Two-dimensional (2D) transition metal dichalcogenide (TMD) heterostructures have
attracted a lot of attention due to their rich material diversity and stack geometry, precise …

The performance of conventional computer based on von Neumann architecture is limited
due to the physical separation of memory and processor. By synergistically integrating …

The dynamic processing of optoelectronic signals carrying temporal and sequential
information is critical to various machine learning applications including language …

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

Perovskite materials have driven tremendous advances in constructing electronic devices
owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties …

The demand for computing power has been increasing exponentially since the emergence
of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel …

Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for
changes in synaptic strength, enabling the brain to learn from experience. With the rapid …

Simulating biological synaptic functionalities through artificial synaptic devices opens up an
innovative way to overcome the von Neumann bottleneck at the device level. Artificial …

Continued reduction in transistor size can improve the performance of silicon integrated
circuits (ICs). However, as Moore's law approaches physical limits, high‐performance …

Neuromorphic computing can potentially solve the von Neumann bottleneck of current
mainstream computing because it excels at self‐adaptive learning and highly parallel …