An intrinsically stretchable rubbery semiconductor with high mobility is critical to the realization of high-performance stretchable electronics and integrated devices for many applications where large mechanical deformation or stretching is involved. Here, we report fully rubbery integrated electronics from a rubbery semiconductor with a high effective mobility, obtained by introducing metallic carbon nanotubes into a rubbery semiconductor composite. This enhancement in effective carrier mobility is enabled by providing fast paths and, therefore, a shortened carrier transport distance. Transistors and their arrays fully based on intrinsically stretchable electronic materials were developed, and they retained electrical performances without substantial loss when subjected to 50% stretching. Fully rubbery integrated electronics and logic gates were developed, and they also functioned reliably upon mechanical stretching. A rubbery active matrix based elastic tactile sensing skin to map physical touch was demonstrated to illustrate one of the applications.
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Artificial neuromorphic cognitive skins based on distributed biaxially stretchable elastomeric synaptic transistors
Cephalopod (e.g., squid, octopus, etc.) skin is a soft cognitive organ capable of elastic deformation, visualizing, stealth, and camouflaging through complex biological processes of sensing, recognition, neurologic processing, and actuation in a noncentralized, distributed manner. However, none of the existing artificial skin devices have shown distributed neuromorphic processing and cognition capabilities similar to those of a cephalopod skin. Thus, the creation of an elastic, biaxially stretchy device with embedded, distributed neurologic and cognitive functions mimicking a cephalopod skin can play a pivotal role in emerging robotics, wearables, skin prosthetics, bioelectronics, etc. This paper introduces artificial neuromorphic cognitive skins based on arrayed, biaxially stretchable synaptic transistors constructed entirely out of elastomeric materials. Systematic investigation of the synaptic characteristics such as the excitatory postsynaptic current, paired-pulse facilitation index of the biaxially stretchable synaptic transistor under various levels of biaxial mechanical strain sets the operational foundation for stretchy distributed synapse arrays and neuromorphic cognitive skin devices. The biaxially stretchy arrays here achieved neuromorphic cognitive functions, including image memorization, long-term memorization, fault tolerance, programming, and erasing functions under 30% biaxial mechanical strain. The stretchy neuromorphic imaging sensory skin devices showed stable neuromorphic pattern reinforcement performance under both biaxial and nonuniform local deformation.
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- Award ID(s):
- 1931893
- NSF-PAR ID:
- 10391550
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 119
- Issue:
- 23
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Neuromorphic systems, which emulate neural functionalities of a human brain, are considered to be an attractive next‐generation computing approach, with advantages of high energy efficiency and fast computing speed. After these neuromorphic systems are proposed, it is demonstrated that artificial synapses and neurons can mimic neural functions of biological synapses and neurons. However, since the neuromorphic functionalities are highly related to the surface properties of materials, bulk material‐based neuromorphic devices suffer from uncontrollable defects at surfaces and strong scattering caused by dangling bonds. Therefore, 2D materials which have dangling‐bond‐free surfaces and excellent crystallinity have emerged as promising candidates for neuromorphic computing hardware. First, the fundamental synaptic behavior is reviewed, such as synaptic plasticity and learning rule, and requirements of artificial synapses to emulate biological synapses. In addition, an overview of recent advances on 2D materials‐based synaptic devices is summarized by categorizing these into various working principles of artificial synapses. Second, the compulsory behavior and requirements of artificial neurons such as the all‐or‐nothing law and refractory periods to simulate a spike neural network are described, and the implementation of 2D materials‐based artificial neurons to date is reviewed. Finally, future challenges and outlooks of 2D materials‐based neuromorphic devices are discussed.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2204852119
