We see light and colours around us every day. However, to analyse the information it carries, we must analyse light using spectrometers, in the lab. These devices detect sparkles and substances that our eyes would otherwise not notice.Now, an international team of researchers, including the University of Cambridge, have designed a miniaturised spectrometer that breaks all current resolution records, and does so in a much smaller package, thanks to computational programmes and AI.The new miniaturised devices could be used in a broad range of sectors, from checking the quality of food to analysing starlight or detecting faint clues of life in outer space. The&nbsp;<a href="https://www.science.org/doi/10.1126/science.add8544" target="_blank">results</a> are reported in the journal Science.Traditionally, spectrometers rely on bulky components to filter and disperse light. Modern approaches simplify these components to shrink footprints, but still suffer from limited resolution and bandwidth. Additionally, traditional spectrometers are heavy and take up extraordinary amounts of space, which limits their applications in portable and mobile devices.To tackle these problems, and shrink the size of the system, researchers have coupled layered materials with artificial intelligence algorithms. The result is an all-in-one spectrometer thousands of times smaller than current commercial systems. At the same time, it offers performance comparable to benchtop systems. In other words, these new spectrometers will provide portable alternatives to uncover otherwise invisible information, without even going into the lab.&ldquo;We eliminate the need for detector arrays, dispersive components, and filters. It&rsquo;s an all-in-one, miniaturised device that could revolutionise this field,&rdquo; said Dr Hoon Hahn Yoon, from Aalto University in Finland, first author of the paper. This spectrometer-on-chip technology is expected to offer high performance and new usability across science and industry.The detector uses van der Waals heterostructures &ndash; a &lsquo;sandwich&rsquo; of different ingredients, including graphene, molybdenum disulfide, and tungsten diselenide. Different combinations of material components enable light detection beyond the visible spectrum, as far as the near-infrared region. This means the spectrometer detects more than just colour, enabling applications such as chemical analysis and night vision.&ldquo;We detect a continuum spectrum of light, opening a world of possibilities in a myriad of markets,&rdquo; said Dr Yoon. &ldquo;Exploring other material combinations could uncover further functionalities, including even broader hyperspectral detection and improved resolution.&rdquo;AI is a key aspect of these devices, commonly called &lsquo;computational&rsquo; spectrometers. This technology compensates for the inherent noise increase that inevitably occurs when the optical component is wholly removed.&ldquo;We were able to use mathematical algorithms to successfully reconstruct the signals and spectra, it&rsquo;s a profound and transformative technological leap,&rdquo; said lead author Professor Zhipei Sun, also from Aalto University, and a former member of Cambridge&rsquo;s Department of Engineering. &ldquo;The current design is just a proof-of-concept. More advanced algorithms, as well as different combinations of materials, could soon provide even better miniaturised spectrometers.&rdquo;Spectrometers are used for toxin detection in food and cosmetics, cancer imaging, and in spacecraft &ndash; including the James Webb Space Telescope. And they will soon become more common thanks to the development and advancement of technologies such as the Internet of Things and Industry 4.0.The detection of light &ndash; and the full analysis of spectroscopic information &ndash; has applications in sensing, surveillance, smart agriculture, and more. Among the most promising applications for miniaturised spectrometers are chemical and biochemical analysis, thanks to the capabilities of the devices to detect light in the infrared wavelength range.The new devices could be incorporated into instruments like drones, mobile phones, and lab-on-a-chip platforms, which can carry out several experiments in a single integrated circuit. The latter also opens up opportunities in healthcare. In this field, spectrometers and light-detectors are already key components of imaging and diagnostic systems &ndash; the new miniaturised devices could enable the simultaneous visualisation and detection of &lsquo;chemical fingerprints&rsquo;, leading to possibilities in the biomedical area.&ldquo;Our miniaturised spectrometers offer high spatial and spectral resolution at the micrometre and nanometre scales, which is particularly exciting for responsive bio-implants and innovative imaging techniques,&rdquo; said co-author&nbsp;<a href="http://www.eng.cam.ac.uk/profiles/th270" target="_blank">Professor Tawfique Hasan</a>, from the&nbsp;<a href="https://www.graphene.cam.ac.uk/" target="_blank">Cambridge Graphene Centre</a>.This technology has huge potential for scalability and integration, thanks to its compatibility with well-established industrial processes. It could open up the future for the next generation of smartphone cameras that evolve into hyperspectral cameras that conventional colour cameras cannot do. Researchers hope their contribution is a stepping stone towards the development of more advanced computational spectrometers, with record-breaking accuracy and resolution. This example, they say, is just the first of many.<hr>Reference: Hoon Hahn Yoon et al. &lsquo;<a href="https://www.science.org/doi/10.1126/science.add8544" target="_blank">Miniaturized Spectrometers with a Tunable van der Waals Junction</a>.&rsquo; Science (2022). DOI: 10.1126/science.add8544.This article first appeared on the University of Cambridge <a href="https://www.cam.ac.uk/research/news/artificial-intelligence-powers-record-breaking-all-in-one-miniature-spectrometers" target="_blank">website</a>. Written by Fernando Gomollon-Bel and edited by Sarah Collins.&nbsp;

Using Artificial Intelligence (AI) to replace optical and mechanical components, researchers have designed a tiny spectrometer that breaks all current resolution records.

Artificial intelligence powers record-breaking all-in-one miniature spectrometers

In this article, we look at how Super Low power Wi-Fi and Bluetooth LE enable smart locks to be more agile and reliable by demonstrating these features with a case study from Renesas. 


Super Low Power Wi-Fi and Bluetooth LE Unlock Smart Locks

This article was first published on <a href="https://news.mit.edu/2022/stackable-artificial-intelligence-chip-0613" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>Imagine a more sustainable future, where cellphones, smartwatches, and other wearable devices don&rsquo;t have to be shelved or discarded for a newer model. Instead, they could be upgraded with the latest sensors and processors that would snap onto a device&rsquo;s internal chip &mdash; like LEGO bricks incorporated into an existing build. Such reconfigurable chipware could keep devices up to date while reducing our electronic waste.&nbsp;Now MIT engineers have taken a step toward that modular vision with a LEGO-like design for a stackable, reconfigurable artificial intelligence chip.The design comprises alternating layers of sensing and processing elements, along with light-emitting diodes (LED) that allow for the chip&rsquo;s layers to communicate optically. Other modular chip designs employ conventional wiring to relay signals between layers. Such intricate connections are difficult if not impossible to sever and rewire, making such stackable designs not reconfigurable.The MIT design uses light, rather than physical wires, to transmit information through the chip. The chip can therefore be reconfigured, with layers that can be swapped out or stacked on, for instance to add new sensors or updated processors.&ldquo;You can add as many computing layers and sensors as you want, such as for light, pressure, and even smell,&rdquo; says MIT postdoc Jihoon Kang. &ldquo;We call this a LEGO-like reconfigurable AI chip because it has unlimited expandability depending on the combination of layers.&rdquo;The researchers are eager to apply the design to edge computing devices &mdash; self-sufficient sensors and other electronics that work independently from any central or distributed resources such as supercomputers or cloud-based computing.&ldquo;As we enter the era of the internet of things based on sensor networks, demand for multifunctioning edge-computing devices will expand dramatically,&rdquo; says Jeehwan Kim, associate professor of mechanical engineering at MIT. &ldquo;Our proposed hardware architecture will provide high versatility of edge computing in the future.&rdquo;The team&rsquo;s results are published today in&nbsp;Nature Electronics. In addition to Kim and Kang, MIT authors include co-first authors Chanyeol Choi, Hyunseok Kim, and Min-Kyu Song, and contributing authors Hanwool Yeon, Celesta Chang, Jun Min Suh, Jiho Shin, Kuangye Lu, Bo-In Park, Yeongin Kim, Han Eol Lee, Doyoon Lee, Subeen Pang, Sang-Hoon Bae, Hun S. Kum, and Peng Lin, along with collaborators from Harvard University, Tsinghua University, Zhejiang University, and elsewhere.<h2>Lighting the way</h2>The team&rsquo;s design is currently configured to carry out basic image-recognition tasks. It does so via a layering of image sensors, LEDs, and processors made from artificial synapses &mdash; arrays of memory resistors, or &ldquo;memristors,&rdquo; that the team&nbsp;<a href="https://news.mit.edu/2020/thousands-artificial-brain-synapses-single-chip-0608" target="_blank">previously developed</a>, which together function as a physical neural network, or &ldquo;brain-on-a-chip.&rdquo; Each array can be trained to process and classify signals directly on a chip, without the need for external software or an Internet connection.In their new chip design, the researchers paired image sensors with artificial synapse arrays, each of which they trained to recognize certain letters &mdash; in this case, M, I, and T. While a conventional approach would be to relay a sensor&rsquo;s signals to a processor via physical wires, the team instead fabricated an optical system between each sensor and artificial synapse array to enable communication between the layers, without requiring a physical connection.&nbsp;&ldquo;Other chips are physically wired through metal, which makes them hard to rewire and redesign, so you&rsquo;d need to make a new chip if you wanted to add any new function,&rdquo; says MIT postdoc Hyunseok Kim. &ldquo;We replaced that physical wire connection with an optical communication system, which gives us the freedom to stack and add chips the way we want.&rdquo;The team&rsquo;s optical communication system consists of paired photodetectors and LEDs, each patterned with tiny pixels. Photodetectors constitute an image sensor for receiving data, and LEDs to transmit data to the next layer. As a signal (for instance an image of a letter) reaches the image sensor, the image&rsquo;s light pattern encodes a certain configuration of LED pixels, which in turn stimulates another layer of photodetectors, along with an artificial synapse array, which classifies the signal based on the pattern and strength of the incoming LED light.<h2>Stacking up</h2>The team fabricated a single chip, with a computing core measuring about 4 square millimeters, or about the size of a piece of confetti. The chip is stacked with three image recognition &ldquo;blocks,&rdquo; each comprising an image sensor, optical communication layer, and artificial synapse array for classifying one of three letters, M, I, or T. They then shone a pixellated image of random letters onto the chip and measured the electrical current that each neural network array produced in response. (The larger the current, the larger the chance that the image is indeed the letter that the particular array is trained to recognize.)The team found that the chip correctly classified clear images of each letter, but it was less able to distinguish between blurry images, for instance between I and T. However, the researchers were able to quickly swap out the chip&rsquo;s processing layer for a better &ldquo;denoising&rdquo; processor, and found the chip then accurately identified the images.&ldquo;We showed stackability, replaceability, and the ability to insert a new function into the chip,&rdquo; notes MIT postdoc Min-Kyu Song.The researchers plan to add more sensing and processing capabilities to the chip, and they envision the applications to be boundless.&ldquo;We can add layers to a cellphone&rsquo;s camera so it could recognize more complex images, or makes these into healthcare monitors that can be embedded in wearable electronic skin,&rdquo; offers Choi, who along with Kim previously developed a&nbsp;<a href="https://news.mit.edu/2021/smart-skin-vitals-0630" target="_blank">&ldquo;smart&rdquo; skin</a> for monitoring vital signs.Another idea, he adds, is for modular chips, built into electronics, that consumers can choose to build up with the latest sensor and processor &ldquo;bricks.&rdquo;&ldquo;We can make a general chip platform, and each layer could be sold separately like a video game,&rdquo; Jeehwan Kim says. &ldquo;We could make different types of neural networks, like for image or voice recognition, and let the customer choose what they want, and add to an existing chip like a LEGO.&rdquo;<hr>&quot;Reprinted with permission of&nbsp;<a href="https://news.mit.edu/2022/stackable-artificial-intelligence-chip-0613" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot;&nbsp;

MIT engineers have created a reconfigurable AI chip that comprises alternating layers of sensing and processing elements that can communicate with each other. Credit: Figure courtesy of the researchers and edited by MIT News

The new design is stackable and reconfigurable, for swapping out and building on existing sensors and neural network processors.

Engineers build LEGO-like artificial intelligence chip

There's never been a better time to dive into custom chip design, with both FPGA and ASIC design routes being more accessible than ever.

As critical components in contemporary computing, choosing between ASICs and FPGAs carries significant implications, underscoring the importance of understanding their unique strengths and trade-offs in the constantly evolving digital landscape.

ASIC vs FPGA: A Comprehensive Comparison

In this episode, we talk about how performance limitations of current microchips are being addressed by utilizing novel materials like graphene and bioinspired designs to address the needs of next generation computing and electronics. As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/4c563405-4ade-4b6a-9070-a75a7d327eef?dark=false" class="fr-draggable" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe><hr>This podcast is sponsored by <a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank">Mouser Electronics</a>. &nbsp; &nbsp;&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjU0MDAxNTk1MjgwLU1vdXNlci1zcG9uc29yLTEtYS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib"><hr>(3:46) - Graphene-hBN Computing(16:44) - Spiderweb MicrochipsEpisode 72 was brought to you by Mouser Electronics, Farbod &amp; Daniel’s favorite electronics distributor. Click <a href="https://www.mouser.com/applications/wide-bandgap-beyond-silicon/" target="_blank">here</a> to read the article discussing &nbsp;how scientists are looking beyond silicon for the future of computer chips.<hr>About the podcast:Every day, some of the most innovative universities, companies, and individual technology developers share their knowledge on Wevolver. To ensure we can also provide this knowledge for the growing group of podcast listeners, we started a collaboration with two young engineers, Daniel Scott Mitchell &amp; Farbod Moghaddam who discuss the most interesting content in this podcast series.&nbsp;To learn more about this show, please visit the <a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">shows page</a>. By following the page, you will get automatic updates by email when a new show is published.Be sure to give us a follow and review on <a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" rel="nofollow" target="_blank">Apple</a> podcasts, <a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" rel="nofollow" target="_blank">Spotify</a>, and most of your favorite podcast platforms!Take a few seconds to leave us a review. It really helps! <a href="https://apple.co/2RIsbZ2" rel="nofollow" target="_blank">https://apple.co/2RIsbZ2&nbsp;</a>if you do it and send us proof, we’ll give you a shoutout on the show.

