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The global electronics manufacturing landscape is witnessing a massive structural realignment. According to recent trade data, India’s electronics exports surged by 11.62%, crossing the $5 billion milestone.
While the headline growth indicates strong momentum for India’s “Make in India” initiative, a deeper look at the data reveals a dramatic geopolitical and supply chain pivot: a sharp drop in trade with the United Arab Emirates (UAE) countered by an aggressive diversification into the United States (US) market.

The UAE Drawdown and Macro-Geopolitical Disruptions

Historically, the UAE has served as a primary re-export hub and secondary market for Indian-manufactured hardware. However, intensifying geopolitical tensions in the Middle East have severely strained traditional shipping lanes and trade corridors.
The impact on tech logistics is stark:

• The April Downturn: Electronics exports to the UAE nosedived, accounting for a mere 6.41% of India’s total electronics export basket in April.
• The Macro Shift: This represents a massive decline from the 2025-26 fiscal year, where the UAE captured 11.03% of India’s total tech exports, consuming over $5 billion worth of electronic goods.

Category-Specific Hit to Components & Hardware

The contraction wasn’t localized to just consumer units; it disrupted multiple hardware tiers where the UAE previously held dominant buyer positions:

• Smartphones: The UAE was formerly the second-largest buyer of Indian-assembled smartphones, representing a $4 billion+ segment.
• Enterprise & Infrastructure: The region plummeted from its status as a top destination for computer hardware and the third-largest destination for core electronic components.
• Regional Contraction: Parallelly, tech shipments to Israel dropped by 40% in April—notably impacting consumer electronics, printed circuit boards (PCBs), and telecommunication transmission equipment.

The US Tech Corridor: Absorbing the Supply Chain Deficit

This strategic shift completely offset regional losses 65% Exponential Surge: Electronics exports specifically to the US surged by 65%.

Net Positive Growth: This massive redirection of hardware volume pushed India’s total electronics export growth up by 24.4% in the tracked period, completely neutralizing the Middle Eastern bottleneck.

Silicon and Circuit Boards: What This Means for Global Hardware Sourcing

For enterprise hardware buyers, infrastructure architects, and supply chain officers, this pivot underscores two major trends:

• India’s Maturing EMS (Electronics Manufacturing Services) Ecosystem: The capacity to rapidly redirect billions of dollars in highly sensitive components—such as PCBs, transmission gear, and enterprise computer hardware—from one global superpower destination to another proves that India’s manufacturing logistics are becoming highly agile.
• De-risking is No Longer Theoretical: The 65% spike in US consumption shows that American enterprise tech pipelines are actively integrating Indian-fabricated hardware to establish multi-layered, resilient supply chains independent of traditional East Asian single-source hubs.

As India moves further up the value chain from basic smartphone assembly to complex multi-layered PCBs and enterprise computing systems, expect the US-India hardware corridor to solidify as a foundational pillar of global technology infrastructure.

The post India’s Hardware Shipments Surge 11.6% Amid Middle East Supply Chain Shifts appeared first on ELE Times.

Going the MVNO route involves potential risks for cellular companies and their customers alike…but also lots of possible upsides.

In conjunction with my recent deeper re-engagement with EDN from an employment standpoint, I not only reconfigured an existing computer in a LAN-isolating fashion but also set up a separate work mobile phone line. For hardware, I pulled out of storage the Google Pixel 7 that I’d mothballed at the conclusion of my prior “day job”. And since I was now on my own from a cellular provider-and-plan selection standpoint, I decided to finally give Google Fi Wireless a try.

The enemy of my enemy is my friend?

Originally known as Project Fi, then Google Fi (with “Wireless” more recently stuck on the end), this particular provider is, like US Mobile and others both in the US and around the world, a Mobile Virtual Network Operator (MVNO). Wikipedia’s entry starts by describing a MVNO as:

A wireless communications services provider that does not own the wireless network infrastructure over which it provides services to its customers. An MVNO enters into a business agreement with a mobile network operator (MNO) to obtain bulk access to network services at wholesale rates, then sets retail prices independently. An MVNO may use its own customer service, billing support systems, marketing, and sales personnel, or it could employ the services of a mobile virtual network enabler (MVNE).

