In this paper, we present the use of artificial neural networks to extract the doping profile from the one-dimensional carrier concentration distribution (and viceversa). The values of the weights and of the biases are computed for the optimum network configuration. The performances and the noise immunity characteristics of the proposed network are assessed and compared with those of the standard techniques. © 2004 Elsevier Ltd. All rights reserved.
On the use of neural networks to solve the reverse modelling problem for the quantification of dopant profiles extracted by scanning probe microscopy techniques / Ciappa, Mauro; Stangoni, Maria; Fichtner, Wolfgang; Ricci, Elisa; Scorzoni, Andrea. - In: MICROELECTRONICS RELIABILITY. - ISSN 0026-2714. - 44:9-11 SPEC. ISS.(2004), pp. 1703-1708. [10.1016/j.microrel.2004.07.058]
On the use of neural networks to solve the reverse modelling problem for the quantification of dopant profiles extracted by scanning probe microscopy techniques
Ricci, Elisa;
2004-01-01
Abstract
In this paper, we present the use of artificial neural networks to extract the doping profile from the one-dimensional carrier concentration distribution (and viceversa). The values of the weights and of the biases are computed for the optimum network configuration. The performances and the noise immunity characteristics of the proposed network are assessed and compared with those of the standard techniques. © 2004 Elsevier Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
