Technology
A new consortium set up to grow the adoption of GaN devices
A new consortium and project – Pre-packaged Power Devices for PCB Embedded Power Electronics (P3EP) – has been set up to promote ready-to-use GaN solutions to the industrial, automotive and aerospace industries, where high-performance, high-effi ciency, high-power- density devices are most required. T e consortium’s members include RAM
Innovations, PPM Power, Cambridge GaN Devices, Camutronics, the Compound Semiconductor Application Catapult and TTPi, who will work together to pave the way for the development of compact and lightweight power-conversion modules that draw on GaN’s strengths, such as high switching speeds and better effi ciencies, compared to other semiconductors. Modules made with GaN can be almost ten times smaller than those using conventional Si transistors. Starting with pre-packaged GaN dies, the
project will begin with the development of manufacturing processes and testing methods
to produce compact, modularised converter- in-package building blocks based on RAM’s multilayer embedded Power Plane methodology. By avoiding conventional packages with wire bonds, parasitic losses will be signifi cantly reduced. Also, major thermal dissipation improvements can be made, leading to the devices’ greater operational reliability. “T ough the potential of GaN to boost
conversion effi ciencies and increase power densities is universally acknowledged, making it practical for OEMs to use in their designs is still challenging,” said Nigel Salter, General Manager of RAM Innovations. “P3EP is all about establishing a robust and eff ective supply chain that will take the GaN devices out of the lab and into the real world.” Among the applications for these modules
are DC-to-DC converters interfacing the high- voltage batteries in electric vehicles (EVs), cabin- power distribution in aircraſt and power systems for industrial robotics.
Project CAP RAM half-bridge inverter with embedded GaN transistors
“T e automotive, aerospace and industrial
sectors need access to module-based solutions that are simple for them to work with and can be incorporated into existing production fl ows. T ese need to be readily available in high volumes,” said Geoff Haynes, Product Development Manager at RAM. “T rough our involvement in P3EP, we are helping to align the sourcing of wide-bandgap power modules to the expectations of OEMs and systems integrators. T is will mean there is a channel that they can always rely on, with the ability to quickly ramp up from initial samples to production-level quantities.” T e P3EP project is funded by the
Driving the Electric Revolution challenge at UK Research and Innovation.
New guidance set up to make AI- and ML-based automation safer
A team of UK computer scientists from University of York has set guidelines to make machine learning (ML) and artifi cial intelligence (AI) for autonomous technologies safe. As robots, delivery drones, smart factories
and driverless cars become pervasive in industry and our everyday lives, current safety regulations for autonomous technologies are seen as a grey area, lacking robust safety nets; global guidelines for autonomous systems are not as stringent as those of other high-risk technologies. Current standards oſt en lack detail, with some new technologies based around AI and ML arriving on the market unsafe. “T e current approach to assuring safety
in autonomous technologies is haphazard, with very little guidance or set standards in place. Sectors everywhere struggle to develop new guidelines fast enough to ensure that robotics and autonomous systems are safe for people to use. If the rush to market is
Overview of the AMLAS methodology; Assuring Autonomy International Programme at University of York
the most important consideration when developing a new product, it will only be a matter of time before an unsafe piece of technology causes a serious accident,” said Dr Richard Hawkins, Senior Research Fellow and one of the authors of the new safety guide. Developed by the Assuring Autonomy International Programme (AAIP) at the
University of York, the new guidance is called “Assurance of Machine Learning for use in Autonomous Systems”, or AMLAS. T e process systematically integrates safety assurance into the development of ML components. AMLAS has already been used in several
applications, including transport and healthcare.
www.electronicsworld.co.uk April 2022 05
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