Hysteresis-aware MEMS neuromorphic networks for embedded sensing and computation.

A Al Zubi, M Megdadi, Y Chen, M Okour, M Al Fayad, S Mohaidat, D Lin, F Alsaleem

Abstract read

In one paragraph

Article in Microsystems & nanoengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

A Al ZubiDepartment of Architectural Engineering, University of Nebraska - Lincoln, Lincoln, NE, USA.

M MegdadiDepartment of Mechanical Engineering, University of Nebraska - Lincoln, Lincoln, NE, USA.ORCID http://orcid.org/0000-0003-0428-6264

Y ChenDepartment of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.

M OkourDepartment of Mechanical Engineering, University of Nebraska - Lincoln, Lincoln, NE, USA.

M Al FayadDepartment of Architectural Engineering, University of Nebraska - Lincoln, Lincoln, NE, USA.

S MohaidatDepartment of Mechanical Engineering, University of Nebraska - Lincoln, Lincoln, NE, USA.

D LinGE Aerospace Research, Niskayuna, NY, USA.

F AlsaleemDepartment of Architectural Engineering, University of Nebraska - Lincoln, Lincoln, NE, USA. falsaleem2@unl.edu.ORCID http://orcid.org/0000-0001-8134-4080

Funding

ODNI | Intelligence Advanced Research Projects Activity (IARPA) 2022-21102100011

6 · The paper itself

Abstract

Microelectromechanical systems (MEMS) provide a platform where sensing, memory, and computation can co-exist within the same physical substrate. Here, we present a hysteresis-aware MEMS-based Continuous-Time Recurrent Neural Network (MEMS-CTRNN) that transforms nonlinear bistability from a device limitation into a computational resource. By embedding hysteresis into the training process, the MEMS-CTRNN achieves temporal memory and noise robustness with threefold fewer parameters than digital Long Short-Term Memory (LSTM) models, while maintaining comparable accuracy. Using insect-scale robotic flight data for validation, we demonstrate collision detection at 93% accuracy, establishing MEMS devices as viable analog recurrent processors. This framework suggests a pathway for MEMS-based neuromorphic architectures that unify sensing and computing for applications spanning microrobotics and in situ biomedical monitoring.

Identifiers

PMID42402608

DOI10.1038/s41378-026-01379-x

PMCPMC13334048

What OpenQuestion holds

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.