Artificial intelligence approach to analyze SIMS profiles of 11B, 31P and 75As in n- and p-type silicon substrates: experimental investigation

Walid Filali; Mohamed Boubaaya; Elyes Garoudja; Fouaz Lekoui; Ibrahime Abdellaoui; Rachid Amrani; Slimane Oussalah; Nouredine Sengouga

doi:10.1515/zna-2023-0200

Article

Artificial intelligence approach to analyze SIMS profiles of ¹¹B, ³¹P and ⁷⁵As in n- and p-type silicon substrates: experimental investigation

Walid Filali , Mohamed Boubaaya , Elyes Garoudja , Fouaz Lekoui , Ibrahime Abdellaoui , Rachid Amrani , Slimane Oussalah and Nouredine Sengouga

Published/Copyright: October 20, 2023

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From the journal Zeitschrift für Naturforschung A Volume 78 Issue 12

Abstract

In this work, we report an effective approach based on an artificial intelligence technique to investigate the secondary ions mass spectroscopy (SIMS) profiles of boron, phosphorus and arsenic ions. Those dopant ions were implanted into n- and p-type (100) Silicon substrate using the ion implantation technique with energy of 100 and 180 keV. Annealing treatment was conducted at various temperatures ranging from 900 to 1030 °C for 30 min. The doping profile parameters such as the activation energy, diffusion coefficient, junction depth, implant dose, projected range and standard deviation were determined using particle swarm optimization (PSO) algorithm. The efficiency of this strategy was experimentally verified by the fitting between both real measured SIMS profile and predicted ones. In addition, a set of simulated doping profiles was generated for different annealing time to prove the ability of this approach to accurately estimate the above parameters even when changing the experimental conditions.

Keywords: artificial intelligence; doping profile; PSO algorithm; SIMS

Corresponding author: Walid Filali, Plateforme Technologique de Microfabrication, Centre de Développement des Technologies Avancées, cité 20 août 1956, Baba Hassen, 16081 Algiers, Algeria, E-mail: wfilali@cdta.dz

Research ethics: Not applicable.
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission; 1-Walid Filali: Experimental fabrication, Characterization and data analysis, manuscript writing (original draft). 2-Mohamed Boubaaya: SIMS analysis and, physical interpretation. 3-Elyes Garoudja: Problem formulation and PSO algorithm implementation. 4-Fouaz Lekoui: Discussion and results interpretation. 5-Ibrahime Abdellaoui: Data analysis and physical interpretation. 6-Rachid Amrani: Helping in results analysis and interpretation. 7-Slimane Oussalah: Obtained results evaluation and validation. 8-Noureddine Sengouga: Contribution evaluation and manuscript improvement.
Competing interests: The authors state no conflict of interest.
Research funding: None declared.
Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-07-24

Accepted: 2023-10-04

Published Online: 2023-10-20

Published in Print: 2023-12-27

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Articles in the same Issue

https://doi.org/10.1515/zna-2023-0200

Keywords for this article

artificial intelligence; doping profile; PSO algorithm; SIMS