In this episode, we talk about how performance limitations of current microchips are being addressed by utilizing novel materials like graphene and bioinspired designs to address the needs of next generation computing and electronics.

Podcast: Spiderweb Inspired & Graphene Enhanced Microchips

This is the final article in a 2-part series about choosing cellular or Wi-Fi.&nbsp;<a href="https://www.wevolver.com/article/cellular-vs-wifi-for-iot-how-to-choose-the-right-one-foundations" rel="noopener noreferrer" target="_blank">Read article one&nbsp;here.&nbsp;</a>There&rsquo;s no one &ldquo;best&rdquo; choice for every single IoT project. Choosing cellular vs. WiFi comes with tradeoffs that have to be understood and managed to find the best option for your deployment. Here are a few things to consider as you decide which technology would be best for your IoT project.<h2 dir="ltr">Device Location and Movability</h2>The first question that you should ask yourself is where your device is going to live and whether it&#39;s movable. For example, any device that&#39;s moving even a few-hundred feet, won&rsquo;t work on WiFi because it&#39;s not going to have access to the same WiFi network across those different places where it needs to operate. If the device is movable, then cellular connectivity would be your best bet. Key examples here would be IoT-connected light electric vehicles, whether they&rsquo;re part of a rideshare, privately owned, or used for last-mile delivery.If the device is going to be part of a mostly stationary asset that will have constant access to a reliable WiFi network, then WiFi can be a good choice as well.<h2 dir="ltr">Number of Devices</h2>A high concentration of connected devices may lend themselves to more to cellular connectivity. This is because the more devices connected to a single WiFi network, the more the signal degrades, potentially leading to lost connectivity. If you have a high concentration of devices in one location, cellular connectivity might be your best bet.<h2 dir="ltr" id="isPasted">The Desired End-User Experience</h2>If you want to provide a seamless onboarding experience for your customer, you want the device to be instantly connected to the Internet anywhere in the world, cellular would be your top choice.WiFi requires the end user to set up the network and ensure the device can connect to it. This isn&rsquo;t a problem if someone with the required technical expertise to do this is present, but can be difficult for those without.The ongoing <a href="https://www.particle.io/iot-guides-and-resources/what-to-know-about-3g-sunsetting/" target="_blank">3G sunsetting</a> and introduction of 5G and other advanced technologies is bringing down the cost of cellular connectivity and widening the network coverage. More and more, IoT deployments that were previously impossible to run on cellular due to no coverage or high cost are becoming technically and financially feasible. Cellular will get increasingly more valuable relative to WiFi with the benefits of 5G and technology innovation.<h2 dir="ltr">Security Needs</h2>Protecting your devices from bad actors is critical, especially if downtime or improper usage can lead to injury or equipment failure. In general, cellular networks offer considerable security advantages. They are encrypted by default, while encryption on WiFi connections must be enabled manually by the network owner. Additionally, security upgrades for cellular-connected devices happen automatically, while security patches must be installed manually for WiFi. If your devices are going to be connected to a public WiFi network, or if they&rsquo;ll be connected to a private network that may not be adequately protected by the owner, cellular would be a safer choice.<h2 dir="ltr">Cellular or WiFi? Why not use both with Particle.</h2>Particle, an integrated IoT Platform provides an opportunity for the best of both WiFi and cellular connection by working on the same Device OS operating system. Device OS offers an easy-to-use programming framework that makes it simple to write IoT applications that can connect via cellular or WiFi. It gives you the same interface no matter how you choose to connect to the Internet. &quot;Whether you build your specific logic for a hot tub, a scooter, or a water monitoring solution, all of that&#39;s built on the same software interface called Device OS, regardless of its WiFi or tracker or site. That makes it really useful for customers who have what we call heterogeneous fleets,&quot; &nbsp;Calvin Jepson, senior manager of solutions architecture at Particle explains.&ldquo;Heterogeneous fleets&rdquo; have multiple device types. If you&rsquo;re using Particle devices, you&rsquo;d be using a mix of the following:<ul><li dir="ltr"><a href="https://docs.particle.io/argon/" target="_blank">Argon</a> - a powerful Wi-Fi development kit</li><li dir="ltr"><a href="https://docs.particle.io/boron/" target="_blank">Boron</a> - a powerful cellular enabled kit</li><li dir="ltr"><a href="https://docs.particle.io/datasheets/asset-tracking/tracker-one/" target="_blank">Tracker/TrackerOne</a> - a ready-to-go tracker SoM (system on a module) carrier board with optional weatherproof enclosure.</li></ul>A heterogeneous fleet reduces technical debt associated with managing various projects and allows for switching between device/connectivity types for different stages of development.In some cases, people start by buying Argons since there&rsquo;s no subscription fee and you can develop firmware and conduct tests easily. Once it&#39;s time for them to start building the devices, they can switch from Argon to Boron and use the same code, which is a unique proposition that Particle can offer.&quot;Imagine you&#39;re a novice at squash, and you&#39;re a right-hand dominator. To get started quickly, you want to use your right hand. But once you join the major leagues, you have to use your left hand to be competitive,&quot; suggests Calvin. &quot;It&rsquo;s the same with IoT. You might be great at using cellular or WiFi during prototyping or at a small scale. But when you <a href="https://www.particle.io/iot-guides-and-resources/iot-scalability/" target="_blank">scale an IoT deployment</a>, you might need to use completely different technologies, redesign a lot of stuff, and redo a lot of the work that you already did to build a scalable solution to be market-ready.&rdquo;<h2 dir="ltr">Conclusion</h2>By understanding the pros and cons of cellular and WiFi technologies you will be able to make the right choice for your IoT device. IoT platforms, such as Particle are enabling innovation by providing easier routes to creating prototypes that can then easily scale at the appropriate time.&nbsp;<h2 dir="ltr">About the sponsor: Particle</h2><a href="https://www.particle.io/" rel="noopener noreferrer" target="_blank">Particle&nbsp;</a>provides an integrated IoT Platform-as-a-Service that helps businesses connect, manage, and deploy software applications to connected devices, from edge to cloud and back. Over 240k developers, and 160+ Enterprise customers are building on Particle, from fast-growing startups to Fortune 100 companies.&nbsp;Our expertise goes beyond world-class technology, enabling next-generation business intelligence, insights, and expert customer support to make sure IoT projects succeed. So you can build the business of tomorrow, today.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1652969762255-particle+bottom.jpg" style="width: 300px;" class="fr-fic fr-dib">

Choosing Cellular or WiFi Article #2: Learn how you can determine the best choice for your IoT deployment.


