precision. In some cases, EoAT force feedback or machine vision boosts parts-handling accuracy by imparting adaptive capabilities — so pick-and-place routines are quickly executed even if there’s some variability in workpiece positions, for example. Such sensor and feedback advancements can sometimes render the complicated electronics-handling fixtures of legacy solutions unnecessary.
🖨️ How Will The Apple Reality Pro Headset Boost 3D Printing?
While most AR/VR companies certainly rely on 3D printing to some extent, at least at the level of product design, Apple’s latest product, specifically, may kickstart a niche segment of the industry known as “additively manufactured electronics (AMEs).” To those who have been following the 3D printing industry, the most obvious method for squeezing electronics into small spaces is to use AMEs. With 3D printing, it’s possible to spray conductive traces onto curved surfaces using a technology called Aerosol Jet, from Optomec, which allows electronic features to be incorporated into the structure of a product, rather than force entirely separate components into already tight spaces.
The Sandia National Labs spinout has sold Aerosol Jet printers to Google, Meta, Samsung and has all-but-confirmed that Apple is using the process, as well. By 2016, Taiwanese manufacturer Lite-On Mobile used these systems to spray antennas onto millions of mobile phones before its then-senior manager of Technology Development for Antennas, Henrik Johansson, left to work for Apple.
However, it isn’t Aerosol Jet alone that may be used by these companies to shrink devices. In December 2022, Meta acquired optics firm Luxexcel with a goal of using its lens printing process to create AR glasses. Luxexcel’s method produces optically clear polymers with the ability to integrate waveguides, necessary for transparent displays, into its lenses. It’s no coincidence then that the social media-turned-metaverse giant will be releasing the newest version of its Quest Pro headset late this year, a device said to rival Apple’s Reality Pro.
World’s Leading Electronics Manufacturers Adopt NVIDIA Generative AI and Omniverse to Digitalize State-of-the-Art Factories
More than 50 manufacturing giants and industrial automation providers — including Foxconn Industrial Internet, Pegatron, Quanta, Siemens and Wistron — are implementing Metropolis for Factories, NVIDIA founder and CEO Jensen Huang announced during his keynote address at the COMPUTEX technology conference in Taipei.
Supported by an expansive partner network, the workflow helps manufacturers plan, build, operate and optimize their factories with an array of NVIDIA technologies. These include NVIDIA Omniverse™, which connects top computer-aided design apps, as well as APIs and cutting-edge frameworks for generative AI; the NVIDIA Isaac Sim™ application for simulating and testing robots; and the NVIDIA Metropolis vision AI framework, now enabled for automated optical inspection. NVIDIA Metropolis for Factories is a collection of factory automation workflows that enables industrial technology companies and manufacturers to develop, deploy and manage customized quality-control systems that offer a competitive advantage.
Why The U.S. Fell Behind In Phone Manufacturing
Smart Machine for Mobile Phone Middle Frame Inspection
Yield Is Top Issue For MicroLEDs
Early test results indicate yield issues at chip transfer, array-to-driver bonding, and other relatively new processes. High cost for this immature technology is keeping microLED displays from making the prototype-to-production leap. And because probers are not well suited to testing thousands of microLED pixels in densely packed arrays, DFT with self-testing is employed, which enables lifecycle testing — at ATE, post-assembly test, and in the field.
For instance, Dialog Semiconductor, a Renesas Company, developed a testing scheme for a white adaptive headlight module containing a 20,000-microLED array with 40µm pitch. “It’s a very good example of how a DFT circuit is not just overhead and cost to buy quality,” said Hans Martin von Staudt, director of Design-for-Test at Renesas. “Instead, it serves a valuable function over the lifetime of the chip. So we needed a DFT scheme with high-diagnostic coverage of the assembly process for pinpointing process weaknesses while enabling in-field monitoring.”
Inspection and testing methods are improving in their ability to identify and segregate out-of-spec product. Mass transfer methods that remove microLED die from wafers or film carriers and position them on IC drivers (for small AR/VR, watch and headlights) or TFT PCBs (for TVs), must easily separate known good die (KGD) from failures and underperforming die.
Yield targets for most microLED display apps are high (see figure 1) because the human eye can quickly spot missing pixels. To put yield targets in perspective, an 8K TV contains 99 million microLED chips. So if the defectivity rate is 0.5%, 520,000 devices must be removed and replaced. Top Engineering estimates this process would take 144 hours, making it cost-prohibitive until repair cost (removal and replacement of individual microLEDs) can be accelerated.
Behind the Foldable Phones in Our Pockets
Flash Joule heating by Rice lab recovers precious metals from electronic waste in seconds
Big Data Analytics in Electronics Manufacturing: is MES the key to unlocking its true potential?
