(Image courtesy of Siemens).
(Image courtesy of Siemens).
(Image courtesy of Siemens).
(Image courtesy of Siemens).
(Image courtesy of Siemens).

Siemens Buys into Machine Learning Tools That Refine Chips

Nov. 28, 2017
Siemens Buys into Machine Learning Tools That Refine Chips

Siemens, to supplement its acquisition of Mentor Graphics, said that it had acquired Solido Design Automation, whose software tools use machine learning to chisel the rough edges from complex chip designs, optimizing power consumption and verifying that the chips are ready to be manufactured.

The acquisition is more evidence that Siemens wants to expand into software tools for computer chips and circuit boards used in everything from factory equipment to airplanes to autonomous cars. Last year, the industrial juggernaut spent $4.5 billion for Mentor Graphics, one of the three major players in electronic design automation.

Solido, which has raised $10.2 million since it was founded in 2005, will be folded into Siemens’ product life cycle management software business. The Plano, Texas-based group sells software to help manage the life cycle of products like electric vehicles and wind turbines, from design to production to service to disposal. The terms of the deal were not disclosed.

Solido’s technology fits with the plans of Chuck Grindstaff, executive chairman of the product life cycle management unit. Grindstaff is betting that companies will start making their own chips that allow factory equipment and cars to monitor their insides and surroundings. Siemens clearly wants to pair Solido's software with its own computer-aided design tools.

Solido exploits machine learning to make sure that the analog and digital circuits inside chips can be manufactured without flaws, which become easier to miss as the computer circuits carved onto silicon wafers become smaller and smaller. More than 40 companies including Nvidia and Broadcom use Solido’s tools for verification and characterization.

The Saskatoon, Canada-based company is not trying is replace engineers with software that design chips from scratch. It is not clear that machine learning algorithms will ever have more than a supporting role, taking tedious tasks out of the hands of humans and helping engineers without decades of experience crafting chips.

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