Choosing Cellular or WiFi for your IoT Project

Cellular and WiFi are the two most popular connectivity methods for IoT technology. Though many early IoT projects relied on WiFi, cellular technology has grown in popularity as it became better equipped to handle more use cases. Against the backdrop of continuous innovations in the wireless industry, choosing the best radio access technology for your IoT project has become challenging. In this first half of a two-part article, we&#39;ll examine the key differences between cellular and WiFi, show example use cases for each one, and explain how you can choose the right technology for your project.<h2 dir="ltr">Cellular VS. WiFi for IoT - A quick comparison</h2>There are several criteria you can use to compare cellular vs. WiFi, such as:<ul><li dir="ltr">Cost</li><li dir="ltr">Reliability and security</li><li dir="ltr">Location detection</li><li dir="ltr">Coverage</li><li dir="ltr">Data throughput</li><li dir="ltr">Device provisioning</li></ul>Let&rsquo;s dig into the details of each one to understand how cellular and WiFi are meaningfully different (or in some cases, similar).<h2 dir="ltr">Cost</h2>Cellular data plans are generally more costly than comparable WiFi plans on a per-byte basis. If your project requires recurring big data transfers, WiFi networks would be your best choice. However, when doing a cost comparison, you should factor in ongoing cost of maintaining your WiFi network as well as the cost of provisioning many devices across different WiFi networks that you as the IoT fleet operator may not control.Cellular is generally more expensive on a per device basis than WiFi but you should also take into account the cost of maintaining and managing the WiFi network as a part of the per-device cost when comparing the two.&nbsp;When comparing one-time hardware costs, cellular modems are more expensive than WiFi modems. That said, it&rsquo;s important to understand how either type of connectivity will influence the total cost of ownership of your IoT deployment.<h2 dir="ltr">Reliability and Security</h2>Part of what you&rsquo;re paying for in your cellular plans is world-class security and reliability. While it is possible to create comparable security and reliability over WiFi, you&#39;re relying on WiFi network owners &amp; building management teams to do that work on an ongoing basis. In contrast, cellular security is backed by knowledgeable cybersecurity specialists.When you&#39;re buying cellular, part of the costs is for the expert network management from the provider. However, when buying WiFi, network management is managed by yourself. Your own WiFi network generally provides a less stable network than the cellular solution does.<h2 dir="ltr">Location Detection</h2>There is not much difference between getting accurate location information via WiFi and cellular. However, in some circumstances where your device is out of range of WiFi, cellular networks will perform better, being able to mark your devices&rsquo; approximate location. In either case, getting location from WiFi SSIDs or cellular towers requires integrations with 3rd party providers which give location estimates based on a geographical database of network IDs.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjUxMzE1MTQ0OTgzLWltYWdlMy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 551px;" class="fr-fic fr-dib">Image 1: In some circumstances where your device is out of range of WiFi, cellular networks will perform better.<h2 dir="ltr" id="isPasted">Coverage</h2>WiFi is a great access technology when your deployed environment has WiFi coverage. This tends to be the case for devices deployed in homes or buildings, or in places where cellular networks are known to not have coverage such as oil rigs and mines. If you&rsquo;re planning to track things that are mobile, such as vehicles, or deployed in remote locations, then cellular is better.Although this is covered below, it bears repeating that cellular is often a great choice for stationary assets in urban and office environments because of the relative ease of onboarding a device to the cellular network compared to a WiFi network where you have to know passwords and get through network-specific security measures.<h2 dir="ltr">Data throughput</h2>Traditionally, WiFi performs faster than cellular networks. But with the development of 4G and 5G, the data throughput of cellular networks can be relatively on par with WiFi. Both cellular and WiFi networks are sufficient for most IoT projects.<h2 dir="ltr">Device Provisioning &amp; Setup</h2>It&rsquo;s much easier to deploy fleets of cellular devices when compared to WiFi. Installing a cellular fleet can be as simple as turning on the power to the device. Assuming that connected device has cellular coverage, it will automatically connect to the network.In contrast, with a WiFi device you may need to first set up your network or otherwise manage access to that network with usernames &amp; passwords for each device. This can be a deal breaker for many use cases where the WiFi network is heavily protected by the owner.<h2 dir="ltr">Benefits and drawbacks of cellular for IoT</h2><h2 dir="ltr">Pros:</h2><ul><li dir="ltr">Cellular has more robust privacy and security with automatic encryption.</li><li dir="ltr">Cellular service has more comprehensive coverage with no set range since you don&#39;t need a router. This is important for remote assets like oil rigs.</li><li dir="ltr">Obstacles do not as easily block deeper coverage with cellular signals.</li><li dir="ltr">Often better for use cases that need low power consumption</li></ul><h2 dir="ltr">Cons</h2><ul><li dir="ltr">Data plans can be expensive if your use case requires you to send large amounts of complex data.</li><li dir="ltr">Bundling SIM cards from different providers can force you to get your entire IoT deployment recertified. Be sure to understand what goes into <a href="https://www.particle.io/iot-guides-and-resources/iot-fcc-certifications/" target="_blank">FCC certifications for IoT</a>.</li><li dir="ltr">Devices can lose coverage in locations where cellular service is spotty.</li><li dir="ltr">Not always accurate when determining device location.</li></ul><h2 dir="ltr">Benefits and drawbacks of WiFi for IoT</h2><h2 dir="ltr">Pros:</h2><ul><li dir="ltr">Private WiFi connections are secure and enable fast access to connecting multiple devices.</li><li dir="ltr">Suitable for data-heavy transfers since there is no cost limitation on the amount of data transferred. A note here is that with the development of 5G, the advantages of WiFi relative to cellular might be eroded.</li></ul><h2 dir="ltr">Cons:</h2><ul><li dir="ltr">Public WiFi may not be secure.</li><li dir="ltr">Reliant on end user&#39;s WiFi. If they change their password or get disconnected, the device may go down.</li><li dir="ltr">The devices need to have access to a WiFi router. The limited range can be a barrier for remote assets.</li><li dir="ltr">Too many devices connecting to one router may degrade the signal.</li><li dir="ltr">WiFi configuration may require technical expertise.</li></ul><h2 dir="ltr">Best use cases for Cellular</h2>The use cases for cellular IoT are extensive. In this section, we&rsquo;ll share a few examples of where it&rsquo;s effective.<h2 dir="ltr">Deploying Continuous Emissions Monitoring</h2><a href="https://www.qubeiot.com/" target="_blank">Qube</a> was an early mover in response to emissions-cutting pledges that gained widespread attention following 2021&rsquo;s COP26 conference. Qube offers <a href="https://www.particle.io/iot-guides-and-resources/energy-production-continuous-emissions-monitoring-solution/" target="_blank">continuous emissions monitoring</a> solutions by combining proprietary algorithms that can detect greenhouse gas emissions and pinpoint their type and location with IoT sensors that provide a continuous data feed.Qube was looking for an affordable platform that could scale across the world while offering low power consumption. The team knew that cellular connectivity would be the right choice as many of their devices would be deployed in remote locations where WiFi wouldn&rsquo;t be available.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjUxMzE1NTE2NTQzLWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 553px;" class="fr-fic fr-dib">Image 2: By connecting monitoring devises via cellular networks, Qube created a continuous emissions detection system that detects sources of emissions and gives real-time insights on leak sources, quantity, and type. Image credit: QubeQube built its solution using IoT platform, Particle&rsquo;s, EtherSIM solution, a SIM card powered by over 350 carriers worldwide. No matter where Qube&rsquo;s devices are deployed, they will automatically connect to the best cellular network available.<blockquote>&ldquo;We rely on Particle&rsquo;s devices to get continuous data, which is streamed to our servers on a minute-by-minute or second-by-second basis. We then use that continuous data in our machine learning algorithms to infer what is actually happening in terms of leak quantity, leak size, and leak type,&rdquo; Cube COO Eric Wen said.</blockquote><h2 dir="ltr">Managing Remote Industrial Assets</h2>While the emissions monitoring sector is benefiting from global connectivity, those with <a href="https://blog.particle.io/the-top-5-applications-for-industrial-iot/" target="_blank">industrial IoT applications</a> are also finding key benefits from cellular connectivity.<a href="https://www.networkinv.com/solution/fieldintell-iiot-automation-tracking/" target="_blank">FieldIntell</a>, a cloud platform serving customers mainly in oil &amp; gas, agriculture, and mining, is also seeing a promising future of stable global-wise cellular connection.Particle provides global connectivity with hundreds of carriers worldwide, which saves FieldIntell&rsquo;s time spent on negotiating with carriers across multiple countries. FieldIntell&rsquo;s customers push Over-the-Air updates and remotely control complex assets with connectivity that&rsquo;s integrated with both the devices and cloud.<h2 dir="ltr">Best use cases of WiFi for IoT</h2>Cellular isn&#39;t always the best everywhere. If your devices mostly live in a facility with cement and metal all around it, signals from a cell tower aren&#39;t going to reach them. There are other use cases, like oil rigs in the middle of the ocean, where there&#39;s no cellular coverage. Under those circumstances, WiFi can be a superior choice. For example, remote oil rigs or mining operations generally have a satellite uplink and a local WiFi network. The same applies to industries like mining, which are usually based in remote locations where cellular networks haven&#39;t invested in putting a cellular tower.<h2 dir="ltr">Combatting the COVID-19 pandemic</h2><a href="https://auraair.io/" target="_blank">Aura Air</a> built a data-driven air quality monitoring system with a WiFi-connected IoT product that was designed to disinfect 99.9% of viruses, bacteria, mold, and VOCs from indoor air. Aura Air&rsquo;s devices can help combat the threat of COVID-19. With the help of IoT technology, the product can detect influenza and COVID-19 viruses and sterilize the air to reduce spread. By harnessing Particle&rsquo;s WiFi module P1 SKU, the Aura Air devices can be connected through local WiFi networks of 2.4 GHz that do not have a firewall enabled. Users can view and update real-time air quality data through their phones or central dashboards.&nbsp;<h2 dir="ltr">Conclusion</h2>As the IoT and the wireless industry continue to innovate, selecting the right radio access technology for your IoT project is increasingly challenging. However, by understanding the pros and cons of each technology and examining similar use cases the decision can become clearer. In the second half of this article, we&rsquo;ll dive deeper into the strengths and weaknesses of each one and how you can determine the best choice for your IoT deployment.<h2 dir="ltr">About the sponsor: Particle</h2><a href="https://www.particle.io/" rel="noopener noreferrer" target="_blank">Particle</a>&nbsp;provides an integrated IoT Platform-as-a-Service that helps businesses connect, manage, and deploy software applications to connected devices, from edge to cloud and back. Over 240k developers, and 160+ Enterprise customers are building on Particle, from fast-growing startups to Fortune 100 companies.Our expertise goes beyond world-class technology, enabling next-generation business intelligence, insights, and expert customer support to make sure IoT projects succeed. So you can build the business of tomorrow, today.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1652969804194-particle+bottom.jpg" style="width: 300px;" class="fr-fic fr-dib">

