As artificial intelligence calls for novel energy-efficient hardware, neuromorphic computing
systems based on analog resistive switching memory (RSM) devices have drawn great …

Silicon microelectronics, consisting of complementary metal–oxide–semiconductor
technology, have changed nearly all aspects of human life from communication to …

With the arrival of the era of big data, the conventional von Neumann architecture is now
insufficient owing to its high latency and energy consumption that originate from its …

Memristors are drawing attention as neuromorphic hardware components because of their
non‐volatility and analog programmability. In particular, electrochemical metallization (ECM) …

Memristors based on two-dimensional (2D) materials are a rapidly growing research area
due to their potential in energy-efficient in-memory processing and neuromorphic …

Resistive random access memory (RRAM), a new non-volatile memory, enables hardware
accelerators based on in-memory computing with improved throughput and energy …

We have investigated the electrothermal behavior in Cu/ZrO2/Pt conductive bridge random
access memory (CBRAM) memory based on the dual-phase-lag thermal model. We have …

The brain's small-world network utilizes its short-range and long-range synaptic connections
to process information in a complex and energy-efficient manner. To emulate the former …

Electrochemical metallization (ECM) cells are based on the principle of voltage controlled
formation or dissolution of a nanometer-thin metallic conductive filament (CF) between two …

Resistively switching electrochemical metallization memory (ECM) cells are gaining huge
interest, as they are seen as promising candidates and basic building blocks of future …