That high-level description doesn’t dive into the minutia of various MVNO implementation variants, nor do I plan to do so here. Broadly speaking, I’ll stick with the high-level observation that it’s an intriguing business model that sometimes leads to startup company “flameouts”. Other times, it results in acquisitions by prior partners, with the MVNO either fully subsumed by the purchaser or maintaining a separate marketing identity and subsequently referred to as a “flanker brand” or as a “captive” versus prior “independent” MVNO. And some companies, such as Google Fi Wireless, remain independent MVNOs long-term.

MVNOs, perhaps obviously, don’t need to shoulder the substantial incremental costs of building out and maintaining a nationwide cellular network. Nor do they need to spend the money necessary to both secure and retain spectrum licenses. And, because they’re generally smaller, their promotional budgets are also leaner than their Mobile Network Operator (MNO) partners. But they also can’t be freeloaders, leading to the obvious next question…what’s in it for the MNO? Incremental revenue (taking the form of bulk, per-customer and/or per-packet regular payments) from MVNO partners, generated by the incremental use of any available excess network resources that would otherwise lie fallow, i.e., go to waste.

Therein lies the potential risk with MVNOs: if the MNO’s own customers consume the entirety of network capacity, there’ll be none left over for the MVNO’s own customers. Averting this outcome requires both upfront and ongoing negotiations between the MVNO and MNO, with “throttling” (dynamic bandwidth adjustments in reaction to capacity utilization changes) an interim step prior to, and hopefully also preventing, MVNO service shutoffs at heavy-use times.

An encouraging first date…

Google initially launched its MVNO service in 2015 alongside the Nexus 6 smartphone, in partnership with both Sprint and T-Mobile; the latter subsequently acquired the former in 2020. Google Fi Wireless currently comes in four plan tier options, all delivering 5G data rates:

• Flexible (“Pay for the data you use”)
  • $35 per month for 2 lines + data ($18 per line + $10/GB), for example, or $20 per month for 1 line + data
  • Data for $10/GB
  • High-speed hotspot tethering
  • International data in 200+ destinations
  • Connectivity for tablets and laptops
  • Full connectivity for select smartwatches
• Unlimited Essentials (“Our most affordable plan”)
  • $60 per month for 2 lines ($30 per line), for example, or $35 per month for 1 line
  • 30 GB of high-speed data
  • Full connectivity for select smartwatches
• Unlimited Standard (“Hotspot tethering for your devices”)
  • $80 per month for 2 lines (or $40 per line), for example, or $50 per month for 1 line
  • 50 GB of high-speed data
  • 25 GB of high-speed hotspot tethering
  • International data in Canada and Mexico
  • Full connectivity for select smartwatches
• Unlimited Premium (“Maximum perks & global connectivity”)
  • $110 per month for 2 lines (or $55 per line), for example, or $65 per month for 1 line
  • 100 GB of high-speed data
  • 50 GB of high-speed hotspot tethering
  • International data in 200+ destinations
  • Connectivity for tablets and laptops
  • Full connectivity for select smartwatches
  • 6 months of YouTube Premium
  • 100 GB of storage with Google One

The above pricing is standard; transient and varying-details pricing promotions that dip below the “MSRPs” are common. I’d even suggest that promotions are the rule versus the exception, not only with Google Fi Wireless but other MVNOs more broadly, further extending to all mobile operators more generally. For my new work line (in which, judging from the phone calls, voicemails and text messages I’ve subsequently received, I inherited a phone number formerly used by a Verizon customer named Robert with really bad credit), I went with the Unlimited Standard plan, since it included hotspot support as well as data support for my Pixel Watch. And at the time I signed up, since I was bringing my own phone, Google was offering half-off the published monthly rate for the first year. $25 per month for one line of service: not too shabby.

…led to a deeper relationship

After a few weeks of trying out the Google Fi Wireless, positively assessing both the company’s customer service and coverage robustness (particularly important in my Rocky Mountains foothills rural locale), I belatedly switched my personal line from AT&T over to Google Fi Wireless, too. I’d been on AT&T since mid-2010 (I’d switched to it from T-Mobile, ironically; nothing like going full circle!) the data portion of the monthly fee was “true” unlimited (i.e., non-throttled) and originally $30. By the time I canceled nearly sixteen years later, it had risen to $45/month but added visual voicemail and had also migrated from 3G (UMTS HSPA) to (4G LTE). More generally, here’s a breakdown of what I was paying per month:

I’d (too long) clung to it primarily due to its true-unlimited data nature; AT&T no longer offered this specific plan option, but I was “grandfathered” in as long as I didn’t cancel. As just noted, AT&T had upgraded it from 3G to 4G at no incremental charge (at the time; the $15/month adder was tacked on later as multiple $5/month increments). But the company declined to further upgrade it to 5G (not just gratis but at all); the “5GE” icon on my phone was marketing fluff, instead reflecting 4G LTE Advanced service. And since my plan didn’t support hotspot capabilities and the data allotment was therefore restricted to use solely by the phone itself, I was woefully undershooting its “unlimited” usage potential each month, anyway.

