Evidence map›Paper›PMID 42080050›Full record

ReviewBiomedical engineering letters2026

Low-power analog and mixed-signal circuit techniques for next-generation miniature implantable neural interface systems.

Linran Zhao, Yaoyao Jia

Abstract readReview
In one paragraph

Review in Biomedical engineering letters, 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

2 authors.

Linran ZhaoThe University of Texas at Austin, Austin, TX USA.
Yaoyao JiaThe University of Texas at Austin, Austin, TX USA.ORCID 0000-0003-2904-1482

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Miniature implantable neural interface devices are increasingly critical for both neuroscience research and clinical neuromodulation applications. However, device miniaturization imposes stringent constraints on power, area, and performance, creating challenges for implementing energy-efficient neuromodulation, high-fidelity neural recording, and wireless data telemetry. This review provides a comprehensive overview of low-power circuit designs enabling next-generation neural interfaces. We discuss energy-efficient stimulation drivers for optogenetic neuromodulation, highlighting advanced switched-capacitor-based techniques that reduce supply voltage requirements while maintaining high-current LED pulses. Low-noise neural recording frontends, including preamplifier-fronted structures, as well as ΔΣ ADC-based and NS-SAR-based direct-digitizing architectures, are reviewed with emphasis on techniques for dynamic range extension, linearity improvement, and artifact tolerance. Finally, state-of-the-art backscatter-based wireless telemetry methods are presented, covering load-shift keying (LSK), frequency-splitting, and push-pull quadrature modulation approaches that decouple power and data transfer to achieve high data rates with minimal energy consumption. This review highlights the critical role of circuit-level innovations in overcoming the power and performance limitations of miniature implants and provides insights for the design of next-generation neural interface systems.

Indexed as

Backscatter telemetryDevice miniaturizationDirect-digitizingEnergy-efficient stimulation driversImplantable neural interfacesNeural recording frontends

Identifiers

PMID42080050
PMCPMC13129172

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.