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More Medical Tech –Smart Bandages for Wound Management

More Medical Tech –Smart Bandages for Wound Management
by Bernard Murphy on 01-06-2016 at 12:00 pm
Categories: IoT

I have a bias (as you may have noticed) for solutions in a domain that take advantage of technology but are developed within that domain. A recently example is intelligent bandages, developed at Massachusetts General Hospital, Harvard, Purdue and several other research centers. The purpose of such a bandage is to monitor a wound as it heals and in some cases to improve healing. (I should add there are lots of applications for smart bandages in monitoring other aspects of health. Here I’m only touching on wound care and prevention.)

Monitoring can take many forms – one application is simply to determine levels of oxygenation around the wound site. Healing requires large levels of oxygen; any shortfall will slow healing or lead to tissue death. That creates two interesting constraints for a bandage; it must be flexible and it must be permeable to oxygen (just like a Band-Aid™). A method to display oxygen concentration in wounds using phosphorescent bandages has already been demonstrated; presumably this could easily be adapted to deliver digital information. An even more interesting prototype not only detects low levels of oxygen but also uses this to trigger a chemical reaction in the bandage which then generates oxygen – a beneficial chemical feedback loop.

Some of these prototypes are currently half an inch thick and six to eight inches long – not quite the thin bandages we think of today, but no doubt this will improve with time. The intelligent part (processing and communication) is, area-wise, actually a very small part of the bandage; the bulk is sensing and chemistry. The chemistry can be further extended – some researchers have shown the ability to deliver pain and anti-bacterial medications directly from the bandage, also though a feedback loop or through wireless control.

A very interesting application is to minimize scarring as the tissue heals. Scars have psychological impact but can also be physically-limiting when new skin stretches too tight around the eyes, mouth and other areas. Intelligent bandages can help by controlling growth through a matrix which guides that growth. Current research uses nano-fibers and similar materials to prompt this growth. There is also a protein called fibronectin which can accelerate growth; initial experiments based on embedding this in the bandage are promising and appear to encourage growth of soft skin (unlike typically taut scar tissue). I would imagine (without proof) that selective electrical stimulation may also be helpful in guiding skin growth.

Another quite different application is to detect early development of bedsores. These start under the skin as a result of prolonged pressure on one area, limiting blood supply to that area. A challenge for doctors is that bedsores cannot be detected until they reach the surface of the skin, by which time it is usually too late to take corrective action. A Berkeley group detects potential problems using impedance measurements through the skin and into underlying tissue, driven by electrodes printed onto a flexible patch. The current technology seems to be just the electrode array but you could easily imagine automation being added.

Most of this work is very much still in the research stage. Perhaps the biggest part of the problem is the complexity of wound biology and the many different factors that must be considered, monitored and addressed: bacterial infections, immune response, oxygenation and skin re-growth. There are plenty of challenges and opportunities for tech in this area that go well beyond the health monitors we think of today.

You can read more about oxygen detection in wounds HERE, intelligent bandages HERE (ACM paper – may require membership or purchase) and HERE. There’s a good video from Science Nation on the topic HERE. The Berkeley application for bedsores is HERE.

More articles by Bernard…

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EDA and the Big Short!

EDA and the Big Short!
by Daniel Nenni on 01-05-2016 at 8:00 pm
Categories: EDA

A funny thing happened while I was reading “The Big Short: Inside the Doomsday Machine”. The book explains the subprime mortgage crisis in painful detail by profiling several key players who predicted and profited from the bubble pop. As a home owner and faux slumlord I had a front row seat to this horror show so it was an interesting read, but I can’t imagine paying to see the movie.

During the introduction of one of the characters it listed an investment in “The alarmingly named Avant!” as one of his big “ick investment” wins. An ick investment is a stock that inspires a first reaction of ick! As a longtime semiconductor professional and former Avant! employee I found this part laugh out loud funny. This guy bought Avant! stock at $12 and watched it go down to $2 before the Synopsys buyout at $22. The best passage is: “Avant! still makes me feel I’m sleeping with the village slut. No matter how my needs are met I doubt I will ever brag about it”.

Here is a little known fact about the Avant! acquisition: Gerry Hsu had a handshake agreement to be acquired by Mentor for significantly less than the $830M Synopsys paid. But, Gerry being Gerry, he then went to Synopsys and not only got a higher price, he got a bigger exit package. Just think what EDA would look like today if Gerry honored his handshake and Mentor acquired Avant! instead of Synopsys?!?!?!?

Speaking of EDA, 2016 is shaping up to be a very interesting year. We are already seeing an EDA traffic surge on SemiWiki and I think it will continue. I also fielded a couple of Wall Street calls on EDA at the end of the year so let’s talk about that.

Yes, I believe semiconductor consolidation will continue in 2016 and no, I don’t think EDA will take a big hit as a result. I definitely see deals being pushed out during the acquisition period and there may be less seats to fill as a result of “expense optimization” but I do not see rampant discounting and those empty seats will be quickly filled by the system companies and the next wave of IoT design starts. Remember, the systems companies (Apple, Samsung, Amazon, Google, etc…) can write MUCH bigger checks to EDA companies than fabless semiconductor companies, just ask TSMC.

