Researchers at the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) have reported a breakthrough in energy-efficient phototransistors - devices that could someday help computers process visual information similarly to the human brain and be used as sensors in applications like self-driving vehicles.
The structures rely on metal-halide perovskites. Jeffrey Blackburn, a senior scientist at NREL and co-author of a new paper outlining the research, said: “In general, these perovskite semiconductors are a really unique functional system with potential benefits for a number of different technologies”. “NREL became interested in this material system for photovoltaics, but they have many properties that could be applied to whole different areas of science.”
In this case, the researchers combined perovskite nanocrystals with a network of single-walled carbon nanotubes to create a material combination they thought might have interesting properties for photovoltaics or detectors. When they shined a laser at it, they found a surprising electrical response.
“What normally would happen is that, after absorbing the light, an electrical current would briefly flow for a short period of time,” said Joseph Luther, a senior scientist and co-author. “But in this case, the current continued to flow and did not stop for several minutes even when the light was switched off.”
Such behavior is referred to as “persistent photoconductivity” and is a form of “optical memory,” where the light energy hitting a device can be stored in “memory” as an electrical current. The phenomenon can also mimic synapses in the brain that are used to store memories. Often, however, persistent photoconductivity requires low temperatures and/or high operating voltages, and the current spike would only last for small fractions of a second. In this new discovery, the persistent photoconductivity produces an electrical current at room temperature and flows current for more than an hour after the light is switched off. In addition, only low voltages and low light intensities were found to be needed, highlighting the low energy needed to store memory.
The research provides previously lacking design principles that can be incorporated into optical memory and neuromorphic computing applications. Visual perception accounts for the vast majority of input the brain collects about the world, and these artificial synapses could be integrated into image recognition systems.
“There are many applications where sensor arrays can take in images and apply training and learning algorithms for artificial intelligence and machine-learning-type applications,” Blackburn said. “As an example, such systems could potentially improve energy efficiency, performance, and reliability in applications such as self-driving vehicles.”
The researchers tried three different types of perovskites—formamidinium lead bromide, cesium lead iodide, and cesium lead bromide—and found each was able to produce a persistent photoconductivity.
“What we made is only one of the simplest devices you could make from combining these two systems, and we demonstrated a simplistic memory-like operation,” Blackburn said. “To build a neural network requires integrating an array of these junctions into more complex architectures, where more complex memory applications and image processing applications can be emulated.”
In addition to the NREL team, the new study was also carried out by scientists from the University of Wisconsin–Madison and the University of Toledo.