Researchers have revealed an adaptive response with a ferroelectric device, which responds to light pulses in a way that resembles the plasticity of neural networks. This behavior could find application in energy-efficient microelectronics.
“Today’s supercomputers and data centers demand many megawatts of power,” said Haidan Wen, a physicist at the U.S. Department of Energy (DOE) Argonne National Laboratory. “One challenge is to find materials for more energy-efficient microelectronics. A promising candidate is a ferroelectric material that can be used for artificial neural networks as a component in energy-efficient microelectronics.”
Ferroelectric materials can be found in different kinds of information processing devices, such as computer memory, transistors, sensors and actuators. Argonne researchers report surprising adaptive behavior in a ferroelectric material that can evolve step-by-step to a desired end, depending on the number of photons from light pulses striking the material. Working alongside Argonne researchers were scientists from Rice University, Pennsylvania State University and DOE’s Lawrence Berkeley National Laboratory.
The paper is published in the journal Advanced Materials.
This team’s material is laden with networked islands or domains that are as distinct as oil in water. These domains are nanometers in size—billionths of a meter—and can rearrange themselves in response to light pulses. This adaptive behavior could be used in the energy-efficient movement of information in microelectronics.
The team’s ferroelectric sample is structured as a sandwich of alternating layers of lead and strontium titanate. Prepared by the Rice University collaborators, this seven-layer sandwich is 1,000 times thinner than a piece of paper. Previously, the team had shined a single, intense light pulse on a sample and created uniform, nanoscale ordered structures.
“This time, we hit the sample with many weak light pulses, each of which lasts a quadrillionth of a second,” Wen said. “As a result, a family of domain structures, rather than a single structure, was created and imaged, depending on the optical dosage.”
To visualize the nanoscale responses, the team called upon the Nanoprobe (beamline 26-ID) operated by the Center for Nanoscale Materials and the Advanced Photon Source (APS). Both are DOE Office of Science user facilities at Argonne. With the Nanoprobe, an X-ray beam tens of nanometers in diameter scanned the sample as it was exposed to a barrage of ultrafast light pulses.
The resulting images revealed networked nanodomains being created, erased and reconfigured due to the light pulses. The regions and boundaries of these domains evolved and rearranged at lengths of 10 nanometers—about 10,000 times smaller than a human hair—to 10 micrometers, roughly the size of a cloud droplet. The final product depended on the number of light pulses used to stimulate the sample.
“By coupling an ultrafast laser to the Nanoprobe beamline, we can initiate and control changes to the networked nanodomains by means of light pulses without requiring much energy,” said Martin Holt, an X-ray and electron microscopy scientist and group leader.
The sample begins with a spiderweb-like arrangement of the nanodomains, and due to the disturbance created by the light pulses, the web breaks down and forms entirely new configurations that work in the service of some desired end in analogy to an adaptive network.
“We have discovered entirely new arrangements of these nanodomains,” said Stephan Hruszkewycz, an Argonne physicist and group leader. “The door is now wide open to many more discoveries. In the future, we will be able to test different regimes of light stimulation and observe even more unknown nanodomains and networks.”
The power to visualize nanoscale change over time will be greatly improved with the recent upgrade to the APS, promising as much as 500 times-brighter X-ray beams.
With this groundbreaking discovery of time-dependent changes in networked nanodomains, developers are on the path to building adaptive networks for information storage and processing. This advancement promises to create more energy-efficient computing systems.
In addition to Wen, Holt and Hruszkewycz, study authors include Marc Zajac, Tao Zhou, Tiannan Yang, Sujit Das, Yue Cao, Burak Guzelturk, Vladimir Stoica, Mathew Cherukara, John Freeland, Venkatraman Gopalan, Ramamoorthy Ramesh, Lane Martin and Long-Qing Chen.
