Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …

With the advent of the post Moore era, modern electronics require further device
miniaturization of all electronic components, particularly ferroelectric memories, due to the …

The conventional computing architecture faces substantial challenges, including high
latency and energy consumption between memory and processing units. In response, in …

Abstract In2Se3, 2D ferroelectric‐semiconductor, is a promising candidate for next‐
generation memory device because of its outstanding electrical properties. However, the …

Neuromorphic computers promise to enhance computing efficiency by eliminating
conventional von Neumann architecture bottlenecks. Bio‐inspired artificial neural networks …

Time‐series analysis and forecasting play a vital role in the fields of economics and
engineering. Neuromorphic computing, particularly recurrent neural networks (RNNs), has …

Reservoir computing is a brain-inspired machine learning framework for processing
temporal data by mapping inputs into high-dimensional spaces. Physical reservoir …

Reservoir computing is a highly efficient machine learning framework for processing
temporal data by extracting input features and mapping them into higher dimensional …

Neuromorphic hardware enables energy-efficient computing, which is essential for a
sustainable system. Recently, significant progress has been reported in neuromorphic …

Abstract Solution-processable 2D materials (2DMs) are gaining attention for applications in
logic, memory, and sensing devices. This review surveys recent advancements in …