I do see however, an EDA landscape change evolving. Among the big three I see Cadence as the best situated for market share growth via the Synopsys IC Compilor monopoly. I have also heard VERY good things about the new Cadence emulator from Cupertino and San Diego. The Cadence AMS and the Mentor Calibre monopolies however seem safe for now. Maybe #53DAC will bring technological disruption in those areas but probably not.

The dark horse here of course is ANSYS if they acquire Mentor for example. That would certainly shake things up a bit. Not only would that take Mentor into a whole new level of exposure outside traditional EDA, it would get ANSYS securely inside the semiconductor ecosystem and give Synopsys and Cadence cause for concern, absolutely. In regards to the other small to medium EDA companies I see more squeezing by the big three, continued consolidation, and fewer emerging companies.

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Do You Need a 3D Printer Yet?

Do You Need a 3D Printer Yet?
by Tom Simon on 01-05-2016 at 4:00 pm
Categories: General

There is no question: you will own a 3D printer – it’s only a matter of time. The situation today is like it was with the early personal computers, at first it was the hobbyists who had them and most other people wondered what they would use one for. But over time their usefulness became obvious and the difficulty of acquiring, using and supporting them diminished by leaps and bounds. So, with 3D printers where do we stand today?

I’ll confess that I’m on the vanguard, but it’s pretty clear where things are headed. Today’s 3D printers, and by that I mean the whole ecosystem supporting them, has moved past the really early “you have to build and understand everything yourself” days. Think back to when owning a computer meant you had a build it yourself. 3D printers have moved beyond this stage.

Unlike the early days, now Amazon lists many 3D printers you can buy assembled and ready to use. Of course you can still buy a kit. Though ready made printers are essential for mass adoption. Still, most all of the assembled printers require the kind of fussing that a consumer would not bother with. The prices for assembled printers start at ~$300 and go up from there. There are a few for that kind of low price that are useful and probably even worth it. But to avoid headaches it is advisable to spend over $1,000.

Virtually no printer that says it is a consumer product is really telling the truth. Because they are mechanical beasts, luck also plays a role in user experience. Some cheap printers work well for some users, and not well for other users. Higher prices mean better quality design and, importantly, better materials and build quality.

The number one issue that needs to be ‘solved’ before 3D printers are something that you pick up at Best Buy and just plug in is bed leveling. Most printers print things using a layer thicknesses of 0.1 to 0.3mm. That is 100 to 300 microns for us chip guys. The very first layer that goes down on the print bed is make or break for everything that comes after. If the print head, which is usually extruding a 0.4mm strand of molten plastic, is too high the plastic does not adhere and simply wads up into a sticky ball at the end of the extruder nozzle. This then gums up everything that is done after. If the head is too low, then the print head is jammed against the bed and the filament can’t some out. This can lead to melted plastic gumming up the heater and extruder internals. Most often though it will temporarily block the nozzle, but regardless the print will not begin properly.

Printers do all sorts of things to improve results when they print the first layer, such as using a heated bed to improve adhesion, running slowly to make sure the first layer sticks well, and extruding a bit more material to make sure the first layer mushrooms out a bit to increase surface area and pressure against the bed. All of these operations are controlled by what is called slicer software. This software reads .STL files and slices up the shape to determine the tool paths for each of the hundreds or thousands of 0.2mm layers needed to complete a print.

Bed leveling needs to be done so every point on the bed is the right distance from the extruder tip, usually 0.1mm, at the start of the print job. Today this process is most often done with checking to see if a sheet of paper will “just” slide under the tip, not too easily and not too stiffly. The thing is, this takes several rounds of moving the print head to the corners and adjusting wing nuts. The print head sometimes can have gobs of hardened plastic on it, which will throw off the process. The whole operation will need to be repeated at varying intervals to keep things working.

Some expensive printers, and at least one cheap one, boasts auto-leveling. But this should be classified today as an unsolved problem for consumer level devices.

At the start of this article I posited that there is a 3D printer in your future. Despite the truth of the saying that a 3D printer is not a tool, it is a hobby, they have immense usefulness. After having a printer at home for many months, I decided that I needed one at my vacation home for over the holidays. I had become used to the notion of thinking of something useful and then having the ability to find or design it, and hold it in my hand shortly thereafter. Online there is a vast collection of free things that can be printed easily. If you have never looked at Thingiverse.com, I suggest you do. Thousands of people have submitted their designs for everything imaginable, so that you or I can simply download them for free and print them.

It is true that the first thing you print with a 3D printer is new or improved parts for it. However, there are many other applications. When it hits you that you can get exactly what you need with out going to the store – it is a revelation. Or even more importantly, you can get something you could never find in any store. Here are few of the things I printed in the last few weeks, while out of town.

I designed and printed this phone stand based on the Linkmount system.
If you own a Go Pro you know how hard it is to open the enclosure. This was downloaded from Thingiverse.com after a quick search and was easily printed at home.
When I am waxing my skis the anti-skid arms are always in the way, so I designed this custom bracket to hold the arms down so I can wax the board without bumping into them. See the ski behind to see how the arms usually stick up.

