✦ LIBER ✦

Neural network modeling of GaAs IC material and MESFET device characteristics

✍ Scribed by Gregory L. Creech; Jacek M. Zurada

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 350 KB
Volume: 9
Category: Article
ISSN: 1096-4290
DOI: 10.1002/(sici)1099-047x(199905)9:3<241::aid-mmce8>3.0.co;2-p

No coin nor oath required. For personal study only.

✦ Synopsis

This paper provides an overview of research focused on the utilization of neurocomputing technology to model critical in-process integrated circuit material and device characteristics. Artificial neural networks are employed to develop models of complex relationships between material and device characteristics at critical stages of the semiconductor fabrication process. Measurements taken and subsequently used in modeling include doping concentrations, layer thicknesses, planar geometries, resistivities, device voltages, and currents. The neural network architecture utilized in this research is the multilayer ( ) perceptron neural network MLPNN . The MLPNN is trained in the supervised mode using the generalized delta learning rule. The MLPNN has demonstrated with good results the ability to model these characteristics, and provide an effective tool for parametric yield prediction and whole wafer characterization in semiconductor manufacturing.

📜 SIMILAR VOLUMES

High-temperature device performance and

High-temperature device performance and thermal characteristics of GaAs MESFETs on CVD diamond substrates

✍ L. H. Zhu; A. Christou; D. F. Barbe 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 410 KB 👁 1 views

Modeling of microwave devices with artif

Modeling of microwave devices with artificial neural networks using segmentation and finite elements

✍ Juan M. Cid; Jesús García; Juan Zapata 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 157 KB 👁 1 views

## Abstract The CAD of microwave devices involves extensively computing their electromagnetic response. Using segmentation and finite elements, a neural model for an arbitrary device is developed efficiently, even if the number of design parameters is high. This is made clear with an example of a t

Airbag fabric material modeling of nylon

Airbag fabric material modeling of nylon and polyester fabrics using a very simple neural network architecture

✍ Ramesh Keshavaraj; Richard W. Tock; Dan Haycook 📂 Article 📅 1996 🏛 John Wiley and Sons 🌐 English ⚖ 909 KB

## SYNOPSIS The material properties of engineering fabrics that are used to manufacture airbags can not be modeled easily by the available nonlinear elastic-plastic shell elements. A nonlinear membrane element that incorporates an elaborate tissue material model has been widely used by the auto in

Small-signal and large-signal modeling o

Small-signal and large-signal modeling of active devices using CAD-optimized neural networks

✍ F. Giannini; G. Leuzzi; G. Orengo; M. Albertini 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 298 KB

Artificial neural networks (ANNs) are presented for the technologyindependent modeling of active devices in MMICs. ANNs trained with S-parameter and DC measurements over the entire bias and frequency operational band are used for the small-signal bias-dependent modeling of a low-noise GaAs HEMT devi

Design of microwave devices by segmentat

Design of microwave devices by segmentation, finite elements, reduced-order models, and neural networks

✍ Juan M. Cid; Jesús García; Javier Monge; Juan Zapata 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 191 KB

## Abstract An accurate and fast neural model for complex microwave circuits is efficiently obtained by using segmentation and exploiting the knowledge of frequency response obtained from reduced order models. Information arriving from the excited modes in the connection ports of the regions to be

The use of artificial neural networks in

The use of artificial neural networks in nonlinear microwave devices and circuits modeling: An application to telecommunication system design (invited article)

✍ Y. Harkouss; J. Rousset; H. Chéhadé; E. Ngoya; D. Barataud; J. P. Teyssier 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 559 KB

The design of telecommunication systems is a hierarchical process involving use of a large set of simulation tools and relying on appropriate modeling of system elements to obtain get-it-right-the-first-time fabrication. This paper investigates in detail possible application of neural networks to mo