31 Jan 2019  |  Author: Dr Mark Dineen 

Quantum, AI and LiDAR Technology in the Home

So there is much excitement in the Dineen house as we’ve bought a robot vacuum cleaner. OK, OK perhaps it’s just me but it’s certainly got me thinking.

The robot does a pretty good job but I believe this is just the beginning and it’s got me thinking how to make it better….there is new technology around the corner that will do just that.

The robot is a mobile device its battery provides the energy to drive the motor for movement, sensing and the vacuum pump. That’s a lot of energy that is needed, the vacuum is power hungry with the amount of dirt collected linked directly to the power of the motor. The system as a whole needs to be lightweight with efficient power conversion. This allows the vacuum to run for longer and at higher cleaning power. It should also charge quickly when docked.

Gallium Nitride (GaN) and Silicon Carbide (SiC) power devices

These new type of Wide Band Gap (WBG) devices are perfectly suited to this application. They offer highly efficient power conversion and management. This and the ability to run at elevated temperatures means that the packaging and overall system requirements can be reduced, dramatically cutting weight. So you have the potential to make your system smaller or you can make a bigger motor both add up to improved performance. Another route is a thinner pump running at high power, thin pump means thin form factor and access underneath furniture a useful feature. Add the GaN devices to the charger and you get quicker, smaller charging units as well, fantastic!

Graphene and Carbon Nanotubes (CNTs) for batteries

Using the latest carbon nano-technology like graphene and carbon nanotubes, it is proposed to increase the storage capacity of batteries by 30-40%. Charging should also be much faster using graphene based supercapacitors. The new batteries use the unprecedented surface area to volume ratio that  graphene and CNTs provide to enhance electrostatic charge storage.

GaAs based VCSELs for LiDAR

The unit does have sensors and they work well, the comment ‘That’s actually quite sick’ to denote it was impressive as it stopped before falling down a step was heard here…praise indeed. However, they are just proximity sensors what would be even better would be sensors that could map out the room. This would allow the unit to plan how’s it going to clean and also alert as to whether there are any obstacles that could be moved. GaAs VCSELs can do this and are already being developed for LiDAR in cars, this is just a low speed low level version of that.

Quantum computing and AI learning

If the area to be cleaned is mapped and then transmitted and stored in the cloud for input into a Quantum computer it then becomes a classic ‘salesman has to visit multiple locations what is the most efficient route’ problem. For 6 cities there are 720 options, for 20 cities the problem would take conventional computing longer than the lifetime of the universe to solve. Quantum computing would take a few minutes to solve it. So through AI you have the problem of how to get round the house using the least power solved. I don’t know how much energy this will save but looking at the way the current one seems to go by trial and error it’s got to be a least 50% more efficient. A massive boost to the energy budget again allowing you to have lighter units or more powerful ones that get more dirt out.

This new technology that is just around the corner is being enabled by our targeted plasma processing solutions. From Atomic Layer Deposition (ALD) of NbN for superconducting Qbits to plasma etch and deposition solutions in GaN and SiC devices Oxford Instruments Plasma Technology is the provider to take your research out of the Lab into the Fab. With high volume manufacturing (HVM) solutions for WBG materials in place over to you Mr Dyson!

For more information on Oxford Instruments Plasma Technology applications visit our page or drop us a line through our email Plasma-experts@oxinst.com