I own three printers. The first was the Micro3D – it was a Kickstarter that cost around $300. I bought it without knowing a lot about 3D printing. While I can say it works, it has many faults. But, remember it is just $300. It is a closed design that made many trade-offs to hold down costs. Most higher quality 3D printers use stepper motors – NEMA being the favored brand. The Micro3D uses low cost motors with a small gear to lower the shaft rotation rate. They also built their own print head. There are lots of print heads on other printers that have evolved into open standards – not unlike open source software. These have matured to work really well and replacement parts, upgrades, and problem solutions are readily available. With the Micro3D you have to live with their design, as is. There are other cost cutting measures in their design like the way the heater coil is used as the thermal sensor – requiring factory recalibration of the firmware when it needs to be replaced. Lastly it compensates for a range of design issues by running at a snail’s pace, making it frustrating when you build larger things.

My next printer is the FlashForge Creator Pro. Compared to the Micro3D, it is a serious piece of hardware. It performs really well and has required very little fussing. It is based on the open source design of the Replicator Makerbot, which means that advice, parts and upgrades are readily available. In fact, the FlashForge already comes with a number of community enhancements to the original design. It costs around $1200 on Amazon and prints at a much higher quality, size, speed and reliability than the Micro3D. Of course there is still some fussing with bed leveling and a few things you’ll want to do to make it work better. But it was up and running very quickly.

For my vacation home I went back to the low-end price range, but this time bought another printer based on an open source design. I bought the Wanhao i3, based on the Prusa i3. Actually I bought a re-labled version from Monoprice.com – called the Maker Select. It took a number of upgrades – most of them downloaded from Thingiverse.com – to get the best results. Also there are a few somewhat tricky things about keeping it calibrated. But there is an active Google group where advice can be gathered. When it is calibrated it prints as well as the FlashForge. For $350 with free shipping I cannot complain.

There is one model by Zortrax, the M200, that claims the highest ease of use and the least hassles for people who not not want a 3D printer hobby. At $1900 it is not the cheapest, but also by no means the most expensive. It requires their proprietary software and filaments. It also has a slightly cumbersome procedure for running prints. However, it is apparently the best no hassle printer available at the consumer level. Remember when the Apple II came out and was only available as a finished product and all the tinkerers said it was “too expensive” and not easily upgradable? Well, stay tuned to this one.

Here is a more ambitious project for a removable camera holder for a ski helmet.

I’d have to say that I “need” a 3D printer. Yes, I am an early adopter and I really cannot make a financial justification for owning one, let alone three. But just like the personal computer, prices will come down and the technology will improve dramatically. I cannot say if it will be in 5 years or 10 years, but your 3D printer will be there sitting on your counter next to the tool box (or coffee maker) sooner than you might think.

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IEDM Blogs – Part 6 – IMEC Technology Forum – Part 1

IEDM Blogs – Part 6 – IMEC Technology Forum – Part 1
by Scotten Jones on 01-05-2016 at 10:00 am
Categories: Foundries

On Sunday evening December 6[SUP]th[/SUP] before IEDM, IMEC held the IMEC Technology Forum (ITF). The ITF was held at the Belgium ambassador’s residence, a really beautiful setting for a meeting.

The ITF began with a brief welcome by the Belgium ambassador followed by a brief introduction to IMEC. IMEC is a research institute located in Belgium. IMEC was formed in the 1983/84 time frame and has an annual budget of approximately 400 million euro. The introductions were followed by the formal presentations.

The technical program of the meeting was really good, unfortunately IMEC will not let me share the presentations but hopefully I can do a good job of summarizing what was presented.

An Steegen
An is the Vice President of Process Technology at IMEC.

IEMC presented 23 papers at IEDM this year and everything is connected. There is a need for more bandwidth at lower energy.

Three keys are:
[LIST=1]

  • Dimensional – lithography
  • Device – novel devices and materials
  • System scaling – 3D and optical

    1) Dimensional – 0.7x scaling per generation. Argon Fluoride immersion lithography (ArFi) can achieve a pitch of approximately 80nm forcing multi-pattering for the latest generation processes. EUV can print 40nm or even 30nm with a single exposure. The 7nm logic node (N7) will have a 30-40nm pitch. For the 5nm logic node (N5) a 24nm pitch will be required driving the need for 1D patterns and EUV for cut/block (multi-patterning).

    2) Devices – Silicon FinFETs currently. Next is FinFETs with III/V channels and then vertically stacked horizontal nanowires.

    3) System scaling – 3D stacking provides smaller size, less power and more bandwidth. Longer term move to optical interconnect.

    IMEC provides infrastructure, people and partnerships. IMEC at a glance:

    • 200mm CMOS pilot line
    • 300mm CMOS pilot line
    • Expanding the cleanroon by 4,000m[SUP]2[/SUP]
    • Doing research on the 3nm logic generation (N3)
    • ~2,300 staff, ~1,500 on the IMEC payroll and ~800 from partners

    IMEC papers accepted at IEDM have been 18, 17, 17, 19, 16 and 23 for 2010, 20122, 2012, 2013, 2014 and 2015 respectively.

    Mark Rodder
    Mark Rodder from Samsung’s Advanced Logic labs went next and presented a whirlwind tour of challenges to continuing Moore’s law (he seriously challenged my ability to rapidly take notes with an incredible information dense presentation).

