Results 71 to 80 of about 1,297 (189)

Silicon Nitride Resistive Memories

Advanced Electronic Materials, EarlyView.
Amorphous SiNx is an attractive resistance switching material for ReRAM applications due to its physicochemical properties, such as humidity resistance, low oxygen diffusivity, and is used as a metal diffusion blocker. By modifying the ratio between N and Si atoms, the microstructure of the SiNx is affected, rendering it possible to change the ...
Alexandros‐Eleftherios Mavropoulis, Nikolaos Vasileiadis, Iosif‐Angelos Fyrigos, Emmanouil Stavroulakis, Caroline Bonafos, Pascal Normand, Georgios Ch. Sirakoulis, Panagiotis Dimitrakis   +7 more
wiley   +1 more source

Array‐Level Characterization of Cryogenic RRAM

Advanced Electronic Materials, EarlyView.
This paper reports the first array‐level comprehensive electrical characterization of a 1024‐device HfO2‐based RRAM array from 300, 77 to 4 K, covering forming, set/reset switching, endurance, retention, relaxation, and read disturb. The results manifest the high performance of RRAM array at cryogenic temperatures and huge application potential for ...
Yuyao Lu, Ruofei Hu, Zhixing Jiang, Feng Xu, Bin Gao, Jianshi Tang, He Qian, Huaqiang Wu   +7 more
wiley   +1 more source

Electromagnetic Analysis of Vertical Resistive Memory with a Sub-nm Thick Electrode

Nanomaterials, 2020
Resistive random access memories (RRAMs) are a type of resistive memory with two metal electrodes and a semi-insulating switching material in-between. As the persistent technology node downscaling continues in transistor technologies, RRAM designers also
Batyrbek Alimkhanuly, Sanghoek Kim, Lok-won Kim, Seunghyun Lee   +3 more
doaj   +1 more source

Highly‐Uniform Passive Crossbar Arrays of Resistive Switching Random Access Memory (RRAM) for In‐Memory Computing Applications

Advanced Electronic Materials, EarlyView.
Passive resistive memory arrays promise efficient in‐memory computing but suffer from sneak paths and programming variability. Here, highly uniform 32 × 32 passive RRAM crossbars are programmed with multilevel precision below 3% error and 99.5% yield.
S. Ricci, P. Mannocci, M. Porzani, D. Bridarolli, F. Carletti, M. Farronato, D. Ielmini   +6 more
wiley   +1 more source

Low-Rank Compensation in Hybrid 3D-RRAM/SRAM Computing-in-Memory System for Edge Computing

Eng
Artificial intelligence (AI) has made significant strides, with computing-in-memory (CIM) emerging as a key enabler for energy-efficient AI acceleration.
Weiye Tang, Long Nie, Cailian Ma, Hao Wu, Yiyang Yuan, Shuaidi Zhang, Qihao Liu, Feng Zhang   +7 more
doaj   +1 more source

Environmental Effects on RRAM Cells Based on 2D Halide Perovskite Materials

Advanced Electronic Materials, EarlyView.
RRAM offers high speed, scalability, and low power, positioning it as a next‐generation non‐volatile memory. Two‐dimensional halide perovskites show promise due to tunable optoelectronic properties and flexible processing but suffer from environmental sensitivity. This review examines degradation from humidity, temperature, light, and strain, discusses
Mojtaba Joodaki, Babitha Arun, Hartmut Hillmer, Chih‐Chieh Hsu   +3 more
wiley   +1 more source

Determining the Relationship Between Composition, Structure, and Device Properties of GexSe1‐x‐Based Selector‐Only Memory

Advanced Electronic Materials, EarlyView.
Composition‐dependent structural evolution in GeXSe1‐X selector‐only memory (SOM) is correlated with device switching behavior. Increasing Ge strengthens network rigidity, suppresses atomic motion, and stabilizes threshold switching, while narrowing the memory window. The revealed structure–property relationship provides a guideline for compositionally
Tien Anh Nguyen, Changdeok Han, Dipannita Ghosh, Suyong Chae, Moongyu Choi, Sanghyun Ban, Myoungsub Kim, Sangmin Yoo, Seung Hwan Lee, Sieun Chae   +9 more
wiley   +1 more source

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

Advanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj, Ella Paasio, Sayani Majumdar   +2 more
wiley   +1 more source

Neuromorphic Denoising with Fully Analog Memristive In‐Memory Computing

Advanced Intelligent Systems, EarlyView.
This article borrows the concepts of episodic memory in human brains to experimentally implement a memristor‐based neuromorphic denoising process. A homogeneous memristor processing unit is experimentally demonstrated for both temporal storage and neural network computation, imitating the synapses in the human brain.
Daijing Shi, Teng Zhang, Yuqi Li, Yaoyu Tao, Bonan Yan, Yuchao Yang   +5 more
wiley   +1 more source

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

Advanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison, Kevin Nadaud, Josquin Harrison, Guillaume F. Nataf, Laura Bégon‐Lours   +4 more
wiley   +1 more source