Choosing Cellular or WiFi Article #1:Cellular and WiFi connectivity have their own distinct benefits for IoT. Learn about the strengths and weaknesses and application examples of each one. 


Cellular VS. WiFi for IoT How to Choose the Right One: Foundations

<h2 dir="ltr" id="isPasted">What is (and isn&#39;t) a microcontroller?</h2>A microcontroller unit (MCU) is a small, self-contained computer that is housed on a single integrated circuit, or microchip. They differ from your desktop computer in that they are typically dedicated to a single function, and are most often embedded in other devices (e.g. cellphones; household electronics).<h2 dir="ltr">What&#39;s the difference between a microcontroller and a microprocessor (MCU vs MPU)?</h2>Microcontrollers also differ from microprocessors. Microprocessors contain only a CPU, and therefore require added peripherals to perform tasks. MCUs, on the other hand, contain RAM, ROM, and similar peripherals, which allow them to perform (simple) tasks independently.Ultimately, despite similar names and appearances, microcontrollers and microprocessors differ widely in their applications. Microprocessors are more powerful, but must be employed as single components in larger systems to function.&nbsp;Microcontrollers, meanwhile, are limited in power and functionality, but can perform simple functions independently. Your Apple TV, for example, requires a microprocessor to handle all the varied and demanding tasks it performs. A connected coffeemaker, on the other hand, only needs to perform simple routines and tasks, and therefore employs a microcontroller.<h2 dir="ltr">What is the difference between a microcontroller (MCU) and a system-on-a-chip (MCU vs SoC)?</h2>The other term one hears often in this discussion is System on a Chip (SoC). The distinction between an MCU and an SoC is much less clear, and the two terms are often used interchangeably. However, in common usage, the term SoC typically refers to MCUs with a greater number of onboard peripherals and functionality. For the purposes of this outline, we won&rsquo;t make a distinction between MCUs and SoCs.<h2 dir="ltr">Why use an MCU for IoT</h2><ul><li dir="ltr">Simplicity: In most IoT use-cases, the relative simplicity of an MCU is an advantage rather than a disadvantage. MCUs don&rsquo;t require operating systems to function, and are easy to interface with external devices such as sensors and motors. Their lack of external dependencies also makes them easy to set up. You can simply turn them on, upload firmware, and they work. Additionally, the coding required to program an MCU is minimal.</li><li dir="ltr">Security: By virtue of their relative simplicity, MCUs also offer fewer avenues of attack. As a rule, each open port and available protocol is also a potential vulnerability. Code on MCUs runs &ldquo;bare metal&rdquo;, meaning it includes no intermediary operating system to execute instructions. This results in limited potential attack vectors and increased inherent security.</li><li dir="ltr">Cost: In most contemporary IoT applications, an MCU can deliver all the processing power and functionality one needs. As a result, MCUs are most often the best, most economic hardware choice for IoT applications. Overall, they offer simple, secure, functionality for little cost.</li></ul><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjUxMjU3Nzk1MjI1LXNodXR0ZXJzdG9ja181Nzg2MjY0NzcgKDEpLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 555px;" class="fr-fic fr-dib">Image 1: Security, cost, and simplicity are key factors in choosing an MCU for your IoT project.<h2 dir="ltr" id="isPasted">Which MCU is right for you?</h2><ul><li dir="ltr">Tech Specs: Which technical specifications your MCU should have will depend entirely upon its intended application. The relative importance of specs such as processing speed and memory capacity will be different from use-case to use-case. For example, An IoT device meant to perform more demanding tasks will necessitate an MCU with a higher clock speed. Less processing-intensive tasks, however, may not necessitate such high-performance hardware. That&rsquo;s why it is imperative that you clearly determine your technical needs before deciding upon a specific piece of technology. Overall, primary spec considerations include: memory, speed, power use, security features, and connectivity type (Bluetooth/Wi-Fi/Cellular).</li><li dir="ltr">Cost: All these technical specifications will be invariably weighed against their cost. Although the cost of MCUs is rapidly decreasing, added power, memory, and other specs will generally result in increased costs. While the costs of hardware may not seem daunting initially, increases in scale can quickly amplify even minute differences in hardware price. At the same time, selecting an MCU with only the bare minimum specs required to function may leave you unable to implement updates, new features, and other improvements in the future.</li><li dir="ltr">Support: When selecting an MCU, it&rsquo;s also important to consider the community surrounding it. MCUs with limited adoption will have fewer resources available to aid you in development.</li><li dir="ltr">Devices like the Photon from Particle, have a large community of users and developers, which makes it much easier to find information and support when needed. With a community of over 100,000 developers and engineers, the Particle platform has a wealth of resources available, including sample projects, demos, and development tools.&nbsp;</li><li dir="ltr">By selecting a more widely adopted MCU, you will ensure that integrating your IoT device with existing cloud services will be easy.</li></ul><h2 dir="ltr">Looking beyond the MCU</h2>Your IoT product is a system, not a component. So, when planning any element of your design, it&rsquo;s important to take into consideration the full stack of hardware, software, and infrastructure. Obviously, your product needs to connect to the Internet to qualify as IoT.How it does so, and how that data is managed, should be central considerations in your IoT development process. In the past, developing, hosting, and managing one&rsquo;s own cloud infrastructure was a necessary part of IoT implementation. Now, platform providers such as Particle are offering cloud-connected hardware and comprehensive IoT platforms that lower the total cost of ownership of an IoT project, as well as reduce the time to market.<h2 dir="ltr">Begin your IoT undertaking with Particle</h2>A piece of IoT hardware is only as good as the platform it lives on. As an integrated IoT platform-as-a-service Particle provides everything you need to connect your device to the web. Pre-configured modules like the Electron are ready to connect to the internet right out of the box, and are already FCC/CE/PTCRB certified. These integrated platforms are allowing entrepreneurs to focus on their products, rather than the complications of connectivity. Particle offers a variety of low-cost, Wi-Fi and Cellular MCU dev kits that are easy to set up, and simple to integrate with your organization&rsquo;s existing infrastructure.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1652969871830-particle+bottom.jpg" style="width: 300px;" class="fr-fic fr-dib">

