The new reconfigurable memristor, or an electronic memory device, is based on a molecular system that can transition between on and off states at several discrete sequential voltages, according to a paper published in the journal Nature.
Profuse dendritic-synaptic interconnections among neurons in the neocortex embed intricate logic structures enabling sophisticated decision-making that vastly outperforms any artificial electronic analogues. The physical complexity is far beyond existing circuit fabrication technologies: moreover, the network in a brain is dynamically reconfigurable, which provides flexibility and adaptability to changing environments. In contrast, state-of-the-art semiconductor logic circuits are based on threshold switches that are hard-wired to perform predefined logic functions. To advance the performance of logic circuits, Goswami et al. re-imagined fundamental electronic circuit elements by expressing complex logic in nanometer-scale material properties. Image credit: National University of Singapore.
“This work is a significant breakthrough in our quest to design low-energy computing,” said Dr. A. Ariando, a researcher at the National University of Singapore.
“The idea of using multiple switching in a single element draws inspiration from how the brain works and fundamentally re-imagines the design strategy of a logic circuit.”
Unlike hard-wired standard circuits, the new memristor can be reconfigured using voltage to embed different computational tasks.
“This new discovery can contribute to developments in edge computing as a sophisticated in-memory computing approach to overcome the von Neumann bottleneck, a delay in computational processing seen in many digital technologies due to the physical separation of memory storage from a device’s processor,” Dr. Ariando said.
The new memristor also has the potential to contribute to designing next generation processing chips with enhanced computational power and speed.
“Similar to the flexibility and adaptability of connections in the human brain, our memory device can be reconfigured on the fly for different computational tasks by simply changing applied voltages,” said Dr. Sreetosh Goswami, also from the National University of Singapore.
“Furthermore, like how nerve cells can store memories, the same device can also retain information for future retrieval and processing.”
In their research, the scientists conceptualized and designed a molecular system belonging to the chemical family of phenyl azo pyridines that have a central metal atom bound to organic molecules called ligands.
“These molecules are like electron sponges that can offer as many as six electron transfers resulting in five different molecular states,”
“The interconnectivity between these states is the key behind the device’s reconfigurability,” said Dr. Sreebrata Goswami, a researcher at the Indian Association for the Cultivation of Science.
The authors created a tiny electrical circuit consisting a 40-nm layer of molecular film sandwiched between a top layer of gold, and a bottom layer of gold-infused nanodisc and indium tin oxide.
They observed an unprecedented current-voltage profile upon applying a negative voltage to the device.
Unlike conventional metal-oxide memristors that are switched on and off at only one fixed voltage, these organic molecular devices could switch between on-off states at several discrete sequential voltages.
Building on their research, the team used the molecular memory devices to run programs for different real-world computational tasks.
As a proof of concept, the researchers demonstrated that their technology could perform complex computations in a single step, and could be reprogrammed to perform another task in the next instant.
An individual molecular memory device could perform the same computational functions as thousands of transistors, making the technology a more powerful and energy-efficient memory option.
“The technology might first be used in handheld devices, like cell phones and sensors, and other applications where power is limited,” Dr. Ariando said.
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S. Goswami et al. 2021. Decision trees within a molecular memristor. Nature 597, 51-56; doi: 10.1038/s41586-021-03748-0