    In 1983 we thought 500nm was the economical limit. In 1986 the half-micron apocalypse paper was published. In 1989 the author of the 1986 paper said scaling will continue. The concerns were similar to today’s challenges, but there are more challenges today, Moore’s law is exponential.

    In a “standard” cell geometry contacted poly pitch (CPP) limits cell width, and back end of line (BEOL) pitch and routing limits the cell height. Cell height limits active space (effective channel width or effective channel width per fin). Cell area limits contact and via area and interconnect length.

    CPP scaling through higher mobility materials – unstrained germanium mobility is similar to strained silicon. Strained germanium has better mobility than strained silicon. III/V materials are similar. III/V materials are more susceptible to surface roughness scattering than silicon, germanium is less susceptible. CCP scaling with III/V is less than ideal due to scattering issues. Germanium can increase leakage due to band to band tunneling (BTBT). New structures can reduce leakage but need to fit.

    Parasitics can dominate performance – reduction of source/drain volume can increase contact resistance and the fundamental contact resistivity may be higher than expected. Further reductions in contact resistance may be limited but we aren’t at the fundamental limit yet.

    BEOL parasitics are more critical – via and line resistance are grand challenges for upcoming nodes. At this point a graph was shown that illustrated that for various copper interconnect line lengths the resistance with scattering is >2x the resistance without scattering. BEOL congestion is also a problem for cell scaling with 1[SUP]st[/SUP] and 2[SUP]nd[/SUP] order rules limiting scaling. New cell designs are needed.

    On die cache – additional cache memory is needed to address the gap between logic and memory performance. STTRAM can address the additional cache needs and is more compact than SRAM (1T – 1MTJ versus 6T). SSTRAM can be stacked in the BEOL versus front end of line (FEOL) SRAM. STTRAM is nonvolatile but the error rate needs to be addressed for fast on-die applications.

    All of the above should get us to 2025, but remember all the past predictions about Moore’s law.

    The net of this is:

    • Simple pitch shrinks are becoming more challenging.
    • Hetero-integration can be useful but requires the right materials and parasitic resistances.
    • BEOL has fundamental limits that need a breakthrough.
    • Extending Moore’s law for several more nodes may be difficult by any one of these technique – we may need breakthroughs for each new node.

    Moore opportunities:

    • System performance can be boosted by Moore’s law or Moore than Moore such as 3D stacking, dense/non-volatile memory, new circuit designs.
    • TFETs – a steep sub threshold slope is possible.
    • 2D materials – low temperature BEOL, band engineering, TFET use, etc., but requires materials and interface development.
    • Advanced interconnects such as optical.
    • New switches such as spintronics.
    • Dry brains (neuromorphic computing) or another efficient hardware implementation. Co-processors are much more efficient for certain tasks.

    The market opportunity is huge, only 3 billion people are connected, there are 4 billion people still to connect. Opportunities in connected cars (we spend >1 hours per day in them), wearables (smart watches, fitness bands, activity tracker, etc.) and smart devices (home energy, home security, appliances, etc.)

    One projection of IoT is for 20 billion connected devices by 2020.

    “Challenges & solutions notwithstanding, there are many opportunities for value from the ever increasing number of connected devices and diverse application space – Whether by Moore’s Law, or by More than Moore”

    In the next installment I will cover Aaron Thean and Malgorzata Jurczak’s presentations.

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    Preview of International CES 2016

    Preview of International CES 2016
    by Bill Jewell on 01-05-2016 at 4:00 am
    Categories: IoT

    Monday, January 4, 2016, Las Vegas, Nevada
    Today I attended CES Unveiled, which offers a sneak peak at some of the new products being introduced at International CES 2016. The Internet of Things (IoT) has been hyped as a key driver of electronics market growth over the next few years. There were plenty of examples at CES Unveiled. Numerous “smart” products were on display. Some of these products seemed questionable as to whether they were of any practical use. Others were innovative and solved real problems.


    The “smart” products included a toothbrush, shower head, tennis racket, shoe, remote control, steering wheel, piano, ceiling fan and smoke alarm battery. A Fridge Cam allowed you to use your smartphone remotely to see what was in your fridge so you could buy what was missing (unless your spouse moved stuff around). Two separate products promised to stimulate hair growth (below). Some of the booths looked like late night TV infomercials.


    The CES Innovation Awards included several products which were not only innovative but practical. Ricoh introduced a camera which captures 360 degree scenes.


    Deeper introduced a wireless fish finder which works with your smartphone. My brother-in-law would love this.


    HP introduced an all-in-one PC which features a curved, panoramic monitor. This could replace multiple monitors in many applications.


    Two products from related companies offer relief of pain and discomfort. Quell’s device for chronic pain claims 86% satisfaction from users. The device is worn on the thigh and works primarily for back, leg and feet pain.


    Reliefband is a device worn on the wrist to relieve nausea due to motion sickness, pregnancy, chemotherapy and other causes. The device emits gentle electrical pulses which work with the body’s nervous system to relieve nausea. Both of these products are consumer versions of proven medical technology.