The ins and outs of microcontrollers, and an overview of the difference between MCU and microprocessors (MPU).


Selecting a microcontroller (MCU) for your IoT product

This country’s semiconductor chip shortage is likely to continue well into 2022, and a Georgia Tech expert predicts that the U.S. will need to make major changes to the manufacturing and supply chain of these all-important chips in the coming year to stave off further effects.That includes making more of these chips here at home. &nbsp;Madhavan Swaminathan is the John Pippin Chair in Electromagnetics in the School of Electrical and Computer Engineering. He also &nbsp;serves as director of the 3D Systems Packaging Research Center. &nbsp;As an author of more than 450 technical publications who holds 29 patents, Swaminathan is one of the world’s leading experts on semiconductors and the semiconductor chips necessary for many of the devices we use every day to function.&nbsp;“Almost any consumer device that is electronic tends to have at least one semiconductor chip in it,” Swaminathan explains. “The more complicated the functions any device performs, the more chips it is likely to have.”&nbsp;Some of these semiconductor chips process information, some store data, and others provide sensing or communication functions.&nbsp;In short, they are crucial in devices from video games and smart thermostats to cars and computers.&nbsp;Our current shortage of these chips began with the Covid-19 pandemic. When consumers started staying at home and car purchases took a downward turn, chip manufacturers tried to shift to make more chips for other goods like smartphones and computers.&nbsp;But Swaminathan explains that making that kind of switch is not simple. Entire production operations have to be changed. The chips are highly sensitive and can be damaged by static electricity, temperature variations, and even tiny specks of dust. The manufacturing environments must be highly regulated, and changes in the process can add months.&nbsp;The pandemic highlighted another challenge with the semiconductor chip industry, according to Swaminathan.&nbsp;“There’s a major shortage of companies making chips,” he says. “If you look worldwide, there are maybe four or five manufacturers making 80-90% of these chips and they are located outside of the United States.”&nbsp;This creates supply chain hiccups with the raw supplies needed to make these chips as well. Add in the fact that many of these companies only design their chips – they don’t manufacture them directly.&nbsp;“American consumers use 50% of the world’s chips,” Swaminathan says, which creates a serious challenge when the overwhelming majority of those chips are manufactured in other nations.&nbsp;In the short term, the costs of the chip shortage is being passed on to the consumer. We see this directly with products like PlayStations and Xboxes that are more and more expensive and harder to purchase when the chips necessary for the consoles to function are in short supply.&nbsp;Beyond 2022, Swaminathan says we need to work to revitalize the industry domestically.&nbsp;“We need to bring more manufacturing back to the United States,” he says. “The U.S. government has recognized the importance of this semiconductor chip shortage and is trying to address the issue directly.”&nbsp;That means investing in new plants to manufacture the chips, but America's journey toward &nbsp;chip self-sufficiency will continue to be a work in progress.“This is a cycle,” Swaminathan explains. “But this is probably the first time where it has had such a major effect in so many different industries.”&nbsp;But consumers can take direct action on their own in the coming year. “Reduce the number of times you purchase or upgrade electronic devices like phones and cars,” he says. “Then it becomes just a supply problem, not a demand and supply problem.”

Georgia Tech expert predicts that America will need to make major changes to the manufacturing and supply chain

