共聚物
神经形态工程学
块(置换群论)
材料科学
非易失性存储器
计算机科学
纳米技术
聚合物
电阻随机存取存储器
晶体管
记忆电阻器
人工智能
人工神经网络
光电子学
电气工程
工程类
电压
复合材料
数学
几何学
作者
Ai‐Chun Chang,Tiffany Mulia,Ya‐Shuan Wu,Yi‐Hsun Weng,Yan‐Cheng Lin,Wen‐Chang Chen
摘要
Abstract The rapid development of artificial intelligence has significantly accelerated computational calculation and enlarged the demands in information storage. Therefore, high‐performance memory devices based on new architectures and functionalities, such as resistive memory, transistor memory, and phase change memory, along with their applications in neuromorphic computing and artificial synapse have arisen extensive interest among researchers. In order to improve the memory performance, block copolymer electrets with diversified self‐assembled structures have been extensively developed, and their structure–performance properties have been investigated. Therefore, in this focused review, a variety of block copolymers combining incompatible polymers, such as hydrophilic/hydrophobic, polar/nonpolar, and conjugated/insulating polymers, are introduced in this focused review. The design concepts and recent advances of block copolymers in nonvolatile memory and artificial synapses were addressed. With these divergent structure designs, block copolymers are regarded as potential candidates to provide high performance in memory device applications. This review sheds light on the great potential and importance of block copolymers for optoelectronic device applications.
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