Open AccessHalide perovskite for low‐power consumption neuromorphic devices
Author(s) -
Raifuku Itaru,
Chao YungPin,
Chen HongHsueh,
Lin ChenFu,
Lin PeiEn,
Shih LiChung,
Chen KuanTing,
Chen JungYao,
Chen JenSue,
Chen Peter
Publication year - 2021
Publication title -
ecomat
Language(s) - English
Resource type - Journals
ISSN - 2567-3173
DOI - 10.1002/eom2.12142
Subject(s) - neuromorphic engineering , perovskite (structure) , halide , computer science , dataflow , energy consumption , von neumann architecture , materials science , efficient energy use , nanotechnology , embedded system , artificial intelligence , artificial neural network , chemistry , engineering , electrical engineering , parallel computing , inorganic chemistry , crystallography , operating system
The rapid emergency of data science, information technology, and artificial intelligence (AI) relies on massive data processing with high computing efficiency and low power consumption. However, the current von‐Neumann architecture system requires high‐energy budget to process data computing and storage between central computing unit and memory. To overcome this problem, neuromorphic computing system which mimics the operation of human brain has been proposed to perform computing in an energy‐efficient manner. Recently, organic–inorganic halide perovskite compounds have been demonstrated as promising components for neuromorphic devices owing to their strong light absorption, solution processability, and unique properties such as ion migration, carrier trapping effects and phase transition. In this review paper, we report recent advances of neuromorphic devices which employed organic–inorganic halide perovskite compounds by analyzing their fundamental operating mechanisms, device architectures, applications and future prospective.