Thinking Big: $1 Trillion MEMS Market – Part 2

Part 1 described Janusz Bryzek‘s ambitious goal of a $1 trillion market for microelectromechanical systems (MEMS) that was the focus of the MicroElectronics Packaging and Test Council (MEPTEC) 10th annual MEMS Technology Symposium. In addition, sensor swarms, road mapping and market numbers were covered. Challenges, example applications, and key takeaways are discussed here along with a final score card on the $1 T market.

Continue reading “Thinking Big: $1 Trillion MEMS Market – Part 2”

Thinking Big: $1 Trillion MEMS Market – Part 1

Usual business advice includes thinking big to win big. Some organizations create Big Hairy Audacious Goals. Others like to find new markets that are underserved and grow to be number one. The semiconductor industry has Moore’s Law – the premise that the minimum cost point is achieved by doubling the number of transistors per chip every two years – driving it forward for almost fifty years.

Janusz Bryzek set a dramatic and ambitious goal of $1 trillion sales for the microelectromechanical systems (MEMS) market in 2022. Even though the MEMS market is expected to have “only” $12 billion in revenue in 2012, he isn’t being called a fool. Having cofounded eight seminal Silicon Valley MEMS companies and currently the Vice President of MEMS Development at Fairchild Semiconductor (which recently acquired his last company), Janusz is taken quite seriously.

Yes, at last week’s MicroElectronics Packaging and Test Council (MEPTEC) 10th annual MEMS Technology Symposium there were some who  Continue reading “Thinking Big: $1 Trillion MEMS Market – Part 1”

Data Exhaust & Data Waves

Is your team reacting or predicting?

Last week, I heard Paul Kedrosky, Senior Fellow at the Kauffman Foundation and Bloomberg contributor, present “Data Exhaust: What We Know About Everything By What No One Tells Us” at the PARC Forum. “Data Exhaust” is his term for the “unintended information we throw off in our daily activities”.

His primary example was the analysis of the debris reported in real time by the California Highway Patrol (CHP). He found patterns that were temporal (Christmas trees in early December and late January) and geographic (mattresses near a discount mattress store immediately adjacent to an on-ramp thereby lacking the opportunity to determine if the mattress was secure prior to driving at speed). More strikingly he discovered the number of ladders dropped on Southern California freeways coincided with Continue reading “Data Exhaust & Data Waves”