Instead, this time I went with Google Fi Wireless’s “Unlimited Premium” plan. Twice as much high-speed data included in the base price per month, twice as much of that also shareable with other devices via integrated hotspot support. Now’s as good a time as any to describe what Google Fi Wireless means by “unlimited”, i.e., what happens after you hit a plan’s per-month included-data threshold (save for the “Flexible” plan, where you pay $10/GB from the get-go):

• The data actually keeps flowing at no extra charge, albeit at a substantially “throttled” rate: 256 Kbps downstream.
• If you want more high-speed data within that month, you can incrementally purchase it at the $10/GB rate shown in the earlier bullet lists.

Data details

Speaking of data, the “Unlimited Premium” plan also includes up to four data-only SIMs at no extra charge, usable in cellular-cognizant devices such as my Surface Pro X and Surface Pro 7+/8 laptops, as well as my various iPad tablets (one’s in my 11” iPad Pro now, in fact) along with any dedicated cellular hotspot devices. Their data usage, in addition to that of my smartphone (both natively and via hotspot) and watch cumulatively goes against the plan’s per-month usage limit.

And speaking of dedicated cellular hotspot devices, the high-end NETGEAR Nighthawk M6 MR6110 I talked about back in mid-March:

is carrier-locked to AT&T. And although the mid-range Franklin A50 also covered there:

initially seemed to be third-party-unlockable, Unlocklocks wasn’t able to get me an unlock code after all (although to its credit, the company quickly refunded me in full after I submitted an order with the device IMEI). The low-end (LTE-only) Franklin T9, on the other hand:

is natively a T-Mobile-centric device. And since Google Fi Wireless runs on T-Mobile’s network, I was able to get up and running straightaway after ordering, receiving and activating a data SIM. For 5G purposes, I sprung for two more used hotspot devices (this time T-Mobile supportive), in both cases bought off eBay and manufactured by Inseego: the high-end MiFi X Pro 5G M3000:

and mainstream M2000 5G MiFi:

Pinching pennies

How much did this all cost? That’s perhaps the best part of all. For one thing, I’m getting $10/month off the normal $65 monthly service rate for the first two years. And I’m also getting a free Pixel 10 (the one below is “Indigo”, mine’s “Obsidian”):

a smartphone I’d initially covered during last August’s intro and I’ll write more about next week. Normally $799 for my 128 GByte variant, Google did an upfront-acquisition discount to $499. That’s a notable markdown as-is, although given that the Pixel 11 family is seemingly enroute, I’ve already seen discounts (although not to this degree) elsewhere already, too. The remaining to-free discount amount takes the form of monthly credits against the service cost, again spread out over 24 months. My Pixel 7 phones are scheduled to fall off the supported device list next October (a two-year extension to the original expiration, mind you), so I’d already been looking for a replacement anyway. I already have a Pixel 9a, which I’d traded in my earlier Pixel 6a to acquire, queued up as the eventual replacement for the “work” Pixel 7; I’ll keep both of them as spares.

There’s also one other monthly expense reduction that I’m considering as well. Conceptually, at least, the data service delivered by the Google Fi Wireless “Unlimited Premium” plan’s data-only SIMs is functionally redundant with the AT&T 5G data service that I still have active. Granted, I’m getting a $20/month discount from AT&T off the normal $55 plan rate. And the fact that just a few months ago, I spent a few hundred dollars on AT&T-only hotspots and accessories (extra batteries, cases, etc.) is also giving me pause. But the AT&T 5G plan, although remarkably fast especially on the external antennae-supportive high-end hotspot device, only provides 5 GBytes per month at the nominal fee rate. And a bit more than a dollar per day, $420/year said another way, is money that I could redirect elsewhere or “bank” for the future.

