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

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 implantatio...

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Publicado en:Zeitschrift für Naturforschung Section A: A Journal of Physical Sciences Vol. 78; no. 12; pp. 1143 - 1152
Autores principales: Filali, Walid, Boubaaya, Mohamed, Garoudja, Elyes, Lekoui, Fouaz, Abdellaoui, Ibrahime, Amrani, Rachid, Oussalah, Slimane, Sengouga, Nouredine
Formato: Artículo
Publicado: De Gruyter Dec2023
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Dec2023
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        10.1515/zna-2023-0200
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        atl: Artificial intelligence approach to analyze SIMS profiles of B, P and As in n- and p-type silicon substrates: experimental investigation.
      aug:
        au:
          Filali, Walid
          Boubaaya, Mohamed
          Garoudja, Elyes
          Lekoui, Fouaz
          Abdellaoui, Ibrahime
          Amrani, Rachid
          Oussalah, Slimane
          Sengouga, Nouredine
        affil:
          Plateforme Technologique de Microfabrication, Centre de Développement des Technologies Avancées, cité 20 août 1956, Baba Hassen, 16081 Algiers, Algeria
          Division Milieux Ionisés et Laser, Centre de Développement des Technologies Avancées, cité 20 août 1956, Baba Hassen, 16081 Algiers, Algeria
          Département des Sciences de la Matière, Université Alger1 Ben Youcef Benkhedda, Algiers, Algeria
          Division Microélectronique et Nanotechnologies, Centre de Développement des Technologies Avancées, cité 20 août 1956, Baba Hassen, 16081 Algiers, Algeria
          Laboratory of Metallic and Semiconducting Materials (LMSM), Université Mohamed Khider Biskra, BP 145 RP, 07000 Biskra, Algeria
      su:
        Artificial intelligence
        Particle swarm optimization
        Ion implantation
        Mass spectrometry
        Silicon
      sug:
        subj:
          Artificial intelligence
          Particle swarm optimization
          Ion implantation
          Mass spectrometry
          Silicon
      keyword:
        artificial intelligence
        doping profile
        PSO algorithm
        SIMS
      ab: 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.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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          year: 2023
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