Results 101 to 110 of about 4,703 (195)
CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased ...
Jaeseoung Park +13 more
doaj +1 more source
In this study, silicon carbide (SiC) thin films for resistive random-access memory (RRAM) devices were successfully prepared using the radio-frequency magnetron sputtering method at deposition powers of 50 and 75 W for 1 h.
Kai-Huang Chen +4 more
doaj +1 more source
Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation
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 +4 more
wiley +1 more source
3D integration of 2D electronics for AI hardware
We demonstrate TSV‐free monolithic 3D integration of MoS2 transistors and WS2 memristors into a 1T1R architecture, achieving reproducible switching, synaptic functions and ultra‐dense integration. This platform reduces latency and energy cost while enabling in‐memory and neuromorphic computing, positioning 2D‐material‐based M3D electronics as a ...
Hyunho Seok +8 more
wiley +1 more source
Conjugated Polymers Engineered for Flexible/Stretchable Electronics
This review highlights glass transition temperature (Tg) as the central parameter linking molecular structure to device performance in conjugated polymers. By tuning backbone rigidity, side‐chain architecture, and dynamic bonding, Tg governs the balance between π–π stacking–enabled charge transport and mechanical compliance.
Yunchong Yang +5 more
wiley +1 more source
Fault Tolerance for RRAM-Based Matrix Operations
An RRAM-based computing system (RCS) provides an energy efficient hardware implementation of vector-matrix multiplication for machine-learning hardware. However, it is vulnerable to faults due to the immature RRAM fabrication process.
Mengyun Liu +7 more
core +2 more sources
Resistive random-access memory (RRAM) has emerged as one of the most promising device platforms for neuromorphic, in-memory computing because its two-terminal metal–insulator–metal (MIM) structure can reproduce the weight-update behavior of biological ...
Sung Gyu Pyo
doaj +1 more source
MXene‐Based Flexible Memory and Neuromorphic Devices
The unique two‐dimensional structure, excellent electrical conductivity, and diverse surface groups of MXenes have garnered significant attention. Coupled with their exceptional flexibility, MXene‐based devices hold immense potential for flexible memory and neuromorphic systems. This review comprehensively discusses the fundamentals of flexible devices,
Yan Li +13 more
wiley +1 more source
Resistance Non-volatile Memory – RRAM
Electric-pulse induced resistance (EPIR) change effect encompasses the reversible change of resistance of a thin oxide film under the application of short, low voltage pulses.
Yibo Nian +6 more
core +1 more source
An Innovative Approach to Multi‐Valued Logic
The current generation of computer systems operates on the principles of binary logic, which encompasses both logical and arithmetic operations. However, silicon technology has reached its peak performance, prompting researchers to explore alternative methods for enhancing computational efficiency. One such method is the adoption of Multi‐Valued Logic (
Ali Mokhtari, Peyman Kabiri
wiley +1 more source

