Research Bits: December 5

Neuromorphic nanowires; hybrid silk transistors; artificial retina.


Neuromorphic nanowires

Researchers from UCLA and University of Sydney built an experimental computing system physically modeled after the biological brain. The device is composed of a tangled-up network of wires containing silver and selenium that were allowed to self-organize into a network of entangled nanowires on top of an array of 16 electrodes. The nanowire network physically reconfigures in response to stimulus, with memory based on its atomic structure and spread throughout the system. Where wires overlap, connections can form or break, similar to the behavior of synapses in the biological brain.

The new platform technology modeled after the brain is composed of a tangled-up network of wires containing silver, laid on a bed of electrodes. (Credit: Sam Lilak/UCLA)

Using a new training algorithm co-designed with the hardware that gave the system continuous information about its success at the task in real time while it learned, the system was able to identify handwritten numbers with an overall accuracy of 93.4%. The researchers suggest that by using such HW/SW co-design, nanowire networks could serve a complementary role alongside silicon-based electronic devices for edge computing. [1]

Hybrid silk transistors

Researchers from Tufts University created transistors that replace the insulating material with biological silk fibroin, the structural protein of silk fibers, and used them to build a highly sensitive and ultrafast breath sensor capable of detecting changes in humidity. When the silk insulator absorbs moisture, it acts like a gel carrying whatever ions are contained within. The gate triggers the on-state by rearranging ions in the silk gel. By changing the ionic composition in the silk, the transistor operation changes, allowing it to be triggered by any gate value between zero and one.

“You could imagine creating circuits that make use of information that is not represented by the discrete binary levels used in digital computing, but can process variable information as in analog computing, with the variation caused by changing what’s inside the silk insulator,” said Fiorenzo Omenetto, professor of engineering at Tufts. “This opens up the possibility of introducing biology into computing within modern microprocessors.”

Silk fibroin can be easily modified with other chemical and biological molecules to change its properties, and the researchers suggest that modifications of the silk layer in the transistors could enable devices to detect some cardiovascular and pulmonary diseases, sleep apnea, or pick up carbon dioxide levels and other gases and molecules in the breath that might provide diagnostic information. They believe it could also be used with blood plasma to determine levels of oxygenation and glucose or circulating antibodies. [2]

Artificial retina

Researchers from Purdue University and University of Texas at San Antonio are working to develop the foundation of an artificial retina that, while fairly low resolution, is well suited to sensing movement. In the prototype device, called an organic electrochemical photonic synapse, light triggers an electrochemical reaction that strengthens steadily and incrementally with repeated exposure to light and dissipates slowly when light is withdrawn, creating what is effectively a memory of the light information the device received. That memory could potentially be used to reduce the amount of data that must be processed to understand a moving scene.

“Computer vision systems use a huge amount of energy, and that’s a bottleneck to using them widely. Our long-term goal is to use biomimicry to tackle the challenge of dynamic imaging with less data processing,” said Jianguo Mei, professor of chemistry in Purdue’s College of Science. “By mimicking our retina in terms of light perception, our system can be potentially much less data intensive, though there is a long way ahead to integrate hardware with software to make it become a reality.” [3]


[1] Zhu, R., Lilak, S., Loeffler, A. et al. Online dynamical learning and sequence memory with neuromorphic nanowire networks. Nat Commun 14, 6697 (2023).

[2] Kim, B. J., Bonacchini, G. E., Ostrovsky-Snider, N. A., Omenetto, F. G., Bimodal Gating Mechanism in Hybrid Thin-Film Transistors Based on Dynamically Reconfigurable Nanoscale Biopolymer Interfaces. Adv. Mater. 2023, 35, 2302062.

[3] Chen, K., Hu, H., Song, I. et al. Organic optoelectronic synapse based on photon-modulated electrochemical doping. Nat. Photon. 17, 629–637 (2023).

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