Jul 22, 2014

SiTime Adds Temperature Compensation

posted by Bryon Moyer

SiTime came out with a 32-kHz temperature-compensated MEMS oscillator a few weeks back, targeting the wearables market. 32 kHz is popular because dividing by an easy 215 gives a 1-second period. Looking through the story, there were a couple elements that bore clarification or investigation.

Let’s back up a year or so to when they announced their TempFlat technology. The basic concept is of a MEMS oscillator that, somehow, is naturally compensated against temperature variation without any circuitry required to do explicit compensation.

At the time, they said they could get to 100 ppb (that’s “billion”) uncompensated, and 5 ppb with compensation. (The “ppb” spec represents the complete deviation across the temperature range; a lower number means a flatter response.) This year, they announced their compensated version: They’re effectively taking a 50 ppm (million, not billion) uncompensated part and adding compensation to bring it down to 5 ppm. I was confused.

On its face, the compensation is a straightforward deal: take the temperature response of the bare oscillator and reverse it.

Figure.jpg

Image courtesy SiTime

But what about the “millions” vs. “billions” thing? Why are we compensating within the “millions” regime if they could get to ppb uncompensated?

Turns out, in the original TempFlat release, they were talking about where they think the TempFlat technology can eventually take them – not where their products are now. For now, they need to compensate to get to 5 ppm. In the future, they see doing 100 ppb without compensation, 5 ppb with compensation. That’s a 1000x improvement over today’s specs. Critically, from what they’ve seen published by their competition, they say that they don’t see their competitors being able to do this.

So, in short: ppmillions today, ppbillions later. These are the same guys, by the way, that have also implemented a lifetime warranty on their parts.

There was one other thing I was hoping I’d be able to write more about: how this whole TempFlat thing works. We looked at Sand 9’s and Silicon Labs’ approaches some time back; they both use layered materials with opposing temperature responses to flatten things out. So how does SiTime do it?

Alas, that will remain a mystery for the moment. They’re declining to detail the technology as a competitive defense thing. The less the competition knows…

You can read more about SiTime’s new TCXO in their announcement.

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Jul 21, 2014

Improved FPGA Tool Results

posted by Bryon Moyer

A bit over a year ago, we looked at startup Plunify, who was marketing cloud-based FPGA tool instantiations. I talked to them again at the recent DAC, and they appear to be carrying out the typical modern startup roadmap, where you start with something, find out what people really do with it, and then use that information to drive new, and sometimes wholly different, products.

What they learned with their original offering was that the analytics module was really popular. So they figured out how to harness the information to help automate FPGA design optimization in the FPGA tools.

The result is called InTime, and it rides over the top of the Altera and Xilinx tools. It does a series of builds, watching the results, and then making recommendations to the designer as to which settings and constraints will provide the best results. Notably, it doesn’t touch the RTL, so this is about matching up the existing design with the tool in the most effective way.

This isn’t a typical design space exploration platform, which tends to have an element of random. This is a directed algorithm that looks at the results of the original full runs and then uses those analytics to refine the settings and constraints to achieve results that they claim to be 30-40% better than what design space exploration provides.

Not only does it improve the design at hand, but they say it can learn over time. If you’re using the cloud, then the global tool accumulates the learning, improving over time. One thing that’s changed from their original offering, however, is the cloud focus. While still available, too many companies are reluctant to go to the cloud, so they also support local instantiation. When implemented locally, the learning will accrue to the benefit of all local designs.

You can learn more in their recent announcement.

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Jul 10, 2014

Muscling Up

posted by Bryon Moyer

We’ve seen gesture recognition before, and the two major modes, if you will, are using cameras (either 2- or 3-D) to “see” and interpret gestures and using inertial sensors to detect hand motion and infer gestures.

Thalmic is about to launch its own gesture control armband, but they rely on a completely different source of information for detecting gestures: muscle movements. Or, more accurately, the electrical signals that govern muscle movement.

The measurement technique is called “electromyography” (EMG), and the device they’re building is called the Myo. While it does contain an inertial sensor, they say that they can detect much more subtle gestures by reading the muscles and cross-referencing that information with that of the IMU, making outsized gesturing less necessary. They claim that the EMG readings are impervious to sweat, dryness, heat, hair, and differences in muscle tone.

Each device contains 8 EMG sensors plus an IMU, some computing capability, and Bluetooth LE. The signals are processed in the armband; the output is an event representing a classified gesture. All of the usable gestures are pre-defined; they’re keeping the number of gestures to a small number.

While the gestures are fixed, their meanings aren’t. Application developers can use their SDK to assign specific semantics for the gestures within their applications. It’s even possible to fuse the events from two different armbands (one on each arm) for more complex two-handed gesturing.

I talked to them in May at the Embedded Vision Summit (ironic); at that time they had alpha samples out for developers. They recently announced the final design, slimming down and changing the look as compared to the alpha armband. In the process, they had to redo some of the electronics to accommodate the shape – and, according to their blog, they’ve improved the electrical performance in the process. Final devices are now expected to ship in September.

Myo_figure.png

 

This doesn’t strike me as something you’d just wear around; it’s still pretty bulky as an accessory. But using it specifically as an input device for things like gaming is an interesting twist. It will also be interesting to see what new roles EMG may provide in future devices.

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