Not a shill

In closing, in case you were wondering, Google doesn’t have any idea that I’ve written this piece. I’m just a happy (so far, at least) customer who also finds the overall MVNO business model interesting. Analogies to the MVNO-and-MNO relationship that come to mind include:

• The semiconductor foundry model, although in this case, TSMC and its foundry-only kin aren’t simultaneously also acting as merchant chip suppliers, and
• Deep learning model developers who license them for use by other software-and-services providers, although in this case, open-source model alternatives also exist

Ironically, in looking back at the June 2010 post that had kicked off my move to AT&T as I wrapped up this writeup, I realized that I’d started it out as follows:

Ever experience one of those situations where, afterward, you wonder why it took you so long to tackle the undertaking? That’s what I’m feeling right about now.

With respect to my current situation, I couldn’t have said it any better myself. Wait…I did. Let me know your thoughts in the comments!

—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

Related Content

• De-commingling LAN equipment: It’s all in what you call it
• From T-Mobile To AT&T: It Couldn’t Have Been More Easy
• Verizon’s (and others’) 5G: underwhelming is putting it mildly
• Cellular hotspots: Multi-option evaluation thoughts
• If you made it through the schtick, Google’s latest products were pretty fantastic

The post Try Google Fi (Wireless)? The perks-for-the-price are why appeared first on EDN.

Футбольний турнір до 20-річчя Держспецзв'язку України Інформація КП чт, 06/18/2026 - 13:00

India’s leading UAV manufacturing startup, BonV Aero, is set to establish a 30-acre drone manufacturing campus in Khordha, Odisha. Envisioned as a first-of-its-kind concept in India, the facility will combine the latest UAV manufacturing with a curated visitor experience, offering firsthand engagement with the UAV ecosystem.

“The first time I saw a drone take flight, it left a lasting impression on me and shaped the path I chose,” said Satyabrata Satapathy, co-founder & CEO, BonV Aero. “Through BonV’s Drone Park, I hope to spark that same sense of awe and possibility in every visitor, especially young, aspiring minds who will go on to shape and secure India’s UAV future.”

That ecosystem is envisioned to take shape across the ₹300 crore facility. The campus will reimagine conventional manufacturing by bringing together advanced assembly lines and a Centre of Excellence alongside training rooms and certification halls open to pilots and technicians. At its core, the BonV Experience Centre will be designed to inspire and engage, offering something for everyone from knowledge seekers to thrill seekers. Thoughtfully designed visitor spaces, the campus will blur the lines between industry and experience, creating a destination that feels as much like a hub of innovation as it does a manufacturing facility.

Most UAV and defence manufacturing facilities in India keep visitors out. BonV Aero Drone Park does the opposite, opening its gates in a structured and guided manner to those seeking to understand how drones are designed, built, and flown all within a single integrated site, added Satapathy.

Moreover, the deep tech firm expects the facility to create over 1,000 jobs across manufacturing, research, and pilot training over the next two years, and sees the campus as a step toward making Odisha a hub for the wider drone economy, not just a production base. The launch comes as Indian states compete for investment in drone manufacturing, backed by federal production-linked incentives and a push for domestic UAV components. Odisha’s ambitious Aerospace and Defence Manufacturing Policy and the B-MAAN scheme for the aviation sector were cited as reasons for the company’s decision to set up in the state.

The post India to Get its First Public Drone Park in Odisha appeared first on ELE Times.

Всеукраїнський хакатон із комп'ютерного зору та робототехніки для школярів kpi чт, 06/18/2026 - 12:18

Every PCB designer has experienced it. A design progresses through schematic capture and layout only to reveal problems during verification, simulation, design review, or manufacturing preparation. A differential pair violates a critical constraint. A return path is compromised. A fabrication limitation was overlooked. A proven solution from a previous design was recreated rather than reused.

The result is familiar: additional iterations, schedule delays, increased costs, and engineering resources consumed by preventable rework.

For decades, many organizations have accepted this cycle as a normal part of PCB development. As design complexity continues to increase, however, this approach is becoming increasingly difficult to sustain. High-speed interfaces, power integrity requirements, signal integrity challenges, miniaturization, manufacturability demands, and compressed development schedules are all converging simultaneously. The traditional response—adding more reviews, more manual checks, and more engineering effort—does not scale.

In today’s design environment, productivity can no longer be measured by the amount of effort expended. It must be measured by how effectively engineering knowledge is captured, applied, reused, and enforced throughout the design process. This is where automation and abstraction are fundamentally changing how successful engineering organizations approach PCB design.

Rethinking productivity in PCB design

Historically, productivity improvements were often achieved by increasing engineering resources or extending design schedules. While those approaches may provide temporary relief, they do little to address the root causes of inefficiency. The reality is that many PCB development processes remain heavily dependent on manual intervention.

