6–10 Jul 2026
University of the Western Cape
Africa/Johannesburg timezone
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Electrical characterisation of aluminium-polyaniline Schottky diodes using a machine learning approach

8 Jul 2026, 11:20
20m
Lecture Hall DL3 (University of the Western Cape)

Lecture Hall DL3

University of the Western Cape

Oral Presentation Track F - Applied Physics Applied Physics

Speaker

Hlulani Ndlovu (Tshwane University Technology)

Description

Understanding of the behaviour of a Schottky contact is important in the design of optoelectronic devices such as solar cells and photosensors. The behaviour of Schottky diodes depends on physical parameters like the thickness of the metal contact layer and electro-optical parameters of the active layer. Schottky contacts between metals and organic semiconductors are of importance in the growing field of organic electronics. Long turn-around times in the characterisation process of the Schottky contacts is a setback in research and development chains because of the many diode parameters involved. In this study aluminium films of different thicknesses were deposited on polyaniline (PANI) using the resistive deposition technique . Schottky diode parameters like ideality factor, the barrier height, reverse saturation current and ohmic resistance of the devices were found to be dependent on the thickness of the aluminium layer. A Machine Learning (ML) model was then developed to predict Schottky contact diode parameters based on the thickness of the Al-PANI contact layer. The results suggest that ML is an effective approach to accelerate the characterisation of Schottky contact diodes and also avoids waste of material that is typical of the traditional trial and error route.

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Authors

Dr Abraham Kapim (Tshwane University Technology) Hlulani Ndlovu (Tshwane University Technology)

Co-authors

Prof. Mandla Msimanga (Tshwane University Technology) Dr Ntombizonke Kheswa (Ithemba LABS)

Presentation materials