Addressing the Microchip Shortage

For most of our nRF52 Series, we have a chip-scale package and a Quad-Flat No-leads (QFN) option. With our nRF52840, we introduced the aQFN package to our product portfolio. We also decided to offer the same device in a QFN package, so Nordic customers now have the option of using nRF52840 in QFN48. This has just been released to the market.There are two ways of looking at this new option. You can see it as a less complex nRF52840 with fewer pins and 1.7-3.6V supply voltage for our customers that don’t need USB and don’t need to run directly off Li-Ion batteries. It is also similar to an nRF52832 with additional security features, more flash and RAM as well as Zigbee and Thread support.I used this opportunity to read up on different package options, and reeducate myself. I will share with you what I learned.<h2>Then and now</h2>The first microcontroller I got in contact with was in a Dual in-line package (DIP). You don’t see them much nowadays, but DIP packages are still beloved by hobbyists. They are easy to solder, and you can do a software update with a screwdriver. Just pop out the microcontroller with a screwdriver and put a new one with updated software into the socket. This is not a modern or resource-friendly approach, but it works if you sell &lt;1000 devices a year that do not need much intelligence. One company I know of still uses this method in some of their older machines.On today’s microcontrollers, you mostly see Quad Flat Packages (QFP), QFN, and Wafer Level Chip Scale Packages (WLCSP). When it comes to QFP, you will see a lot of Low profile Quad Flat Packages (LQFP) and Thin Quad Flat Packages (TQFP). These are surface-mounted packages with the leads extended out to the four sides of the package.They have been around for decades and are still widely used. They are still solderable by hand (which I do not recommend), and the extended leads allow for visual inspection after assembly. They also make the solder joints more resilient to thermal and mechanical stress than leadless packages.The extended leads are also what cause trouble with these packages. If one of the leads gets bent during transport or assembly, that integrated circuit (IC) becomes unusable for automated assembly. Having all the pins on the side of the package also leads to bigger packages in general.The length of the pins is also why you do not see Nordic using any QFP package variants. The length impacts the inductance. Moreover, the capacitance between the pins is heavily impacted as the pins are parallel to each other. This increases the noise levels and coupling between the pins, particularly for devices using GHz radio.<h2>QFN</h2>QFN packages, as the name suggests, do not have extended leads. This reduces the coupling and noise between the pins. The contacts to the PCB are on the bottom of the package. Most packages also have an exposed thermal pad on the bottom, attached to the IC. It is usually grounded, which reduces the noise level further.QFN packages are a lot smaller than QFP-variants with the same number of pins. Some benefits are clear, like the smaller footprint and lower weight. No external leads and shorter bond wires mean reduced lead inductance. Besides, less metal is used since most bond wires use gold or copper.In short: Smaller package and better thermal and electrical performance, but visual inspection gets harder.<div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1635922759572-QFN48+6+x+6+mm+.png" style="width: 765px;" class="fr-fic fr-dib">Figure 1 QFN48 6 x 6 mm package&nbsp;</div><h2 id="isPasted">WLSCP</h2>Array-type packages is the way to go for devices requiring high pin density. BGA is mostly used for big processors with hundreds of pins. For that reason, I will not write too much about BGA itself but focus on WLCSP here.When you read Chip-scale, it usually means the package is not more than 20% larger than the die itself. I mentioned before that QFN was a small package compared to other options. Let me put that into perspective. For our nRF52832, the QFN48 is 6x6 mm, and the WLCSP is 3.0x3.2 mm. So, the WLCSP is less than 30% of the area of the QFN. The pins are on the bottom of the chip and not only on the sides, which means we can drastically reduce the size and don’t have to use bond wires.For WLSCP, the die will be painted with UV protective paint, or a thin plastic layer is added on the top of the chip. To make the pins accessible, we add wiring to the bottom and place the lead balls like in a BGA.WLCSP is mostly chosen because of cost or size. It’s generally the cheapest option for the manufacturer, although varying with die size, pin count, etc. A 73-pin device will be more expensive than the same die with only 48 pins.WLCSP denseness usually requires more aggressive PCB designs. For a smaller design, you will also need via in pad or buried vias. This is great for reducing size but drives assembly costs up. However, since these are usually needed for size-constrained devices, this is not an added cost of the WLCSP.The main drawbacks are less resilience to mechanical stress and a more complex assembly process with additional layers on the PCB.<div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1635922780959-WLCSP+3.544+x+3.607+mm+.png" style="width: 765px;" class="fr-fic fr-dib">Figure 2 WLCSP 3.544 x 3.607 mm package&nbsp;</div><h2 id="isPasted">QFN or aQFN</h2>Advanced Quad Flat No-lead (aQFN) is an improvement on the QFN for higher pin counts. It has multiple rows of pins on the bottom of the package as well as the exposed thermal pad. A 73 pin package would then be 9x9 mm or 10x10 mm as a QFN72 and about half as an aQFN73 (7x7 mm).With this package, we have even shorter bond wires than with the QFN and, therefore, less inductance. Again, a step up in terms of thermal and electrical performance.The main drawback with an aQFN package compared with a QFN package is that it requires more advanced PCBs. We typically see the need to go to 4 layers as well as more aggressive via and track width rules to be able to use all the pins, especially the pins on the inner ring.<div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1635922801882-aQFN%E2%84%A273+7+x+7+mm+package.png" style="width: 765px;" class="fr-fic fr-dib">Figure 3 aQFN™73 7 x 7 mm package&nbsp;</div>To give you an idea of the different sizes, here is a comparison of a QFN, an aQFN, and a WLCSP with similar pin count.&nbsp;<div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1635922826228-QFN%2C+aQFN+WLCSP+size+comparison.png" style="width: 765px;" class="fr-fic fr-dib">Figure 4 QFN, aQFN WLCSP size comparison&nbsp;</div><h2 id="isPasted">What does the PCB look like?</h2>To finish off the package overview, let’s look at some PCB designs for different packages. I have selected one of our nRF52833 reference layouts for each package option. We have a QFN, an aQFN, and a WLCSP package for the nRF52833.If you want to look at them for yourself, we have reference layouts for all our products <a href="https://www.nordicsemi.com/Products/Low-power-short-range-wireless/nRF52833/Downloads#infotabs" rel="noopener" target="_blank">in the downloads part of the product page</a>. This is where you’ll find the layouts I show here.<div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1635922847663-QFN+pcb.png" style="width: 766px;" class="fr-fic fr-dib"></div>This picture shows that we can break out the device and route all signals on one layer when using a QFN. You then use vias to ground the thermal pad. Those vias help reduce the inductance and improve thermal coupling as well as improve the mechanical attachment of the SoC.<div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1635922870010-aQFN+pcb.png" style="width: 766px;" class="fr-fic fr-dib"></div>If you are using an aQFN, you have the pins in two rows underneath the SoC. You will need at least two separated layers to route all signals. However, the area we use on the PCB is smaller, and we can place additional components closer to the Soc because we do the routing underneath.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1635922938371-WLCSP+pcb.png" style="width: 766px;" class="fr-fic fr-dib">If we now look at one of the reference designs for the WLCSP, we can see that we need another additional layer to access all the device’s pins. We also use even less area on the PCB for this package.<h2>Why nRF52840 in a QFN48 package?</h2>The short answer is more options for our customers. The nRF52840 in QFN48 (nRF52840-QF) does not support USB. Also, VDD and VDDH are connected inside the QFN48 package. Therefore, the device is only usable in normal voltage supply mode (1.7-3.6V). The customers using our nRF52832 could see this as an SoC in the same package with double the Flash, four times the RAM, Cryptocell 310, Zigbee, and Thread.It is also a cheaper and smaller version of the nRF52840 in aQFN73 (nRF52840-QI) if you don’t need the USB, 5,5V, or the additional pins. You might also be able to reduce the layers on the PCB. This is a great option for Smart home applications using Matter or Apple's HomeKit that don't require more than 30 GPIOs. There is ample memory for the different protocols and device firmware updated.<table id="isPasted"><tbody><tr><td>Package</td><td>LQFP</td><td>QFN</td><td>aQFN</td><td>WLCSP</td></tr><tr><td>Package cost</td><td>High-&nbsp;much material needed-&nbsp;longest bond wires</td><td>Less-&nbsp;smaller package-&nbsp;shorter bond wires</td><td>Effective-&nbsp;even less material used</td><td>Less-&nbsp;with least material used</td></tr><tr><td>PCB</td><td>Large area-&nbsp;big packageFew layers-&nbsp;no additional layers to access the pins</td><td>Medium area-&nbsp;Smaller package than LQFPFew layers-&nbsp;no additional layers to access the pins</td><td>Small area-&nbsp;Even smaller package-&nbsp;Components can be closer to the PCBAdditional layers-&nbsp;Needs additional layers to access pins</td><td>Minimal area-&nbsp;Chip scale package-&nbsp;More space for other componentsMore additional layers-&nbsp;Needs more additional layers to access pins</td></tr><tr><td>Design</td><td>Simple design</td><td>Simple design</td><td>More complex design</td><td>Most complex design</td></tr><tr><td>Assembly</td><td>Easy assemblybut leads can be bent during transport or assembly</td><td>Easy assembly</td><td>Complex assembly</td><td>Complex assembly</td></tr></tbody></table><hr>This article was first published on Nordic's&nbsp;<a href="https://blog.nordicsemi.com/getconnected" target="_blank" rel="nofollow" id="isPasted"></a><a href="https://blog.nordicsemi.com/getconnected?utm_campaign=2021%20wevolver" rel="nofollow" target="_blank">Get Connected Blog</a>.

