A review of acoustic metamaterials for electrical devices reliability: A reliability-oriented design perspective
Source abstract
Abstract
Modern electronic systems increasingly operate in harsh environments where vibration, thermal cycling, and electromagnetic interference (EMI) jointly threaten their long-term reliability. Acoustic metamaterials (AMMs), with their ability to manipulate wave propagation through engineered subwavelength structures, offer a promising pathway toward mitigating these reliability risks. This review provides a comprehensive overview of AMMs from a reliability-oriented design (ROD) perspective. The proposed ROD framework systematically maps environmental stressors to failure mechanisms and corresponding AMM strategies, thereby bridging the gap between material innovation and reliability assurance. Core AMM mechanisms—including local resonance, Bragg scattering, and cavity absorption—are analyzed alongside emerging multifunctional designs that integrate mechanical, thermal, and electromagnetic functions. Representative applications in MEMS, RF resonators, vehicular electronics, and data-center cooling systems demonstrate the practical benefits of AMMs in suppressing failure-inducing stressors, while emerging applications such as voice security and wearable devices are also introduced. The discussion section addresses persistent challenges in multifunctional coupling, scale incompatibility, manufacturing constraints, and the need for reliable integration across multiple physical domains, while also highlighting emerging trends in intelligent design facilitated by artificial intelligent (AI) integration. This review links material-level innovation and system-level reliability, offering a new paradigm for embedding AMMs into next-generation resilient electronic systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Acoustic Wave Phenomena Research · Aeroelasticity and Vibration Control · Dielectric materials and actuators
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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