According to Pagemill Partners, a well-known Silicon Valley venture capital (VC) firm, the number of semiconductor companies spawned with VC funding has been steadily declining for nearly a decade. In 2003, VCs gave life to 63 new chip companies. Last year the number was 13. It’s a trend that promises to reshape the semiconductor industry. (Note: the figures reflect companies formed in North America, Europe and Israel.)

Established chip companies planning to expand via acquisitions should take notice. [More]

Of course electronic design automation (EDA) matters! It’s indispensible to chip design. However, the more important question is whether EDA is keeping pace with increasing IC design complexity. In most cases, the answer is no. Design complexity is increasing much faster than productivity. How do I know? Average development team size continues to grow.

So where does that put EDA? Perhaps the best place to look is the Design Automation Conference held a few weeks ago in San Diego. The venerable 48-year-old show played host to nearly 200 vendors, staged myriad panel sessions and technical papers, and attracted thousands of attendees. But as far as I could tell there were no earth-shattering tool breakthroughs portending to reverse the tide. Maybe you saw something I missed—let me know. Naturally, vendors announced plenty of new products, many of which will undoubtedly boost productivity. But none are likely to obviate the need for ever-larger teams, which is the current prescription for declining relative-productivity. [More]

Politics and productivity seem to go hand-in-hand in semiconductor R&D organizations. Perhaps it’s natural. No manager or project team wants the low productivity Scarlet Letter. So it’s hardly surprising that ostensibly poor performers use politics to avoid scrutiny.

But are these so-called low productivity projects really poor performers? In fact, many are not. Quite the opposite in fact—they often have high productivity (although insufficient throughput) but are mistakenly pigeonholed because their crime was a missed schedule. Moreover, schedule overrun usually is not due to low productivity. [More]

Engineering utilization, productivity and throughput are among the most important metrics for measuring R&D performance. If you want to improve your R&D capability, focus on these three metrics.

Productivity and utilization directly determine throughput, and throughput is the most important of all R&D performance metrics. It measures the rate at which an R&D team develops production-ready products. The higher the productivity and utilization, the higher the R&D throughput. Higher throughput means the R&D team churns out more products in a given period of time. That usually translates to revenue and profits— assuming the rest of the enterprise pulls its weight. Big assumption, right? [More]