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Updated: Aug 14, 2026

Gyroid Nickel Nanostructures from Diblock Copolymer Supramolecules
Published on: April 28, 2014
Ultrasoft and High-Adhesion Block Copolymers for Neuromorphic Computing
Xiaohong Li1, Xingcheng Ou1, Guoliang Chen1
1Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China.
Abstract:
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.

