Humans rely increasingly on sensors to address grand challenges and to improve quality of
life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are …

Research on electronic devices and materials is currently driven by both the slowing down
of transistor scaling and the exponential growth of computing needs, which make present …

Neural networks based on memristive devices,–have the ability to improve throughput and
energy efficiency for machine learning, and artificial intelligence, especially in edge …

Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …

Reproducing ion channel–based neural functions with artificial fluidic systems has long
been an aspirational goal for both neuromorphic computing and biomedical applications. In …

Many in-memory computing frameworks demand electronic devices with specific switching
characteristics to achieve the desired level of computational complexity. Existing memristive …

Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

By integrating sensing, memory and processing functionalities, biological nervous systems
are energy and area efficient. Emulating such capabilities in artificial systems is, however …

Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require
integrating artificial neuromorphic devices with biological systems. Due to their poor …