Ultrasoft and High-Adhesion Block Copolymers for Neuromorphic Computing
The “von Neumann bottleneck” is a formidable challenge in conventional computing, driving exploration into artificial synapses. Organic semiconductor materials show promise but are hindered by issues such as poor adhesion and a high elastic modulus. Here, we combine polyisoindigo-bithiophene (PIID-2...
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Published in | ACS applied materials & interfaces Vol. 16; no. 10; pp. 12897 - 12906 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Chemical Society
13.03.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The “von Neumann bottleneck” is a formidable challenge in conventional computing, driving exploration into artificial synapses. Organic semiconductor materials show promise but are hindered by issues such as poor adhesion and a high elastic modulus. Here, we combine polyisoindigo-bithiophene (PIID-2T) with grafted poly(dimethylsiloxane) (PDMS) to synthesize the triblock-conjugated polymer (PIID-2T-PDMS). The polymer exhibited substantial enhancements in adhesion (4.8–68.8 nN) and reductions in elastic modulus (1.6–0.58 GPa) while maintaining the electrical characteristics of PIID-2T. The three-terminal organic synaptic transistor (three-terminal p-type organic artificial synapse (TPOAS)), constructed using PIID-2T-PDMS, exhibits an unprecedented analog switching range of 276×, surpassing previous records, and a remarkable memory on–off ratio of 106. Moreover, the device displays outstanding operational stability, retaining 99.6% of its original current after 1600 write–read events in the air. Notably, TPOAS replicates key biological synaptic behaviors, including paired-pulse facilitation (PPF), short-term plasticity (STP), and long-term plasticity (LTP). Simulations using handwritten digital data sets reveal an impressive recognition accuracy of 91.7%. This study presents a polyisoindigo-bithiophene-based block copolymer that offers enhanced adhesion, reduced elastic modulus, and high-performance artificial synapses, paving the way for the next generation of neuromorphic computing systems. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1944-8244 1944-8252 1944-8252 |
DOI: | 10.1021/acsami.3c19350 |