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Regression Model for the Specific Contact Resistance of SiC Ohmic Contacts

The number of variables involved in the formation of Ohmic contacts to SiC is large, and their relationships to the final contact resistance are often unclear. As such, trial-and-error methods are typically employed to develop or improve SiC contacts. In pursuit of a better alternative, we developed and tested several regression models to predict the specific contact resistance of Ni, Ti, and Al based contacts on both n- and p-type SiC. Literature data was used to train linear regression, Gaussian process regression, and neural network (NN) ensemble models; of these, the NN ensemble was the most effective at predicting contact resistances. We then applied the model to optimize the annealing schedule for Ni contacts to n-type 4H-SiC, and Ti/Al contacts to p-type 4H-SiC. Finally, we use the model to generate optimal simultaneous contact recipes.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING; Vol.34 No.4 November 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 493-499

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

GAUSSIAN PROCESSES
NEURAL NETWORKS
SILICON COMPOUNDS
OHMIC CONTACTS

Info Detail Spesifik

DOI: 10.1109/TSM.2021.3108460

Pernyataan Tanggungjawab

Jordan R. Nicholls, Sima Dimitrijev

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