In a modern SMT fab, every time a stencil is loaded or a squeegee makes a pass, data is generated. Every time a nozzle picks and places a component, data is generated. Every time a camera records a component or board inspection image, data is generated. The abundance of data in the electronics industry is a result of the long-existing and widespread process automation and proliferation of sensors, gauges, meters and cameras, which capture process metrics, equipment data and quality data.
In SMT and electronics the main challenge isn’t the availability of data, rather the ability to look at the data generated from the process as a whole, making sense of data pertaining to each shop floor transaction, then being able to use this data to generate information from a single point of truth instead of disparate unconnected point solutions and use the generated insight to make decisions which ultimately improve process KPIs, OEE, productivity, yield, compliance and quality.
Printing process holds promise for bendable displays
A new process for creating flexible large area electronics could lead to breakthroughs in technologies including prosthetics, high-end electronics and fully bendable digital displays.
Until now, the most advanced flexible electronics have been mainly manufactured via a three-stage stamping process called transfer printing. Processes have been developed to make the stamping transfer more effective, but they often require additional equipment like lasers and magnets, which adds extra manufacturing cost.
The Glasgow team said they have eliminated the second stage of the conventional transfer printing process and replaced it with ‘direct roll transfer’ to print silicon straight onto a flexible surface.
Vision Cameras Inspect Disk Drive Assemblies
Once manufactured, an HDD is carefully fitted and sealed in a metal or plastic case. The case ensures that all drive components are perfectly secured in place and their mechanics work well over the lifetime of the product. It also protects the sensitive disks from dust, humidity, shock and vibration.
An HDD case must be defect-free and have perfectly machined thread holes to perform these functions, according to Somporn Kornwong, a manager at Flexon. In 2019 his company developed Visual Machine Inspection (VMI) for a manufacturer so it can quickly and thoroughly inspect each case it produces.
MacroFab: Driving The Cloud-Based Transformation Of Electronics Manufacturing
The company brings cloud-based, manufacturing-as-a-service (MaaS) solutions to the electronics industry. On its platform, companies can upload component designs, obtain quotes, place orders and follow the progress towards delivery. Companies can price and order a wide range of parts and products, from printed circuit boards (PCB) to fully assembled and packaged electronics products.
Getting specific – how discrete manufacturers can build greater resilience
We’ll see how Mixed Reality (MR) makes it easier for shopfloor operators to work on complex, customized products – without the lengthy, face-to-face training plus the travel this often involves. This also enables discrete manufacturers to respond to flexible product configurations with instant updating of product documentation across entire engineering and supply chains.
We’ll also look at how cloud-based Manufacturing Execution Systems (MES) and Asset Management systems connects multiple facilities and customers vendors and all stakeholders in an ecosystem.
Google Cloud and Seagate: Transforming hard-disk drive maintenance with predictive ML
At Google Cloud, we know first-hand how critical it is to manage HDDs in operations and preemptively identify potential failures. We are responsible for running some of the largest data centers in the world—any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services. In the past, when a disk was flagged for a problem, the main option was to repair the problem on site using software. But this procedure was expensive and time-consuming. It required draining the data from the drive, isolating the drive, running diagnostics, and then re-introducing it to traffic.
That’s why we teamed up with Seagate, our HDD original equipment manufacturer (OEM) partner for Google’s data centers, to find a way to predict frequent HDD problems. Together, we developed a machine learning (ML) system, built on top of Google Cloud, to forecast the probability of a recurring failing disk—a disk that fails or has experienced three or more problems in 30 days.
Industry 4.0 Solves The Billion-Dollar Misalignment Problem In Electronics Supply Chain
Electronics manufacturing loses billions of dollars every year due to misaligned incentives within the supply chain. These misalignments fester under the surface leading to suboptimal results: lower margins, late shipments, and lower trust relationships with suppliers.
But the most visionary supply chain and manufacturing leaders are realizing that Industry 4.0 and Smart Manufacturing technologies, traditionally billed as increasing productivity and increasing Overall Equipment Effectiveness (OEE) are a secret weapon they can use to drive cultural change that corrects these misalignments. They are pushing these technologies to do double-duty: driving both the core efficiency improvements and setting a new culture around them. By reevaluating the misaligned incentives that have developed in their supply chains over decades, these leaders are breaking the mold, empowering their employees, and driving results that are saving their companies tens of millions of dollars or more each year.
Pushing The Frontiers Of Manufacturing AI At Seagate
Big data, analytics and AI are widely used in industries like financial services and e-commerce, but are less likely to be found in manufacturing companies. With some exceptions like predictive maintenance, few manufacturing firms have marshaled the amounts of data and analytical talent to aggressively apply analytics and AI to key processes.
Seagate Technology, an over $10B manufacturer of data storage and management solutions, is a prominent counter-example to this trend. It has massive amounts of sensor data in its factories and has been using it extensively over the last five years to ensure and improve the quality and efficiency of its manufacturing processes.