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 …

Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

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

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 …

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

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 …

Memristive and nanoionic devices have recently emerged as leading candidates for
neuromorphic computing architectures. While top-down fabrication based on conventional …