As design complexity increases, these manual approaches create significant risk. Constraints are often defined inconsistently. Verification occurs after implementation. Design knowledge resides primarily with individual engineers. Reuse is informal and dependent upon who remembers what was done on a previous project. The challenge is not a lack of engineering talent. The challenge is that manual processes struggle to keep pace with the increasing demands placed on modern electronic systems.

True productivity improvements come not from performing more work, but from eliminating unnecessary work altogether. More importantly, they come from preventing problems before they occur.

Automation: Enforcing design intent in real time

Automation represents a shift from manual execution to intelligent process control. Automation-assisted PCB design environments provide the ability to define electrical, physical, manufacturing, and reliability requirements as constraints that are continuously enforced throughout implementation.

Rather than relying on engineers to manually identify violations after routing is complete, constraint-driven design environments can evaluate compliance in real time. This enables:

• Continuous enforcement of electrical and physical design rules
• Real-time verification during placement and routing
• Guided routing aligned with signal and power integrity requirements
• Automated validation of manufacturing constraints
• Automated generation of manufacturing deliverables

The significance of this shift extends beyond simple efficiency gains. When design rules are evaluated continuously throughout implementation, engineers spend less time identifying problems and more time solving higher-value design challenges. Design intent becomes embedded within the process itself rather than residing solely in engineering documentation or individual expertise.

The result is improved design quality, reduced rework, greater predictability, and shorter development cycles. Simply put, designs become correct by construction rather than corrected after construction.

Engineering knowledge should not leave with the engineer

One of the most significant challenges facing engineering organizations today is the management of institutional knowledge. Many companies still depend heavily on the experience of senior engineers to ensure successful implementation of complex designs. While expertise remains invaluable, this approach creates an inherent scalability problem.

When critical knowledge exists primarily in the minds of individual contributors, organizations become vulnerable to personnel changes, inconsistent execution, and repeated mistakes. The departure of a key engineer should not result in the loss of years of accumulated design intelligence. Automation provides a mechanism for capturing and institutionalizing engineering knowledge.

Constraints, routing strategies, manufacturing requirements, design guidelines, and verification methodologies can be embedded directly within the design environment. Rather than relying on tribal knowledge, organizations can create repeatable engineering processes that consistently produce successful outcomes. The objective is not to replace engineering expertise. The objective is to amplify it and make it scalable across teams, programs, and future generations of designers.

Abstraction: Simplifying complexity through reuse

As systems become more sophisticated, managing every design detail at the individual net level becomes increasingly inefficient. This is where abstraction becomes a powerful productivity enabler. Abstraction allows engineers to work at higher levels of design intent by encapsulating proven solutions into reusable building blocks.

Examples include:

• Reusable hierarchical design blocks
• Standardized constraint templates
• Proven interface implementations
• Reference architectures
• Verified subsystem designs
• Reusable placement and routing methodologies

Design reuse is often misunderstood as simply copying circuitry from a previous project. Effective reuse goes much further. It involves capturing validated circuitry, proven constraints, routing topologies, placement strategies, manufacturing knowledge, and verification data so that future projects can build upon prior success rather than recreating it from scratch.

The difference is significant. Instead of repeatedly solving the same problems, engineering teams can focus their efforts on innovation and differentiation. This transforms design knowledge from a project-specific asset into an organizational asset.

From design automation to intent-driven design

Individually, automation and abstraction provide substantial benefits. Together they enable a more profound transformation: intent-driven design.

In an intent-driven workflow, engineers focus on defining system objectives, performance requirements, and design constraints. The design environment then continuously enforces those requirements throughout implementation. This reduces reliance on manual interpretation while improving consistency across teams and projects.

Intent-driven methodologies help ensure that:

• Design requirements remain aligned throughout implementation
• Constraints are applied consistently
• Reuse strategies are standardized
• Verification becomes continuous rather than sequential
• Manufacturing considerations are addressed earlier in the process

The result is a more predictable design flow that reduces ambiguity and improves overall engineering effectiveness.

Overcoming the adoption barrier

Despite the benefits, many organizations hesitate to adopt advanced automation and abstraction methodologies. The most common concern is the upfront investment required to define constraints, establish reusable design frameworks, and standardize engineering processes. From the perspective of an individual project, these activities can appear to add time. From the perspective of the organization, however, they represent investments in long-term scalability.