    These are but a small sampling of the products which will be introduced at CES 2016. I will provide daily updates through Thursday, January 7 on my website at www.sc-iq.com

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    A System Spin on IoT Security

    A System Spin on IoT Security
    by Bernard Murphy on 01-04-2016 at 4:00 pm
    Categories: IoT, Security

    A lot of progress has been made in infrastructure to secure edge nodes in the IoT and to secure communications between edge nodes and gateways, all of which is good and necessary to block manifest evil, but it’s never enough. Perfect security is and always will be an asymptotic goal, so there should always be room for new ideas. To a large extent our approach to security looks a lot like what we already understand in the traditional Internet; self-defense within nodes plus firewalls in the infrastructure to limit the spread of contagion. One possible approach to augment this base-layer would consider security from a system perspective – how the system as a whole can defend its integrity, while acknowledging that some components may need to be sacrificed in defense of the greater good.

    In 2014 I wrote an article in another forum in which I discussed biology-inspired approaches to security. Some of the details may be a little dated but I think the principles are still relevant. The core idea is that the IoT, at least at the scale we eventually envision, is a very large system with a very large attack surface, not unlike biological systems. Therefore, biological defenses may be a productive source of inspiration for added defenses we might consider. Most of this is based on work done by others. What I added (I hope) was to collect together the ideas and view them in the context of the IoT.

    Let’s start with diversity. The engineer in each of us says that we should drive to a small number of system types with as much commonality as possible because standards encourage growth and innovation and reduce cost. But lack of diversity also carries risk – a pathogen exploiting a zero-day weakness may be able to spread quickly through the system before effective counter-measures can be found. A famous biological counterpart is the Irish potato famine in which almost all of the crop, based on a single strain, was wiped out. You might argue that a proliferation of vendors and applications will solve this problem, but I’m not so sure. Much of that diversity may be only skin-deep thanks to the dominance of a limited set of core architectures and OSes. And in time, as in most markets, the majority of product volume will be supplied by a couple of dominant players. All is not lost though. There are ways to add diversity even to common platforms, for example by randomizing stack layout.

    Another technique is to mimic immunological defenses. The basic idea here is to identify potential pathogens based on behavioral rather than structural signatures (the standard approach in computer virus defenses), since behavioral signature detection is potentially much more economical, especially in edge nodes, than structural-based approaches. The system must be trained to identify “self” or normal behavior from unexpected behavior, mimicking biological immunity inherited through evolutionary adaptation. Simple examples might be (self) allowable paths of IP addresses for communication or (non-self) detecting a fragment of the device encryption key in an I/O channel.

    In the example above, there is an important difference from traditional approaches to security. By the time a behavioral trigger is fired, an attack is possibly already underway or may have succeeded. At that point, the best goal may be to sacrifice the node. The parallel in biology is programmed cell death – or equivalently an IoT node shuts itself down (in critical cases an exception might be to fail-over to a default non-programmable behavior). The node also emits an alarm signaling surrounding nodes to disable communication with this node, allowing for the possibility that it may already be too compromised to effect a shut-down..

    The last method I mention builds on a common pathogenic strategy – deception – and is well-known today in IT security systems. Pathogens can evade immunological defense through deception by making their behavior appear like “self” behavior. A counter-deception strategy would be to present tempting but fake “honeypot” targets. These may be dummy DNS targets, empty file or directory links, or dummy accounts with temptingly easy passwords. Any attempt to access one of these triggers an alarm, which again may be used to trigger self-sacrifice. Finally you make these hard to evade by having many more honeypots than real targets.

    Hopefully you see in these ideas at least a different way to think about security and especially a way to think about whole-system defenses (top-down) as a complement to more traditional defenses (bottom-up). None of these is any more “invincible” than other approaches. But by further raising the bar, they should increase the cost and therefore decrease the likelihood of attack. You can read the complete article HERE.

    More articles by Bernard…

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    My Life at Fairchild – 1980-1983

    My Life at Fairchild – 1980-1983
    by Mark Rioux on 01-04-2016 at 12:00 pm
    Categories: Foundries

    After spending my first year learning a great deal about Diffusion and completing my orientation at Fairchild, I was moved to the 3″ Photolithography area as a sustaining engineer. As with the Diffusion area, being a sustaining engineer in Photo meant dispositioning lots on hold and making process improvements as needed.
    Continue reading “My Life at Fairchild – 1980-1983”

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    Inventor of Netscape Looks at IoT

    Inventor of Netscape Looks at IoT
    by Daniel Payne on 01-04-2016 at 7:00 am
    Categories: IoT

    1995 was the year that a co-worker walked into my cubicle and said, “Hey, you have to see this new web browser and Internet thing.” I promptly installed Mosaic, later renamed Netscape, and began surfing the web with all of those interesting hyperlinks bringing me to new articles. Marc Andreessen was the mastermind behind Mosaic and Netscape, so I give him a lot of credibility to spot new trends in our tech-dominated world.

    Looking beyond the common Smart Phone that we enjoy today, Andreessen sees a world of ambient or ubiquitous computing where, “You walk up to a wall, sit at a table and talk to an earpiece or eyeglasses to make a call.” That reminds me quite a bit of the weekly cartoon series, The Jetson’s, where in 1962 they portrayed a future of ubiquitous computing and automation.

    Marc is asking questions like, why have a smart phone at all?

    A California start-up called Samsarareceived a $25 million investment from Andreessen to help them automate industrial processes using IoT. They offer IoT Gateways, Environmental Monitors, Input Modules and Power Monitors. Industries like pharmaceuticals, transportation, power deliver and water are all using Samsara for sensors and data analytics in the cloud.


    Industrial or Vehicle IoT Gateways


    Wireless Monitor with temperature probe


    Self-powered wireless input module with 2X analog inputs, for 4-20mA current loop sensors

    Market research company Gardner is projecting that businesses will be spending some $1.4 trillion on IoT in 2016, growing to $3.0 trillion by 2020. IoT startup companies are raking in investments to the tune of $7.4 billion so far, which was a doubling in just the past five years.

    Manual measurements just cost too much money and waste manpower, so the industrial IoT is starting to really automate the processes in our hospitals, pharmaceutical supply chains, and warehouses. By installing inexpensive sensors, then uploading and analyzing the data in the cloud, the Samsara approach is about 1/10th the cost of other industrial approaches. An American yogurt company called Chobani, two big pharma companies and city water districts are all in trials with Samsara products. The water districts want to know how much energy they are using for their water pumps and to understand the usage patterns.

    Andreessen looks out over the next 20 years and seeing nearly every physical item having a computer chip or sensor embedded. I’m hoping that this product growth for industrial IoT applications will translate into a very healthy semiconductor market with opportunities for broad growth. It will be quite interesting to see if the hardware or software oriented companies will make more revenue for industrial IoT. Companies like Samsara are owning both the hardware and software, so should be in a good position to control the end product and help users gather and manage all of that analytics coming from embedded devices.


    Marc Andreessen, source:Reuters

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    Who Does Voice Recognition in the Samsung Gear S2?

    Who Does Voice Recognition in the Samsung Gear S2?
    by Eric Esteve on 01-03-2016 at 4:00 pm
    Categories: Ceva, IP

    If you have bought a Samsung Gear S2 smartwatch for Christmas, you certainly didn’t open it to do a teardown. Chipworks did it and have shared the results: Qualcomm is the big winner here with five different chips: Snapdragon 400 as the main CPU of the system, the RF transceiver, the audio codec, the power and the baseband processor (MSM8226). Not really a surprise as the Qualcomm wireless port-folio is certainly the strongest in the industry.

    Samsung has integrated three of its own chips, DRAM memory, NFC controller and Wi-Fi module, a DC-DC converter for AMOLED, accelerometer and gyroscope and barometer from STMicroelectronics and another DC-DC converter from TI. In fact, the real surprise comes from a chip from DSP Group HDClear family, especially dedicated to manage the audio command capabilities of Samsung Gear S2. At the heart of the DSPG HDClear is CEVA-TeakLite-III DSP core that DSPG has licensed a few years ago.

    HDClear DSP includes noise reduction algorithms and filtering ambient noise of any kind to deliver “cleaner” speech to ASR (Automatic Speech Recognition). According with DSP Group website, HDClear technology outperforms other available solutions, with Word Error Rate (WER) under 20% in any ambient noise environment.

    CEVA-TeakLite-III DSP core delivers the processing power providing the exceptional voice intelligibility of this DSPG HDClear chip while enabling extremely low power always-on capabilities, making HDClear is the lowest power chip of its kind in the industry, according with DSPG.

    Let’s zoom on CEVA-TeakLite-III, a true 32-bit DSP addressing high-end audio processing, voice processing and wireless baseband applications. TeakLite-III is based on a fully synthesizable dual-MAC native 32-bit DSP engine forming the basis of two implementations: the CEVA-TL3210 and CEVA-TL3211 DSP cores. The CEVA-TL3210 offers a wealth of high-end features including a configurable L1 program cache memory and support for industry-standard APB and AHB-Lite system busses. The CEVA-TL3211 offers configurable L1 program and data cache memories, support for high-speed AXI system busses, and an integrated Power Scaling Unit (PSU).

    Based on a native 32-bit architecture, the CEVA-TeakLite-lll can perform two 16×16-bit Multiply-Accumulate (MAC) operations or one 32×32-bit MAC in a single cycle. The CEVA-TeakLite-lll also offers:

    • Strong bit-manipulation capabilities for stream processing
    • Up to three instructions executed in parallel
    • Dedicated single-precision and double-precision FFT instructions
    • Up to 4GW program memory and 4GW data memory (16-bit words)
    • L1 program memory (cache or TCM)
    • L1 data memory (CEVA-TL3210 = TCM; CEVA-TL3211 = 2-way, set-associative, hardware-configurable cache)

    An integrated Power Scaling Unit (PSU) provides advanced power management including support for clock and voltage scaling, only available with the CEVA-TL3211. This PSU is responsible for the very low-power capability of the CEVA-TeakLite-lll DSP core. This low-power feature has certainly been one of the main reasons for the DSPG HDClear design win into the Gear S2 smartwatch.

    Voice recognition algorithms can require very intensive DSP computation, especially in noisy ambient environment. 20% error rate would be dramatic for any networking or storage system, but a Word Error Rate (WER) under 20% is completely acceptable for voice recognition system. If you speak to pilot your Gear S2 smartwatch using a 5 words sentence, in the worst case scenario the HDClear DSP will transmit 4 words to the main CPU. If you make a test, you realize that the recognition of 4 words in a 5 words sentence is probably enough to rebuild the initial message.

