✦ LIBER ✦

Neural network based time domain modelling of 0.18 μm MOSFETs

✍ Scribed by B. Toner; M. S. Alam; V. F. Fusco; G. A. Armstrong

Publisher: John Wiley and Sons
Year: 2002
Tongue: English
Weight: 437 KB
Volume: 35
Category: Article
ISSN: 0895-2477
DOI: 10.1002/mop.10558

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

In this paper a new time domain based neural network model of a 0.18 μm RF MOSFET will be demonstrated. The model consists of only three intrinsic non‐linear current sources, each being modelled by a neural network derived from large signal time domain voltage/current waveform measurements at the intrinsic device terminals. This approach negates the traditional method of multi‐bias S‐parameter measurement providing a fast and accurate method of modelling the transistor under large signal conditions. Full verification of the model will be provided against an 80 μm/0.18 μm MOSFET operating at 2.4 GHz. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 35: 203–206, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10558

📜 SIMILAR VOLUMES

Non-linear modeling of 0.18-μM CMOS usin

Non-linear modeling of 0.18-μM CMOS using neural network

✍ M. S. Alam; G. A. Armstrong; B. Toner; V. F. Fusco 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 113 KB

## Abstract This paper describes an artificial‐neural‐network (ANN) based non‐linear modeling of deep‐submicron CMOS for RF applications. The neural network model is concise when compared to the conventional modeling approach based on empirical equations and can demonstrate comparable accuracy. The

ARTIFICIAL NEURAL NETWORK BASED FAULT DI

ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES

✍ B. SAMANTA; K.R. AL-BALUSHI 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 455 KB

A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden

Univariate modelling of monthly maximum

Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach

✍ Surajit Chattopadhyay; Deepak Jhajharia; Goutami Chattopadhyay 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 234 KB

## Abstract The present paper has adopted an autoregressive approach to inspect the time series of monthly maximum temperature (__T__~max~) over northeast India. Through autocorrelation analysis the __T__~max~ time series of northeast India is identified as non‐stationary, with a seasonality of 12