For most of our nRF52 Series, we have a chip-scale package and a Quad-Flat No-leads (QFN) option. With our nRF52840, we introduced the aQFN package to our product portfolio.

QFN and aQFN options for the nRF52840

Taming the AI Demon - Arnaud Hubaux (ASML), Senior Technical Program Manager AINot AI but domain knowledge is what sets you apart from the restWhy should you invest in AI now? Both the expected ROI and risks are still highly insecure. Nevertheless, ASML invests heavily in applying AI. The company is not only one of world’s most successful high-tech companies and world-leader in making lithography machines but also a true leader in AI. Arnaud Hubaux (ASML), senior technical program manager AI, shares his lessons learned. His key take-away during a lecture at the AI Leadership Forum: AI on its own is not a Unique Selling Point but a commodity. Defining your use cases and applying AI in a smart way is what sets you apart from the rest. Your domain knowledge is what distinguishes you from your competitors.“Much is still unknown when it comes to AI, “says Arnaud Hubaux during a session organized by AI Innovation Center. “A lot of leaders in companies around me wonder if they should invest in AI. Reports promise the moon, but a lot is still unknown and the expected Return On Investment is still insecure. So why should you get on this train now?”Keep the end-customer in mind “But how do you decide? And how do you get an overview of the risks? I always keep my end customers in my mind. They all have dreams of their own. They want to explore space or make nanobots that cure cancer, or they want to solve mobility issues. They have one thing in common, they all need chips that are produced by using our machines. The linking pin is the silicon wafer. Our machines turn the chip designs into silicon chips that are produced on the silicon wafers.”Making chips faster and cheaper “Chip manufacturers want to produce as many high-quality chips as is possible per day. Manufacturers of devices want to produce as many high-quality devices as possible per day. It is about good chips and affordable chips, affordable devices. That is where Moore’s law kicks in. It is not just about having continuously more power but also about making it cheaper.”Demand for better litho performance “This requires new chip designs, which requires chip manufacturers to keep on producing new types of machines. This is how the industry works. Innovation is key. The demand for new machines generates R&amp;D budgets. ASML uses these budgets to continuously improve litho performance, our core business.”Using AI to innovate “This is where ASML’s thinking about AI starts. You look at what you can use AI for. This boils down to three things; 1. reducing costs, cost of machines, the cost of ownership for customers 2. reducing time to market and 3. improving customer value. Reducing costs for instance can be done by improving your chip defect rate detection by using deep learning technology or by detecting defective coating, by segmenting and classifying images to zoom in on damaged areas, qualification of parts goes faster.”Risks of applying AI “So AI can be useful and brings value, but AI also brings along certain risks. Let us call this the AI demon. These are a few examples:1. AI makes you build solutions that are unexplainable, this is due to models. Models learn from the input you give them and try to predict some form of output. A model tries to figure out some form of correlation but as we all know correlation is not causation. So, then you know something does not function well, but you still do not know why. If you do not understand and cannot explain why things go wrong that is not satisfactory to your customer, your team members cannot fix it and you neither improve the machine designs nor innovate. &nbsp;2. Context dependency is a second issue. Once you have discovered a defect you tweak everything in house in a lab situation, accordingly you achieve the accuracy level that you were aiming for but the model is sensitive to small variations. So, once you deploy it, you notice that it does not work.3. Machine Learning and AI are a commodity. You have to be aware and accept the fact that models and algorithms can be copied easily. Six months after you have built a model, your customers and competitors can do the same. AI is not what gives you a competitive advantage.”Delivering sustainable innovation by using AI &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; “Then why should you invest in AI? By countering the risks, so by ensuring your output is explainable, context-specific and by accepting that AI is a commodity you can make AI work for you. There are four basic steps your system can go through. They are displayed in the image below. The data comes from the system, from there you can calculate features (they are in fact physical models that give more meaning to these raw data). Next you put this input in a machine learning model, the output of that model can then give you several indications why the system is not ok or present the root cause with a certain likelihood. When it is a recurring issue, you want to schedule some maintenance on the system.” <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1622451400990-asml.png" style="width: 748px;" class="fr-fic fr-dib">Invest in the blue boxes “The blue boxes are your domain knowledge, the unique knowledge that you are bringing to market. These are the subjects you should invest your time and money in: the physical features and the symptom analyses. This means that when you have data that derives from physics or from historic equipment corrections for instance you should use them instead of trying to predict based on data by using an AI-model.”Build in autonomous retraining and include customer in feedback loop “When it comes to the context specific element, beware of the fact that variations occur, especially when you are in the manufacturing field, that is completely normal behavior. That is why besides your real-time prediction, you should build in an autonomous retraining system, to make sure that the AI model monitors itself to stay accurate over time. Finally, include the end customer in your feedback loop. The customer can tell you if the result is acceptable. You must include that in your model.”Define the use-case in which AI can make a difference “Finally in most cases it is not necessary to build AI models yourself. As I’ve said before it is a commodity. By using existing models, you can go a very long way. What makes the difference is the quality of your data and the quality of your physical models. If you know your industry, then focus on those use-cases in which AI can make a difference. Make sure that is explainable and context specific. Your unique differentiator is what makes you successful and that is not Artificial Intelligence in itself. AI helps ASML to optimize for a very specific context, by adding a layer on top of what we do we can really squeeze even more performance of our system.”

Arnaud Hubaux, Senior Technical Program Manager at ASML

AI on its own is not a Unique Selling Point but a commodity. Defining your use cases and applying AI in a smart way is what sets you apart from the rest. Your domain knowledge is what distinguishes you from your competitors.

microchip

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