    Did you know that CEVA-TeakLite family of DSPs has shipped in more than 4.5 billion devices to date, making this DSP IP core the leader in audio/voice for mobile ? Now that audio/voice is making its way into more and more devices as a means to control and activate (think of voice-controlled smart home systems, handsfree automotive systems), no doubt that CEVA-TeakLite family of DSPs are set to power billions of new devices outside in the handset space. The Samsung Gear S2 design win is the first of its kind but is truly only the beginning of a new era of smart and connected CEVA -powered devices.

    Eric Esteve from IPNEST

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    Internet of Things 2015 Year End Review: IoT Business Ecosystem

    Internet of Things 2015 Year End Review: IoT Business Ecosystem
    by Alex G. Lee on 01-03-2016 at 12:00 pm
    Categories: Automotive, IoT
    2 Comments

    Goldman Sachs defines the Internet of Things (IoT) as the third wave of internet revolution: By connecting billions of devices to the internet, the IoT can open up a host of new business opportunities and challenges. According to McKinsey, the IoT has the potential to create up to $6 trillion economic value annually by 2025. According to Research and Markets, there are more than 2000 companies that are selling the IoT enabled products, playing a vital role in the IoT technology innovation, or act as an enabler to the IoT business development.

    A business ecosystem is the community of business entities that is formed by the competitive and collaborative interactions among business entities for new innovations. A business ecosystem evolves to form a new value network, and thus, to create a new market. The IoT has various applications including, smart home, connected car, connected health, and business/industrial applications. Thus, many business players across diverse industries including semiconductor, consumer electronics, IT, telecom, healthcare, medical devices, retail, industrial & manufacturing and transportation are participated in the IoT business ecosystem.

    The key IoT business ecosystem players based on their patenting activities are Samsung Electronics, Google, Toyota, Ford, GM, Philips, GE, IBM, Cisco, and Ericsson.

    Samsung Electronics is a key IoT business ecosystem player in the smart home applications. In number of patent applications, Samsung Electronics dominates the patent applications (nearly 15% of the more than 400 total patent applications among more than 70 patent owners). Samsung Electronics patent applications are highly focused on the home automation and security to support its current Samsung SmartThings business. Samsung SmartThings is the first fully integrated smart home system for providing the home automation and safety services to make people’s daily lives easier, more comfortable and safe. Recently, Samsung filed several patent applications regarding home energy management and smart lighting for providing other value added services to make Samsung as the smart home market leader in 2016.

    For example, US20150330652 illustrates the temperature control method and device of a heating/ventilation/air-conditioning (HVAC) system for efficiently saving energy. The method includes determining occupancy or non-occupancy of a user in a space subject to temperature control. When the user’s non-occupancy is determined, the temperature control device determines whether to start the temperature control based on probability distribution of a non-occupancy period that is predetermined. When it is determined to start the temperature control, the temperature control device determines the user’s target temperature based on previously collected data, calculates a setback temperature based on the target temperature, and performs temperature control according to the calculated setback temperature.

    Google (including Nest Labs) is another key IoT business ecosystem player in the smart home applications. In number of patent applications, Google also dominates the patent applications (nearly 15% of the more than 400 total patent applications among more than 70 patent owners). Google patent applications are focused on the energy/utility management. There are also many recent patent applications regarding the safety monitoring (e.g., hazard detecting, elderly care) and home security. For example, US20150120598 illustrates the smart home system for protecting delivered packages from a thief. The system can receive and retain package in a secure location that is trusted by both deliverer and a system user.

    Google also has significant number of patent applications regarding the health monitoring biomedical sensing devices. The health monitoring biomedical sensing devices can provide a real time monitoring of person’s health status such as concentration of glucose, heart rate and blood pressure, and thus, enable remote healthcare services.

    For example, US20150164321 illustrates an eye-mountable device for measuring an intraocular pressure. The device includes a transparent polymeric material having a concave surface that is mounted over a corneal surface of an eye, an antenna, an expandable member, a sensor and control electronics that is embedded in the transparent polymeric material. The device can expand and apply a force to the corneal surface and the sensor can detect a resistance to deformation of the cornea in response to the applied force. The resistance to deformation of the cornea in response to the force applied by the expandable member is indicative of the intraocular pressure of the eye.

    Toyota, Ford, and GM are three key IoT business ecosystem players in the connected car applications. Their patent applications regarding the IoT connected car applications cover the safety/collision avoidance, intelligence navigation, driver assistance for smart driving, infotainment, and vehicle operation automation. The IoT connected car applications exploit the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems.

    For example, Toyota patent application US20140005906 illustrates the system for assisting the driver of a host vehicle through predicting the future position and velocity trajectory of a preceding vehicle. The preceding vehicle is a vehicle immediately ahead of the host vehicle, and the dynamic state of the preceding vehicle was predicted based on data received from surrounding vehicles using the V2V communications. The system allows a more comfortable driving experience in dense traffic environment. US9031779 illustrates the navigation system with the hazard avoidance feature. The system navigation allows for vehicles and other entities to collaborate and share information via vehicular networks regarding hazards, defects, obstacles, flaws, and other abnormalities that exist in any environment. The system navigation automatically detects and catalog environmental hazards and/or obstacles for route planning. Routes can be planned that avoid these hazards reducing lost time or frustration.

