Evidence map›Paper›PMID 42076207›Full record

ArticleMicromachines2026

Dual-Side Hybrid Embedding Network for Gain Enhancement of Terahertz Amplifiers at Frequencies Near

Xiaorui Liu, Jianguo Yu, Yun Wang, Yibo Huang, Feixiang Zhang, Zhanjiang Wang, Yaqi Cheng

Abstract read
In one paragraph

Article in Micromachines, 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

7 authors.

Xiaorui LiuSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Jianguo YuSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Yun WangSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Yibo HuangSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Feixiang ZhangSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.ORCID 0009-0003-0298-8470
Zhanjiang WangSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Yaqi ChengSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Funding

National Key Research and Development Program of China 2023YFB2905602National Natural Science Foundation of China 62127802
6 · The paper itself

Abstract

This paper proposes a dual-side hybrid embedding network (DHEN) to mitigate gain degradation in terahertz amplifiers at frequencies near fmax. The proposed approach employs a pre-embedding network for parasitic absorption, followed by Y-embedding for gain enhancement. A theoretical analysis is conducted to derive the embedding conditions for process-constrained circuit synthesis. In this architecture, a capacitive base-side pre-embedding provides intrinsic DC isolation, while a defected-ground-structure (DGS) inductor realizes the Y-embedding inductive element with reduced layout area. Based on the DHEN, a four-stage amplifier is designed in a 130 nm SiGe BiCMOS process. Electromagnetic co-simulation results demonstrate a power gain of 19.3 dB at 280 GHz, corresponding to an 11.5 dB improvement over a conventional unboosted amplifier. The proposed approach provides a unified synthesis methodology that simultaneously addresses parasitic absorption, DC isolation, and gain enhancement for near-fmax THz amplifier design.

Indexed as

defected ground structure (DGS)embedding networkgain enhancementterahertz amplifier

Identifiers

PMID42076207
PMCPMC13118767

What OpenQuestion holds

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Registered trials

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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.