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Cited by 621

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Self-powered signal processing using vibration-based power generation

Amirtharajah, R., Chandrakasan, A.P.·1998·IEEE Journal of Solid-State Circuits

Abstract

Low power design trends raise the possibility of using ambient energy to power future digital systems. A chip has been designed and tested to demonstrate the feasibility of operating a digital system from power generated by vibrations in its environment. A moving coil electromagnetic transducer was used as a power generator. Calculations show that power on the order of 400 /spl mu/W can be generated. The test chip integrates an ultra-low power controller to regulate the generator voltage using delay feedback techniques, and a low power subband filter DSP load circuit. Tests verify 500 kHz self-powered operation of the subband filter, a level of performance suitable for sensor applications. The entire system, including the DSP load, consumes 18 /spl mu/W of power. The chip is implemented in a standard 0.8 /spl mu/m CMOS process. A single generator excitation produced 23 ms of valid DSP operation at a 500 kHz clock frequency, corresponding to 11,700 cycles.

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Metadata

Authors
Amirtharajah, R., & Chandrakasan, A. P.
Venue
IEEE Journal of Solid-State Circuits
Published
1998-05
Volume
33
Issue
5
Pages
687-695
ISSN
0018-9200 (ISSN-L)
OpenAlex
W2161620971
Cited by
621
References
9
FWCI
18.14
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Self-powered signal processing using vibration-based power generation. https://app.cochat.ai/papers/10.1109/4.668982
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Amirtharajah, R., & Chandrakasan, A. P. (1998). Self-powered signal processing using vibration-based power generation. IEEE Journal of Solid-State Circuits. https://doi.org/10.1109/4.668982

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.