Results 31 to 40 of about 242 (144)

CMOS Image Sensor Random Telegraph Noise Time Constant Extraction From Correlated To Uncorrelated Double Sampling

IEEE Journal of the Electron Devices Society, 2017
A new method for on-chip random telegraph noise (RTN) characteristic time constant extraction using the double sampling circuit in an 8.3 Mpixel CMOS image sensor is described.
Calvin Yi-Ping Chao, Honyih Tu, Thomas Wu, Kuo-Yu Chou, Shang-Fu Yeh, Fu-Lung Hsueh   +5 more
doaj   +1 more source

Model Implementation of Lorentzian Spectra for Circuit Noise Simulations in the Frequency Domain

IEEE Journal of the Electron Devices Society, 2022
This work presents a new method for the Verilog-A implementation of Lorentzian noise models, in a module called VERILOR, which can automatically generate either Lorentzian or 1/f-like noise spectra depending on the trap density and gate oxide area, for ...
Angeliki Tataridou, Gerard Ghibaudo, Christoforos Theodorou   +2 more
doaj   +1 more source

Low frequency noise peak near magnon emission energy in magnetic tunnel junctions

AIP Advances, 2014
We report on the low frequency (LF) noise measurements in magnetic tunnel junctions (MTJs) below 4 K and at low bias, where the transport is strongly affected by scattering with magnons emitted by hot tunnelling electrons, as thermal activation of ...
Liang Liu, Li Xiang, Huiqiang Guo, Jian Wei, D. L. Li, Z. H. Yuan, J. F. Feng, X. F. Han, J. M. D. Coey   +8 more
doaj   +1 more source

MoS₂ Channel-Enhanced High-Density Charge Trap Flash Memory and Machine Learning-Assisted Sensing Methodologies for Memory-Centric Computing Systems. [PDF]

Adv Sci (Weinh)
Driven by AI computing demands, this study investigates MoS2 channels for 3D NAND Flash to achieve high‐density, low‐power, and reliable nonvolatile memory. MoS2 enables a large memory window and lower operating voltages with low‐k tunneling layer, demonstrating endurance of 10⁴ cycles and retention of 10⁵ s. Comprehensive analyses and machine learning‐
Kim KH, Park JH, Lee KJ, Seo JW, Kim YK, Choi J, Seo MJ, Jang BC.   +7 more
europepmc   +2 more sources

Gate Voltage Dependence of MOSFET Random Telegraph Noise Amplitude at Room and Cryogenic Temperatures

IEEE Journal of the Electron Devices Society
Random telegraph noise (RTN) in 65 nm technology bulk CMOS devices was measured at both 300 K and 1.5 K, and the dependence of noise amplitude on gate voltage was analyzed.
Kiyoshi Takeuchi, Tomoko Mizutani, Takuya Saraya, Hiroshi Oka, Takahiro Mori, Masaharu Kobayashi, Toshiro Hiramoto   +6 more
doaj   +1 more source

Discrete-Trap Effects on 3-D NAND Variability – Part II: Random Telegraph Noise

IEEE Journal of the Electron Devices Society
In Part II of this article we discuss the impact of a discrete treatment of traps on 3-D NAND Flash random telegraph noise (RTN). A higher RTN results when discrete traps are taken into account, that can only be explained by a stronger influence of the ...
Gerardo Malavena, Salvatore M. Amoroso, Andrew R. Brown, Plamen Asenov, Xi-Wei Lin, Victor Moroz, Mattia Giulianini, David Refaldi, Christian Monzio Compagnoni, Alessandro S. Spinelli   +9 more
doaj   +1 more source

An Assessment of the Statistical Distribution of Random Telegraph Noise Time Constants

IEEE Access, 2020
As transistor sizes are downscaled, a single trapped charge has a larger impact on smaller devices and the Random Telegraph Noise (RTN) becomes increasingly important.
Mehzabeen Mehedi, Kean Hong Tok, Jian Fu Zhang, Zhigang Ji, Zengliang Ye, Weidong Zhang, John S. Marsland   +6 more
doaj   +1 more source

RRAM Variability Harvesting for CIM‐Integrated TRNG

Advanced Electronic Materials, EarlyView.
This work demonstrates a compute‐in‐memory‐compatible true random number generator that harvests intrinsic cycle‐to‐cycle variability from a 1T1R RRAM array. Parallel entropy extraction enables high‐throughput bit generation without dedicated circuits. This approach achieves NIST‐compliant randomness and low per‐bit energy, offering a scalable hardware
Ankit Bende, Gokulnath Rajendran, Regina Dittmann, Anupam Chattopadhyay, Vikas Rana   +4 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

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