Every reusable design block created today can eliminate future engineering effort. Every validated constraint template can prevent future design errors. Every automated verification process can reduce future iterations. Over time, these benefits multiply.

Organizations that continue relying primarily on manual processes often find themselves trapped in a cycle where increasing complexity demands increasing effort. Organizations that invest in automation and abstraction create systems that scale with complexity, rather than being overwhelmed by it.

Connecting design intent across the product lifecycle

The value of automation and abstraction extends beyond PCB layout. Today’s products are increasingly developed within digital engineering ecosystems where requirements, simulation, design, manufacturing, and test activities must remain connected.

Traditional workflows often rely on disconnected tools and fragmented data sources. This creates opportunities for miscommunication, inconsistent implementation, and costly delays. On the other hand, a connected digital thread helps maintain continuity of design intent throughout the product lifecycle by linking:

• System requirements
• Architecture development
• PCB design and layout
• Simulation and verification
• Manufacturing preparation
• Test and validation

This continuity improves traceability, reduces information loss, and supports a model-based engineering approach where decisions are informed by connected data rather than isolated activities. As organizations continue advancing toward digital engineering and digital twin methodologies, the ability to maintain and leverage design intelligence throughout the lifecycle will become increasingly important.

Capture, reuse, and apply design intelligence

The future of PCB design will not be defined by how many hours engineers spend pushing traces or performing repetitive verification tasks. It will be defined by how effectively organizations capture, reuse, and apply engineering intelligence throughout the design process.

Automation and abstraction are not about replacing engineering expertise. They are about amplifying it. When constraints are defined once and enforced consistently, when proven design knowledge can be reused across programs, and when design intent remains connected throughout the product lifecycle, engineering teams gain something far more valuable than incremental productivity improvements: they establish predictability.

The organizations that embrace this shift will be better positioned to manage increasing design complexity, accelerate development cycles, and deliver higher-quality products with greater confidence. In an industry where complexity continues to grow faster than available engineering resources, success will increasingly belong to those who can transform engineering knowledge into scalable engineering intelligence.

Stephen V. Chavez is a principal printed circuit engineer with over three decades of experience. He is acknowledged globally as an industry Subject Matter Expert (SME) in PCB design. He is also an author, blogger, podcast host and is currently a principal technical product marketing manager with Siemens EDA.

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• EDA AI Agents: Intelligent Automation in Semiconductor and PCB Design

The post How automation and abstraction are transforming PCB design appeared first on EDN.

AXT Inc of Fremont, CA, USA — which makes gallium arsenide (GaAs), indium phosphide (InP) and germanium (Ge) substrates and raw materials at plants in China — says that its subsidiary Beijing Tongmei Xtal Technology Co Ltd has entered into a long-term supply agreement to reserve production capacity and raw-material allocation for, and grant supply priority to, Nanjing Casela Technologies Corporation Ltd...

Про підсумки конкурсу "Екоінноватор" Інформація КП чт, 06/18/2026 - 11:04

Elethron Ltd of London, UK and ATMOS Space Cargo GmbH of Lichtenau, Baden-Württemberg, Germany have concluded a joint engineering campaign to map and bridge the critical interfaces and operational architecture required to integrate Elethron’s microgravity materials processing lab into ATMOS’ PHOENIX free-flying reusable orbital transfer and return vehicle (OTRV), an uncrewed transportation system for controlled cargo transport, operations and return from low Earth orbit (LEO)...

Keysight Technologies, Inc. joins the Siemens Digital Industries Software Technology Partner Program. The collaboration gives customers access to Keysight Eggplant Test, an AI-driven test automation solution, to validate their digital engineering and product lifecycle management (PLM) environments.

Manufacturers face growing pressure to shorten development cycles while managing increasingly complex software-driven products. As engineering teams rely on digital tools like PLM platforms, testing those workflows, integrations, and system performance has become a significant operational challenge, with manual processes too slow and inconsistent to address at scale.

Siemens Digital Industries Software develops solutions for engineering, manufacturing, and product lifecycle management. Through the partnership, customers using the Teamcenter software can deploy Keysight Eggplant Test, an AI-driven test design and generation solution, to validate their enterprise applications and engineering workflows before they reach production.

Gareth Smith, Software Quality Engineering General Manager at Keysight, said: “As PLM environments grow in complexity, organizations need a reliable, AI-driven way to validate software before it reaches production. By joining the Siemens Digital Industries Software Solution Partner Program, engineers can use Keysight Eggplant Test to reduce the risk of undetected issues when upgrades or integrations are released, maintaining system performance and reliability at every stage of the product lifecycle.”

