editor's blog
Subscribe Now

Two Weeks of MEMS and Sensors

Cornucopia.png

The last couple weeks have involved two events with sensors center-stage. The MEMS Executive Congress is a confab of executives from the MEMS industry (and some non-MEMS companies), put on by the MEMS Industry Group (MIG). It’s been around for years.

TSensors, by contrast, started last year as a push by MEMS luminary Janusz Bryzek to identify and eliminate roadblocks to achieving sensor volumes in the trillions (the “T” in “TSensors” is for “trillion.”) While the MIG event tends to involve a conventional conference pace (hopefully with unconventional new ideas), TSensors involves two days of rapid-fire presentations (18 minutes to present, 2 minutes of questions… the timer is ticking!).

I learned lots of new things at both events, and I’ll be rolling out details over time. But, backing up a level, I wanted to take note of the tone taken in particular by TSensors.

The TSensors theme was “Abundance,” leveraging the popular book by Peter Diamandis. First of all, the tone of the book (which I’ll freely admit I haven’t read myself) is said to be highly optimistic – a refreshing take in a time when things don’t always feel like they’re going well.

But the other thing that I came away with was a renewed sense of engineering doing things that help the world. Frankly, some of the goals – like access by all to health care – might be viewed as problematic in some corners. Be that as it may, it felt good to think about the impact of our work on real people.

It’s not like money left the equation; heck, one of the repeated themes was the need to reduce sensor costs so that we can do these things while still rewarding folks for their innovations; it won’t work otherwise. But the difference was that the money, while necessary and important, wasn’t the be-all end-all goal in and of itself. It’s an enabler, not the final result.

Whether the bean counters strip all the hippie-dippy crap by the time this turns from PowerPoint to a business plan remains to be seen. But it’s good to look around occasionally and notice that we do some good work.

And there’s lots more good work to be done.

Leave a Reply

featured blogs
May 19, 2022
The current challenge in custom/mixed-signal design is to have a fast and silicon-accurate methodology. In this blog series, we are exploring the Custom IC Design Flow and Methodology stages. This... ...
May 19, 2022
Learn about the AI chip design breakthroughs and case studies discussed at SNUG Silicon Valley 2022, including autonomous PPA optimization using DSO.ai. The post Key Highlights from SNUG 2022: AI Is Fast Forwarding Chip Design appeared first on From Silicon To Software....
May 12, 2022
By Shelly Stalnaker Every year, the editors of Elektronik in Germany compile a list of the most interesting and innovative… ...
Apr 29, 2022
What do you do if someone starts waving furiously at you, seemingly delighted to see you, but you fear they are being overenthusiastic?...

featured video

Increasing Semiconductor Predictability in an Unpredictable World

Sponsored by Synopsys

SLM presents significant value-driven opportunities for assessing the reliability and resilience of silicon devices, from data gathered during design, manufacture, test, and in-field. Silicon data driven analytics provide new actionable insights to address the challenges posed to large scale silicon designs.

Learn More

featured paper

5 common Hall-effect sensor myths

Sponsored by Texas Instruments

Hall-effect sensors can be used in a variety of automotive and industrial systems. Higher system performance requirements created the need for improved accuracy and more integration – extending the use of Hall-effect sensors. Read this article to learn about common Hall-effect sensor misconceptions and see how these sensors can be used in real-world applications.

Click to read more

featured chalk talk

Machine-Learning Optimized Chip Design -- Cadence Design Systems

Sponsored by Cadence Design Systems

New applications and technology are driving demand for even more compute and functionality in the devices we use every day. System on chip (SoC) designs are quickly migrating to new process nodes, and rapidly growing in size and complexity. In this episode of Chalk Talk, Amelia Dalton chats with Rod Metcalfe about how machine learning combined with distributed computing offers new capabilities to automate and scale RTL to GDS chip implementation flows, enabling design teams to support more, and increasingly complex, SoC projects.

Click here for more information about Cerebrus Intelligent Chip Explorer