Memristors in electronics have shown the potential for a range of applications, ranging from
circuit elements to neuromorphic computing. In recent years, the ability to vary the …

Owing to its close resemblance to biological systems and materials, soft matter has been
successfully implemented in numerous bioelectronic and biosensing applications, as well as …

Brain-inspired computing hardware aims to emulate the structure and working principles of
the brain and could be used to address current limitations in artificial intelligence …

The growing computational demand in artificial intelligence calls for hardware solutions that
are capable of in situ machine learning, where both training and inference are performed by …

Large language models (LLMs) have been transformative. They are pretrained foundational
models that are self-supervised and can be adapted with fine-tuning to a wide range of …

Ferroelectric materials play an important role in a wide spectrum of semiconductor
technologies and device applications. Two-dimensional (2D) van der Waals (vdW) …

Approaching limitations of digital computing technologies have spurred research in
neuromorphic and other unconventional approaches to computing. Here we argue that if we …

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Artificial intelligence now advances by performing twice as many floating-point
multiplications every two months, but the semiconductor industry tiles twice as many …

Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …