Recent Progress of Protein‐Based Data Storage and Neuromorphic Devices

Abstract

By virtue of energy efficiency, high speed, and parallelism, brain‐inspired neuromorphic computing is a promising technology to overcome the von Neumann bottleneck and capable of processing massive sophisticated tasks in the background of big data. The abilities of perceiving and reacting to events in artificial neuromorphic systems allow us to build the communicative electronic–biological interfaces to get closer to electronic life. Protein materials offer great application potentials in such a system due to their sustainability, low cost, controllable hierarchical structure, intrinsic biocompatibility, and biodegradability. Herein, a timely review of the development of protein‐based memories for data storage and neuromorphic computing is provided. Proteins’ unique mechanical, electronic, optical properties, and their broad applications are discussed. Then, the progress of protein‐based two‐terminal memristor and three‐terminal transistor‐type memory is reviewed, and their applications for data storage, logic circuit, and neuromorphic computing are introduced. Finally, the major challenges and outlook toward the future developing directions of protein‐based computing systems are pointed out.

Multi-factor-controlled ReRAM devices and their applications

“…By combining multiplication and addition, biomemristors can be used for neuromorphic computing. 208 In particular, the resistance value of the memristor can be rapidly and reversibly switched under the applied voltage, which makes the neuromorphic computing chip or brain-like chip integrated by the memristor not only perform energy-saving computing, but can also be reprogrammable, which brings unparalleled advantages to neuromorphic computing. 209–212…”

“…By combining multiplication and addition, biomemristors can be used for neuromorphic computing. 208 In particular, the resistance value of the memristor can be rapidly and reversibly switched under the applied voltage, which makes the neuromorphic computing chip or brain-like chip integrated by the memristor not only perform energy-saving computing, but can also be reprogrammable, which brings unparalleled advantages to neuromorphic computing. [209][210][211][212] In summary, such ReRAM devices add extra-factors as extra control parameters besides the traditional electrical pulse to manipulate resistive memories, and it can realize more functions so as to be better used in artificial intelligence, such as integrating the photodetectors with the nonvolatile memory devices, which can simulate visual sensing and visual memory functions.…”

Recordism: A social-scientific prospect of blockchain from social, legal, financial, and technological perspectives

“…Also, the art of folded protein is a way to efficiently store the copy of the blockchain in the near future, thanks to its compact size and persistent nature. Recent protein-based storage (Wang et al, 2021) progress provides a practical way to distribute store the same piece of information with a highly stable shape and link. The protein can be rapidly scanned to find out the difference in their structure hence figuring out the data changes.…”

Evolutionary 2D organic crystals for optoelectronic transistors and neuromorphic computing

“…To date, optoelectronic transistors have been reported as promising artificial synapses based on diverse materials systems, including inorganic perovskites, amorphous oxide, biological materials, organic materials, and two-dimensional materials [9][10][11][12][13][14][15]. Among the aforementioned materials systems, organic-based transistors have a unique combination of intriguing properties, including wide spectrum response for photoelectrical conversion, tunable molecular structures, low-cost fabrication techniques, flexibility, and abundant deposition conditions [16][17][18][19][20][21][22][23][24][25].…”