    As another example, Ford patent application US20150149088 illustrates the autonomous vehicle collision avoidance system. The system enables autonomous vehicle predicts objects lie in a planned path of the autonomous vehicle. Accordingly, the system determines when a collision between the vehicle and objects is possible and alters the vehicle path to avoid the potential collision.

    Philips is a key IoT business ecosystem player in the connected health applications. Philips announced a partnership with Amazon to provide a new platform for the IoT connected healthcare applications. Philips connected healthcare platform is based on its cloud-based HealthSuite platform and Amazon AWS IoT platform. Philips patent applications regarding the IoT connected health applications cover the healthcare IT System, Tele health/medicine, body sensor network, and preventative and predictive healthcare. For example, US8884754 illustrates the method of monitoring the vital parameters of a patient using the body sensor network. The method improves the data transmission between on-body sensors of the body sensor network with the off-body monitoring device. Philips also has significant number of patent applications regarding the smart lighting system for the smart home/building applications.

    GE is another key IoT business ecosystem player in the connected health applications. GE is also a key IoT business ecosystem player in the industrial applications. According to Industrial Internet Insights Research Report from GE and Accenture, the Industrial Internet—the combination of Big Data analytics with the Internet of Things (IoT)—will produce huge opportunities for companies in Aviation, Oil and Gas, Transportation, Power Generation and Distribution (e.g., smart grids), Manufacturing, Healthcare, and Mining industries. For example, US20150040051 illustrates the industrial monitoring system that provides monitoring capabilities for various types of mechanical devices and systems. US20150032464 illustrates the system to analyze patient information obtained from the biosensors and recommend the personalized medicine therapy approach based on the patient information exploiting the predictive analytics. GE also has significant number of patent applications regarding the smart lighting system for the smart home/building applications and the smart home automation.

    IBM is a key IoT business ecosystem player in the data analytics for the IoT applications. IBM announced a plan to invest more than $3 billion over the next four years to build the IoT business. Predictive analytics analyzes current and historical data to make predictions about future events and trends. Predictive analytics can apply to many IoT applications such as real-time asset management and predictive maintenance of industrial equipment. For example, US20140236650 illustrates the cost effective end-to-end analytics driven asset management by managing maintenance operations (e.g., scheduling, preventive maintenance, operating parameter control). IoT big data analytics are becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. For example, US20150134704 illustrates the system for processing large scale unstructured data in real time.

    Cisco is a key IoT business ecosystem player in the IoT connectivity/networks. Recently Cisco and Ericsson announced a strategic partnership to create the networks of the future. Cisco filed more than 100 patent applications regarding the IoT connectivity/networks. Cisco patent applications cover the intelligent autonomous IoT networks exploiting the machine learning (ML), predictive analytics for the IoT networks, deterministic networking for smart grids, and fog computing. For example, US20150195216 illustrates the use of the ML in order to estimate the behavior of the communication channels based on prediction, and then, to select the appropriate transmission strategy in the multi-hopping networks. US20150333992 illustrates the application of predictive analytics for managing the IoT Networks.

    Billions of interconnected devices that are connected to the internet in the IoT will produce astronomical amount of data to process. The amount of data can easily overload the cloud computing resources at the back-end IT systems. With Fog (or Edge) computing, the problem can be eased by allowing smart devices (e.g., smartphones, PCs, set-top boxes) at the edge of the IoT networks. US20150261876 illustrates the network environment includes multiple fog computing devices each connected with a communication network.

    Ericsson is another key IoT business ecosystem player in the IoT connectivity/networks. Machine to Machine (M2M) communications involve the communication (using wired or wireless means, or a combination of both) between two machines without human intervention. Ericsson is developing the seamless M2M networks with high mobility and reduced latency. For example, US20150319771 illustrates the M2M networking of the IoT device at low cost that complies with modern cellular communication standards while having low power consumption. US20150078327 illustrates the mobility-based radio resource assignment methods. US20150305054 illustrates the method for power optimized transmission scheduling in an energy harvesting M2M device. US20150249901 illustrates the M2M services enablement architecture using a cellular access networks.

    Ericsson is also developing 6LoWPAN. 6LoWPAN is an acronym of IPv6 over Low power Wireless Personal Area Networks. 6LoWPAN is a set of standards defined by the Internet Engineering Task Force (IETF), which enables the efficient use of IPv6 over low-power, low-rate wireless networks on simple embedded devices through an adaptation layer and the optimization of related protocols. Its main goal is to send/receive IPv6 packets over 802.15.4 links. IoT connectivity standard Thread uses 6LoWPAN for connecting smart home devices. 6LoWPAN radio devices are typically constrained with respect to memory/processing resources, power consumption, and radio transmission range. Integration of the network of 6LoWPAN-compliant low-power devices is the challenge and continues to present a significant obstacle to implementing advanced IPv6-based IoT services. Ericsson patent application US20150245332 illustrates the system for providing access with respect to 6LoWPAN format in a number of IoT networking implementations exploiting 6LoWPAN-compliant low-power devices.


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