A Hybrid Fuzzy Decision Model for Evaluating MEMS and IC Integration Technologies

Area of Science:

• * Engineering and Materials Science
• * Decision Science and Artificial Intelligence

Background:

• * The increasing integration of microelectromechanical systems (MEMS) with integrated circuits (IC) is crucial for the Internet of Things (IoT).
• * Evaluating and selecting optimal MEMS-IC integration technologies presents a complex decision-making challenge.
• * Existing methods may not adequately address the inherent uncertainties and multiple criteria in technology selection.

Purpose of the Study:

• * To propose a hybrid fuzzy multi-criteria decision making (MCDM) model for evaluating MEMS and IC integration technologies.
• * To rank alternative MEMS-IC integration technologies based on defined criteria within a fuzzy environment.
• * To provide a strategic framework for selecting future MEMS-IC integration technologies.

Main Methods:

• * Development of a hybrid fuzzy MCDM model combining Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy VIKOR methods.
• * Identification of six key evaluation criteria and five major alternative technologies through literature review and expert input.
• * Fuzzy AHP used to determine criterion weights; Fuzzy VIKOR applied for ranking the alternative technologies.

Main Results:

• * Empirical results indicate that 'development schedule' and 'manufacturing capability' are the most significant criteria.
• * The top-ranked technologies are 3D (three-dimensional) System-in-Package (SiP) and monolithic System-on-Chip (SoC).
• * The proposed model successfully ranks technologies in a fuzzy decision-making context.

Conclusions:

• * The hybrid fuzzy MCDM model offers an effective approach for strategic technology evaluation and selection.
• * The findings highlight critical factors for successful MEMS-IC integration.
• * The model serves as a valuable reference for researchers and industry professionals in the field of MEMS-IC integration.