An integrated fuzzy multi-measurement decision-making model for selecting optimization techniques of semiconductor materials

• Main Authors: Al-Samarraay, Mohammed, Al-Zuhairi, Omar, Alamoodi, A. H., Albahri, O. S., Deveci, Muhammet, Alobaidi, O. R., Albahri, A. S., Kou, Gang
• Format: Article
• Published: PERGAMON-ELSEVIER SCIENCE LTD 2024
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.um.edu.my/44319/; https://doi.org/10.1016/j.eswa.2023.121439

Semiconductor materials play a crucial role in the development of optoelectronics and power devices. However, their evaluation and selection pose a multi-attribute decision-making problem. This problem encompasses various considerations, such as multiple evaluation criteria, data variation, and the importance of criteria multiplicity. Therefore, this study proposes an integrated fuzzy multi-measurement decision-making model (IFMMDMM) to evaluate and select optimization techniques for semi-polar III-V semiconductor materials. The research methodology is designed based on three sequential phases. Firstly, four optimization techniques for semi-polar III-V semiconductor materials and four evaluation criteria are identified to construct the evaluation decision matrix. Secondly, the fuzzy-weighted zero-inconsistency method is developed to evaluate and assign weights to the defined multi-measurement criteria. Thirdly, the fuzzy decision by opinion score method is developed to select the optimization techniques for semi-polar III-V semiconductor materials. The weighting results reveal that the highest weight value was assigned to `root mean square under surface morphology' (0.1382), while `peak-to-valley under surface morphology' received the lowest weight value (0.1074). The selection results indicated that `different flux with fixed cycle NH3 treatment (D)' ranked first, whereas `NH3 flux at changing V/III (A)' had the lowest performance order. Systematic and sensitivity ranking assessments were performed to verify the efficiency of the proposed model.