Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

ABSTRACT Transducers underpin modern sensing, actuation, and modulation by converting physical signals into electrical or optical representations. Despite rapid advances across materials, fabrication, and device architectures, individual transduction mechanisms remain constrained by intrinsic trade‐offs among bandwidth, sensitivity, speed, energy consumption, and integrability. This review examines transducer technologies across mechanical, acoustic, electromagnetic, and optical domains, and shows that performance evolution is not only increasingly governed by the discovery of new mechanisms, but also by the system‐level coordination of established ones. By organizing representative platforms according to physical scale, operating frequency, and accessible degrees of freedom, we reveal how distinct mechanisms occupy complementary performance envelopes across Hertz‐to‐THz regimes. We highlight how heterogeneous integration and multiphysics co‐design enable these envelopes to be traversed through coordinated architectures that combine flexible interfaces, electromechanical systems, metasurfaces, and photonic circuits. This perspective reframes transducers from isolated interfaces into programmable system nodes that jointly support sensing, modulation, and information processing. The resulting framework provides a foundation for designing reconfigurable and scalable transducer systems for sustainable applications, precision imaging, adaptive communication, edge intelligence, and emerging quantum technologies.

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Publication Details

Journal
Advanced Materials Technologies
Published
2026-03-09
DOI
https://doi.org/10.1002/admt.202502093
Citations
1
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
52.10
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Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

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1 citations
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Neural Networks and Reservoir Computing
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article

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Daoye Zheng, Luwei Wang, Yinpeng Wang, Huajun Liu, Mengyao Xiao, Zhouli Sui, Yuxin Liu, Chengkuo Lee, Aolei Xu
article en
1 citations

Abstract

ABSTRACT Transducers underpin modern sensing, actuation, and modulation by converting physical signals into electrical or optical representations. Despite rapid advances across materials, fabrication, and device architectures, individual transduction mechanisms remain constrained by intrinsic trade‐offs among bandwidth, sensitivity, speed, energy consumption, and integrability. This review examines transducer technologies across mechanical, acoustic, electromagnetic, and optical domains, and shows that performance evolution is not only increasingly governed by the discovery of new mechanisms, but also by the system‐level coordination of established ones. By organizing representative platforms according to physical scale, operating frequency, and accessible degrees of freedom, we reveal how distinct mechanisms occupy complementary performance envelopes across Hertz‐to‐THz regimes. We highlight how heterogeneous integration and multiphysics co‐design enable these envelopes to be traversed through coordinated architectures that combine flexible interfaces, electromechanical systems, metasurfaces, and photonic circuits. This perspective reframes transducers from isolated interfaces into programmable system nodes that jointly support sensing, modulation, and information processing. The resulting framework provides a foundation for designing reconfigurable and scalable transducer systems for sustainable applications, precision imaging, adaptive communication, edge intelligence, and emerging quantum technologies.

Advanced Materials Technologies
Agency for Science, Technology and Research (SG), National University of Singapore (SG), Institute of Materials Research and Engineering (SG), Suzhou Research Institute (CN)
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
52.10
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