Resources

• White Paper: The Business Case for PLM Test Automation
• Survey: Complexity Overload and Bottleneck Struggles: The Hidden Costs of Manual PLM Testing
• Data Sheet: Testing the Product Lifecycle Management (PLM) Process with Keysight Eggplant Test

The post Keysight and Siemens Collaborate on AI-Driven Test Automation appeared first on ELE Times.

Aehr Test Systems of Fremont, CA, USA — which provides solutions for testing, burning-in and stabilizing semiconductor devices in wafer-level, singulated die, and packaged-part form — has received a follow-on production order for a fully automated FOX-XP wafer-level burn-in (WLBI) system, for delivery within the next six months. The system is configured to test nine wafers in parallel, and includes a fully integrated WaferPak Auto Aligner, and a full set of FOX WaferPak Contactors...

I have some nimo tubes, so i'm just showcasing them here. submitted by /u/EmptyWater7775 [link] [comments]

About a month ago I posted my credit-card sized computer project here and was honestly overwhelmed by the response - and thanks for all the encouraging feedback, that really helped a lot! One thing that came up repeatedly was people asking how it was actually built, so here I have some more details on the actual process. It's actually a bit of a hassle to take photos while working with dangerous chemicals, but it was worth it for sure! Honestly, my first thought after seeng this first picture was like "dang, this is nowhere as clean as I thought..." to the naked eye, everything looks precise and flawless, until you take photos with macro lens mounted on a mirrorless camera. But honestly, this kind of is satisfying too: Not only you can see all the impurities, but also every single overflow of solder paste, which doesn't even look like paste anymore as you can see the microscopic solder balls swimming in flux. Some areas needed some manual rework with additional solder paste, and the bridge over there was a result of my single layer limitation for now. And yes, I see it's almost shorting with another net but it luckily turned out fine. And regarding the actual etching process, that was described in my GitHub repo, but it was basically the normal method of etching PCBs with the difference of using copper foil with kapton tape as substrate. Curing the photoresist layer, developing it with a 5% sodium carbonate solution, etching it with ferric chloride, and lastly stripping the remaining photoresist with a 2% sodium hydroxide solution. Optionally solder mask if needed, but I skipped that step with this one. It's somewhat workable to get fast iterations but has the drawbacks of being extremely fragile. On some photos you can see how uneven the PCB is even though I taped it stretched onto a flat, rigid surface. Note that the pictures of each step is made on different runs, so you might spot some differences as result of trying different techniques. I already ordered a proper PCB from a fab, once that arrives, the Muxcard will be actually durable enough to be used as a daily driver. And for those who asked: Yes, I do plan to launch this soon. And if you're interested, you can find more details on the GitHub page :) But this post is more about these cool pictures I wanted to share here first, I'll add them into the repo as well as reddit doesn't seem to support including pictures in the text body. If there's anything you're curious about, feel free to ask - I'll try my best to answer every comment! :) submitted by /u/krauseler [link] [comments]

Allegro DVT’s Pulsar D400 series of multi-format video decoder IP now supports real-time AV2 decoding for advanced SoCs and ASICs. AV2, developed by the Alliance for Open Media, is an open, royalty-free video compression specification designed for next-generation streaming applications. As the successor to AV1, it improves compression efficiency, delivering high-quality video at significantly lower bitrates.

With AV2 capability, the Pulsar D400 series enables streaming applications up to 8K resolution with ultra-low-latency decoding (down to the sub-frame). Its multi-codec architecture supports H.264, HEVC, VVC, VP9, and AV1, while reducing silicon footprint, DDR memory bandwidth requirements, and power consumption.

 Allegro DVT also provides AV2 development and validation tools, including the Sirius AV2 Test Suites and Astralis AV2 Bitstream Analyzer, along with silicon-proven IP and compliance expertise.

Pulsar D400 product page 

Allegro DVT 

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EPC’s EPC99132 evaluation board is a GaN-based three-phase inverter for small BLDC motor drives in drones and robotic wrists. The design is built around the EPC33110, a 100-V, 20-A three-phase ePower Stage module that integrates three half bridges (six eGaN FETs), gate drivers, level shifters, and bootstrap circuitry in a 6×6.5-mm QFN package.

