In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

With scaling, existing charge-based memory technologies exhibit limitations due to charge
leaking away easily in a smaller device. Therefore, non-charge based memory technologies …

Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical
way, by enabling machine learning in the industry, business, health, transportation, and …

The close replication of synaptic functions is an important objective for achieving a highly
realistic memristor-based cognitive computation. The emulation of neurobiological learning …

Interface-driven effects in ferroelectric van der Waals (vdW) heterostructures provide fresh
opportunities in the search for alternative device architectures toward overcoming the von …

In our brain, information is exchanged among neurons in the form of spikes where both the
space (which neuron fires) and time (when the neuron fires) contain relevant information …

Neuromorphic computing has emerged as one of the most promising paradigms to
overcome the limitations of von Neumann architecture of conventional digital processors …

As a new generation of non-volatile memory, phase change random access memory
(PCRAM) has the potential to fill the hierarchical gap between DRAM and NAND FLASH in …

Neuromorphic computing has shown great advantages towards cognitive tasks with high
speed and remarkable energy efficiency. Memristor is considered as one of the most …

A hardware-based neural network that enables on-chip training using a thin-film transistor-
type AND flash memory array architecture is designed. The synaptic device constituting the …