Modeling nanoelectronic CNN cells: CMOS, SETs and QCAs

Gerousis, C.; Goodnick, Stephen M.; Wang, Xiaohui; Porod, Wolfgang; Csurgay, Árpád I.; Tóth, Géza; Lent, Craig S.

doi:10.1109/iscas.2000.857082

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…CNN is one of the most efficient and extensively used deep learning model, which is mostly used for feature extraction, pattern recognition, natural language processing, image, and video recognition. The semiconductor industry is also vastly implementing the CNN algorithm for wafer map defect detection [52], semiconductor thin film thickness prediction [53], manufacturing defect classification [54], wafer surface defect classification [55], and many other industry applications [56], [57], [58], [59], [60]. The CNN technique is benefitting the industrial application in every dimension from years.…”

Section: B 1d-convolutional Neural Network (1d-cnn)mentioning

confidence: 99%

A Hybrid 1D-CNN-LSTM Technique for WKF-Induced Variability of Multi-Channel GAA NS- and NF-FETs

Dash

Sung

2023

IEEE Access

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Presently deep learning (DL) techniques are massively used in the semiconductor industry. At the same time, applying a deep learning approach for small datasets is also an immense challenge as larger dataset generation needs more computational time-cost factors for technology computer-aided design (TCAD) simulation. In this paper, to overcome the aforesaid issue, a hybrid DL-aided prediction of electrical characteristics of the multichannel devices induced by work function fluctuation (WKF) with a smaller dataset is proposed. For the first time, an amalgamation approach of two deep learning algorithms (i.e.1D-CNN and LSTM) is implemented for all four channels (1 to 4 channels) of gate-all-around (GAA) silicon Nanosheet and Nanofin MOSFETs (NS-FETs and NF-FETs). The proposed joint learning framework combines a one-dimensional convolutional neural network (1D-CNN) with long short-term memory (LSTM) model. In this architecture, CNN can extract the features efficiently from the input WKF, and LSTM identifies the historical sequence of the captured features of the input regression data. To illustrate the excellency of the proposed approach, a comparative study of our hybrid model along with three individual DL models i.e. 1D-CNN and LSTM including a baseline multilevel perceptron (MLP) model are demonstrated for a promising small dataset (i.e.1100 samples). The results indicate a superior prediction of 1D-CNN-LSTM in terms of root mean square error (RMSE) with an average value of 1.7943 X10 −7 and R 2 Score with an average value of 96.18% within the shortest time span in contrast to the other three algorithms. Finally, it can be quantified according to the evaluation and performance that hybrid methodology not only adopts the complexity of both NS-and NF-FETs, but also estimates the characteristics of all four channels of it efficiently with a smaller dataset, lesser time span, and reduced computational cost.

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Section: B 1d-convolutional Neural Network (1d-cnn)mentioning

confidence: 99%

A Hybrid 1D-CNN-LSTM Technique for WKF-Induced Variability of Multi-Channel GAA NS- and NF-FETs

Dash

Sung

2023

IEEE Access

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“…[21], [45]). These are of particular interest as they offer potential support for architectures such as the Quantum Cellular Array (QCA) that are part of a range of potential circuit technologies applicable to nano-scale dimensions [35], [36], [15]. Recently, Léonard and Tersoff [26] have shown via quantum transport simulations that, in addition to conventional FET-like operation, CNTs may exhibit resonant tunneling behaviour that is a function of gate bias.…”

Section: Carbon Nanotube Devicesmentioning

confidence: 99%

Exploiting multiple functionality for nano-scale reconfigurable systems

Beckett¹

2003

Proceedings of the 13th ACM Great Lakes Symposium on VLSI - GLSVLSI '03

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It is likely that it will become increasingly difficult to manufacture the complex, heterogeneous logic structures that characterise current reconfigurable logic systems. As a result, these systems may come to be characterised by vast arrays of largely identical devices that are differentiated via postfabrication configuration -but only if low-overhead configuration can be achieved. Two simulation studies are presented that describe some ideas for achieving low-overhead reconfigurability in systems built from nano-scale components. The first is based on variable-threshold devices built from thin-body double gate transistors while a second, more speculative idea is based on recently identified resonant tunneling behaviour in carbon nanotubes. Various logic functions can be configured via the application of a simple bias voltage. Further, two approaches to the issue of generating the required bias voltages based on RTD devices and chalcogenide films are briefly explored.

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“…Recently, Gerousis et al [12] suggested a node for a cellular neural network based on the three-island structure extended with an extra junction (see Figure 11, upper part with dashed junction). The node switches non-linearly between two stable operating points.…”

Section: Neural Circuitsmentioning

confidence: 99%

Design philosophy for nanoelectronic systems, from SETs to neural nets

Roermund

Hoekstra

2000

Int. J. Circ. Theor. Appl.

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This paper describes an overall policy for the design of nanoelectronic systems, showing how specific properties of quantum devices can be exploited instead of being counteracted, by introducing unconventional design approaches. Single‐electron tunnelling (SET) circuit ideas, as components for neural networks, are described in more detail. It is argued that the orthodox theory of single‐electron devices is not appropriate for circuit design and simulation, and needs reconsideration. An overview of SET circuit designs for neural nodes is given. Copyright © 2000 John Wiley & Sons, Ltd.

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Modeling nanoelectronic CNN cells: CMOS, SETs and QCAs

Cited by 9 publications

References 9 publications

A Hybrid 1D-CNN-LSTM Technique for WKF-Induced Variability of Multi-Channel GAA NS- and NF-FETs

A Hybrid 1D-CNN-LSTM Technique for WKF-Induced Variability of Multi-Channel GAA NS- and NF-FETs

Exploiting multiple functionality for nano-scale reconfigurable systems

Design philosophy for nanoelectronic systems, from SETs to neural nets

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