The EPC33110 co-packaged module requires a 5-V supply and supports 3.3-V or 5-V logic inputs. Its integrated eGaN FETs feature typical on-resistance values of 11.7 mΩ (high-side) and 13 mΩ (low-side). Performance testing demonstrated continuous current delivery of 11 ARMS per phase in a 48-V robotic joint at switching frequencies up to 100 kHz.

The EPC91132 evaluation board operates from a 10-V to 60-V DC input and integrates an MCU, regulated power supplies, DC bus voltage sensing, and current sensing. It also includes an onboard magnetic encoder for rotor position and speed control. The inverter is 23 mm in diameter, making it suitable for small drone motors.

The EPC91132 is priced at $406.25. Design support materials, including schematics, bill of materials, and Gerber files, are available for download on the product page.

EPC99132 product page

Efficient Power Conversion 

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GigaDevice offers the GD32E512 and GD32E252 MCUs purpose-built for high-speed and low-speed optical modules, respectively. The devices target applications in AI data centers, cloud infrastructure, telecommunications networks, and access networks.

The GD32E512 features an Arm Cortex-M33 core operating at 120 MHz and integrates I3C support for high-bandwidth, low-latency, high-density communications in next-generation optical modules. Its peripheral set includes two 12-bit ADCs, up to eight 12-bit DACs, two comparators, two op amps, three I²C interfaces, and one MDIO interface, enabling monitoring, control, and management functions in a compact 3×3-mm chip-scale package.

Powered by an Arm Cortex-M23 core operating at 72 MHz, the GD32E252 delivers a balance of performance, integration, and efficiency for cost-sensitive and lower-speed optical connectivity applications. The MCU integrates one 12-bit ADC, four 12-bit DACs, one comparator, one I²S interface, and three I²C interfaces in a choice of QFN package options. 

GD32E512 product page 

GD32E252 product page

GigaDevice

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A smart load switch from Diodes features low on-resistance for reliable power sequencing and rail control in automotive applications. Designated the DML1012ALDSQ, it integrates an N-channel MOSFET with 8-mΩ RDS(ON), minimizing conduction losses and reducing heat generation. The single-channel device is well suited for ADAS, infotainment platforms, and display clusters.

The switch supports a 0.8-V to 1.5×VBIAS input range and operates from a 3.2-V to 5.5-V bias supply, allowing flexibility across subsystem power rail domains. A junction-to-case thermal resistance of 8°C/W enables up to 6 A of continuous output current under appropriate thermal conditions, while low 28-µA quiescent current from VBIAS improves efficiency during power gating and reduces standby power consumption. Together, these features deliver precise system-level power sequencing.

For automotive power management applications, the DML1012ALDSQ integrates controlled output voltage slew rate, quick output discharge, and undervoltage lockout (UVLO) protection features. Controlled slew rate minimizes inrush current during startup, while quick output discharge fully discharges downstream components during shutdown. UVLO disables operation when the supply voltage falls below a safe threshold, helping ensure predictable system behavior.

Prices for the DML1012ALDSQ start at $0.17 each in 1000-piece quantities.

DML1012ALDSQ product page

Diodes Inc.

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TI’s BQ79826Z-Q1 automotive battery monitor supports up to 26 cells in series and is stackable to 128 devices. Its integrated electrochemical impedance spectroscopy (EIS) engine detects early signs of thermal runaway inside battery cells, helping improve safety and performance in EVs and energy storage systems.

The BQ79826Z-Q1 combines real-time diagnostics with predictive battery monitoring to help extend battery life. According to TI, the 26-channel chip delivers the highest cell-count monitoring in its class, tracking up to 44% more channels than previous generations. The higher channel count can reduce the number of monitoring devices and associated components required in a battery pack.

With voltage accuracy of less than 2 mV across the full -40°C to +125°C temperature range and a dedicated ADC for each channel, the BQ79826Z-Q1 enables more accurate state-of-charge and state-of-health estimation. These measurements can improve EV range prediction while supporting battery performance and longevity. EIS measurements are up to five times faster than those of previous devices, enabling more frequent battery diagnostics and earlier detection of cell degradation.

Preproduction quantities of the BQ79826Z-Q1 are available on TI.com, with production expected by the end of 2026.

BQ79826Z-Q1 product page

Texas Instruments 

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КПІ першим серед університетів України приєднався до BankID НБУ на некомерційних умовах kpi ср, 06/17/2026 - 18:56

Інновації КПІ ім. Ігоря Сікорського на INSCIENCE Conference KPI4U-2 ср, 06/17/2026 - 17:58