BGU scientists make breakthrough in optical microchips

These new optical chips have many bio-medical and optical-communication applications.

OCTOBER 8, 2016 23:59

Ben Gurion University (photo credit: WWW.PIKIWIKI.ORG.IL) — Ben Gurion University

(photo credit: WWW.PIKIWIKI.ORG.IL)

A breakthrough in optical microchips has been achieved by researchers at Ben-Gurion University of the Negev in Beersheba and the University of Southampton in the UK. They inscribed waveguides – channels for the light to flow – on a piece of polished glass, with molecules attracted to the miniature circuit once charged with oxygen.

These new optical chips have many bio-medical and optical-communication applications. One such application is a tiny spectrometer just four millimeters in size that can provide instantaneous results. The glass instantly analyzes the material’s chemical properties and reveals its unique signature, which is the specific frequency the chemical resonates at when light is passed through it.

Such devices could instantly distinguish between viruses or analyze any material or detect air pollutants.

Current spectrometers are generally several orders of magnitude larger and, in the hospital setting, require a dedicated technician to move from room to room with the device. Moreover, the sample sizes required are substantial – so much so that certain delicate procedures could not be attempted until now lest they harm the patient.

BGU’s Dr. Alina Karabchevsky, who was the principal investigator, and collaborator Prof. Alexey Kavokin of the University of Southampton not only miniaturized the entire detection apparatus, they also dramatically reduced the sample size of the material needed to conduct a chemical analysis and identify its signature.

Their pioneering findings were published recently in Scientific Reports. They are the first to use simple glass as the basis for such chips.

“Our new chip could be the basis for a spectrometer at the bedside of every patient in every hospital. The materials we used – glass and light – are plentiful and cheap. Just a drop of a sample placed on one of the waveguides can be analyzed at the speed of light. In fact, a single piece of glass could analyze a number of materials simultaneously,” said Karabchevsky.

He added that delicate procedures such as analyzing a sample from a sick baby’s lungs, where all that would be needed was a tiny sample, can now be accomplished.

Until now, such procedures required too large a sample size, putting the patient at risk. Other applications include analyzing suspected explosive materials in real time with a handheld device, she says.

“There are many new directions this work encourages us to explore – from fundamental breakthroughs in molecular harmonics excitation under evanescent radiation on a chip to actual devices such as spectrometers on a chip and doctor-in-your-pocket devices.

ROBOTS CAN LEARN BY OBSERVATION It is now possible for machines to learn how natural or artificial systems work simply by watching them – without being told what to look for, according to researchers at the University of Sheffield in the UK.

This could mean advances in the world of technology with machines able to predict human behavior, among other things.

The discovery takes inspiration from the work of pioneering computer scientist Alan Turing, who proposed a test that a machine could pass if it behaved indistinguishably from a human. In this test, an interrogator exchanges messages with two players in a different room, one human and the other a machine.

The interrogator has to find out which of the two players is human. If they consistently fail to do so – meaning that they are no more successful than if they had chosen one player at random – the machine has passed the test and is considered to have human-level intelligence.

Dr. Roderich Gross from the UK university’s automatic control and systems engineering department, said, “Our study uses the Turing test to reveal how a given system – not necessarily a human – works. In our case, we put a swarm of robots under surveillance and wanted to find out which rules caused their movements. To do so, we put a second swarm, made of learning robots, under surveillance too. The movements of all the robots were recorded, and the motion data shown to interrogators.

“Unlike in the original Turing test,” he added, “our interrogators are not human but rather computer programs that learn by themselves. Their task is to distinguish between robots from either swarm. They are rewarded for correctly categorizing the motion data from the original swarm as genuine, and those from the other swarm as counterfeit.

The learning robots that succeed in fooling an interrogator – making it believe their motion data were genuine – receive a reward.”

Gross explained that the advantage of the approach, called “Turing Learning,” is that humans no longer need to tell machines what to look for.

“Imagine you want a robot to paint like Picasso. Conventional machine-learning algorithms would rate the robot’s paintings for how closely they resembled a Picasso. But someone would have to tell the algorithms what is considered similar to a Picasso to begin with. Turing Learning does not require such prior knowledge. It would simply reward the robot if it painted something that was considered genuine by the interrogators. Turing Learning would simultaneously learn how to interrogate and how to paint.”

Thus Turing Learning could lead to significant advances in science and technology, he concluded.

“Scientists could use it to discover the rules governing natural or artificial systems, especially where behavior cannot be easily characterized using similarity metrics,” he said.

“Computer games, for example, could gain in realism as virtual players could observe and assume characteristic traits of their human counterparts. They would not simply copy the observed behavior, but rather reveal what makes human players distinctive from the rest.”

The discovery could also be used to create algorithms that detect abnormalities in behavior – useful for the health monitoring of livestock and for the preventive maintenance of machines, cars and airplanes. Turing Learning could also be used in security applications, such as for lie detection or online identity verification.