A floating-gate analog memory device for neural networks

1993 ◽  
Vol 40 (11) ◽  
pp. 2029-2035 ◽  
Author(s):  
O. Fujita ◽  
Y. Amemiya
Keyword(s):  
Neural Networks ◽  
Memory Device ◽  
Floating Gate ◽  
Analog Memory

Carrier Profiling of the 10-nm-order Structure in a 3D Flash Memory Cell Using Scanning Nonlinear Dielectric Microscopy

2019 ◽  
Author(s):  
Jun Hirota ◽  
Ken Hoshino ◽  
Tsukasa Nakai ◽  
Kohei Yamasue ◽  
Yasuo Cho
Keyword(s):  
Spatial Resolution ◽  
Flash Memory ◽  
Analytical Techniques ◽  
Memory Cell ◽  
Memory Device ◽  
Floating Gate ◽  
Order Structure ◽  
Carrier Distribution ◽  
Nonlinear Dielectric ◽  
Flash Memory Cell

Abstract In this paper, the authors report their successful attempt to acquire the scanning nonlinear dielectric microscopy (SNDM) signals around the floating gate and channel structures of the 3D Flash memory device, utilizing the custom-built SNDM tool with a super-sharp diamond tip. The report includes details of the SNDM measurement and process involved in sample preparation. With the super-sharp diamond tips with radius of less than 5 nm to achieve the supreme spatial resolution, the authors successfully obtained the SNDM signals of floating gate in high contrast to the background in the selected areas. They deduced the minimum spatial resolution and seized a clear evidence that the diffusion length differences of the n-type impurity among the channels are less than 21 nm. Thus, they concluded that SNDM is one of the most powerful analytical techniques to evaluate the carrier distribution in the superfine three dimensionally structured memory devices.

scholarly journals Single-Poly Floating-Gate Memory Cell Options for Analog Neural Networks

2021 ◽  
pp. 108062
Author(s):  
Maksym Paliy ◽  
Tommaso Rizzo ◽  
Piero Ruiu ◽  
Sebastiano Strangio ◽  
Giuseppe Iannaccone
Keyword(s):  
Neural Networks ◽  
Memory Cell ◽  
Floating Gate ◽  
Floating Gate Memory ◽  
Analog Neural Networks

scholarly journals Using Floating-Gate Memory to Train Ideal Accuracy Neural Networks

2019 ◽  
Vol 5 (1) ◽  
pp. 52-57 ◽  
Author(s):  
Sapan Agarwal ◽  
Diana Garland ◽  
John Niroula ◽  
Robin B. Jacobs-Gedrim ◽  
Alex Hsia ◽  
...  
Keyword(s):  
Neural Networks ◽  
Floating Gate ◽  
Floating Gate Memory

MBE-grown Si and Si1−xGexquantum dots embedded within epitaxial Gd2O3on Si(111) substrate for floating gate memory device

2013 ◽  
Vol 24 (50) ◽  
pp. 505709 ◽  
Author(s):  
S Manna ◽  
R Aluguri ◽  
A Katiyar ◽  
S Das ◽  
A Laha ◽  
...  
Keyword(s):  
Memory Device ◽  
Floating Gate ◽  
Floating Gate Memory

Quasiperiodic-Chaotic Neural Networks and Short-Term Analog Memory

2021 ◽  
Vol 31 (01) ◽  
pp. 2130003
Author(s):  
Natsuhiro Ichinose
Keyword(s):  
Neural Networks ◽  
Visual Memory ◽  
Large Scale ◽  
Quasiperiodic Solution ◽  
Short Term ◽  
Chaotic Neural Networks ◽  
Analog Memory ◽  
Short Term Visual Memory ◽  
Potential Applications ◽  
Chaotic Neuron

A model of quasiperiodic-chaotic neural networks is proposed on the basis of chaotic neural networks. A quasiperiodic-chaotic neuron exhibits quasiperiodic dynamics that an original chaotic neuron does not have. Quasiperiodic and chaotic solutions are exclusively isolated in the parameter space. The chaotic domain can be identified by the presence of a folding structure of an invariant closed curve. Using the property that the influence of perturbation is conserved in the quasiperiodic solution, we demonstrate short-term visual memory in which real numbers are acceptable for representing colors. The quasiperiodic solution is sensitive to dynamical noise when images are restored. However, the quasiperiodic synchronization among neurons can reduce the influence of noise. Short-term analog memory using quasiperiodicity is important in that it can directly store analog quantities. The quasiperiodic-chaotic neural networks are shown to work as large-scale analog storage arrays. This type of analog memory has potential applications to analog computation such as deep learning.

Inference of Deep Neural Networks with Analog Memory Devices

2020 ◽  
Author(s):  
Stefano Ambrogio ◽  
Pritish Narayanan ◽  
Hsinyu Tsai ◽  
Charles Mackin ◽  
Katherine Spoon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Memory Devices ◽  
Analog Memory

Threshold Voltage Shift in Hetero-nanocystal Floating Gate Flash Memory

2004 ◽  
Vol 832 ◽  
Author(s):  
Yan Zhu ◽  
Dengtao Zhao ◽  
Ruigang Li ◽  
Jianlin Liu
Keyword(s):  
Threshold Voltage ◽  
Flash Memory ◽  
Oxide Thickness ◽  
Memory Device ◽  
Floating Gate ◽  
Threshold Voltage Shift ◽  
Voltage Shift ◽  
Poisson Boltzmann ◽  
Poisson Boltzmann Equation ◽  
Dot Density

ABSTRACTThe threshold voltage shift of a p-channel Ge/Si hetero-nanocrystal floating gate memory device was investigated both numerically and phenomenologically. The numerical investigations, by solving 2-D Poisson-Boltzmann equation, show that the presence of the Ge on Si dot tremendously prolongs the retention time, reflected by the time decay behavior of the threshold voltage shift. The increase of the thickness of either Si or Ge dot will reduce the threshold voltage shift. The shift strongly depends on the dot density. Nevertheless, only a weak relation between the threshold voltage shift and the tunneling oxide thickness was found. A circuit model was then introduced to interpret the behavior of threshold voltage shift, which agrees well with the results of the numerical method.

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