Nanowire LED Sensor Could Revolutionise Human-Machine Interfaces

Researchers have developed a sensor device that converts mechanical pressure -- from a signature or a fingerprint -- directly into light signals that can be captured and processed optically. Shown is Professor Zhong Lin Wang of the Georgia Institute of Technology. Image Credit: Georgia Tech Photo: Gary Meek

A new sensor technology, with sensitivity close to that of human skin, converts fingerprints and signatures into an array of lights via mechanical pressure.

The pioneering work has been conducted by a research team from the Georgia Institute of Technology, which includes Zhong Lin Wang, Regents' professor and Hightower Chair in the School of Materials Science and Engineering at Georgia Tech.

According to Wang,

"You can write with your pen and the sensor will optically detect what you write at high resolution and with a very fast response rate." "This is a new principle for imaging force that uses parallel detection and avoids many of the complications of existing pressure sensors."

The technology is based on a research area known as piezo-phototronics – the process of changing strain applied to piezoelectric materials into light signals to be processed.

Piezoelectricty is electrical charge that builds in certain materials when mechanical stress is applied. Examples of piezoelectric materials include bone and DNA, as well as various types of crystals and ceramics.

In this case, zinc oxide was used as the piezoelectric material, from which LED nanowires were formed.  When these nanowires contact with a gallium nitride (GaN) film, varying electroluminescent signals are emitted, depending on the incident pressure.

Wang explains:

"When you have a zinc oxide nanowire under strain, you create a piezoelectric charge at both ends which forms a piezoelectric potential." "The presence of the potential distorts the band structure in the wire, causing electrons to remain in the p-n junction longer and enhancing the efficiency of the LED."

This schematic shows a device for imaging pressure distribution by the piezo-phototronic effect. The illustration shows a nanowire-LED based pressure sensor array before (a) and after (b) applying a compressive strain. A convex character pattern, such as "ABC," molded on a sapphire substrate, is used to apply the pressure pattern on the top of the indium-tin oxide (ITO) electrode. Image Credit: Courtesy of Zhong Lin Wang.

The nanowires were grown using a low-temperature chemical growth technique on a gallium nitride substrate along a single orientation. A PMMA thermoplastic was then added to the space between to wires, which was subsequently etched away using oxygen plasma, allowing just the tops of the nanowires to be exposed.

Lastly, transparent indium-tin oxide (ITO) film was deposited on top of the nanowires to act as a common electrode, and an ohmic contact at the base of the gallium-nitride was formed using a nickel-gold electrode.

There appear to be a plethora of potential benefits for the sensors industry in using the new technology. For instance, the information captured is at a very high resolution - up to 6,300 dots per inch.

Furthermore, the technology also appears to be stable and reproducible. Over 25 on-off cycles, the fluctuation in output was much lower than the overall signal level, at around 5%.

Wang also describes a fast response time:

"The response time is fast, and you can read a million pixels in a microsecond," "When the light emission is created, it can be detected immediately with the optical fiber."

Further improvements to the technology may be possible if the diameter of the nanowires is reduced, thus allowing more of the wires to be grown.

Aside from recording fingerprints and signatures, the technology could also be implemented in data transmission, processing and recording with on-chip photonics.

Shown are Professor Zhong Lin Wang of the Georgia Institute of Technology and his research team. Image Credit: Georgia Tech Photo: Gary Meek

Original source: Georgia Institute of Technology

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