Evidence map›Paper›PMID 42374916›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2026

Differential Image Sensor With Decoupled Static and Dynamic Outputs.

Yegang Liang, Yi Liu, Lin Yuan, Wenhao Ran, Shukun Li, Zeke Liu, Yang Song, Bin Wei, Qingsong Deng, Min Xia and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Differential Image Sensor With Decoupled Static and Dynamic Outputs.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
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

14 authors.

Yegang LiangSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
Yi LiuSchool of Automation, Nanjing University of Information Science and Technology, Nanjing, China.ORCID https://orcid.org/0000-0003-3056-7713
Lin YuanInstitute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, China.
Wenhao RanSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
Shukun LiSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
Zeke LiuInstitute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, China.ORCID https://orcid.org/0000-0002-2507-4386
Yang SongSchool of Automation, Nanjing University of Information Science and Technology, Nanjing, China.
Bin WeiSchool of Materials, Sun Yat-sen University, Shenzhen, China.
Qingsong DengCollege of Materials Science and Engineering, Beijing University of Technology, Beijing, China.
Min XiaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing, China.ORCID https://orcid.org/0000-0003-4681-9129
You MengChangsha Semiconductor Technology and Application Innovation Research Institute, College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, China.
Zhuoran WangSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.ORCID https://orcid.org/0000-0002-3055-3437
Johnny C HoDepartment of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China.ORCID https://orcid.org/0000-0003-3000-8794
Guozhen ShenSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.ORCID https://orcid.org/0000-0002-9755-1647

Funding

Beijing Natural Science Foundation F262035Beijing Natural Science Foundation Z210006Fundamental Research Funds for the Central UniversitiesHebei Provincial Natural Science Foundation F2025105012National Natural Science Foundation of China 42405148National Natural Science Foundation of China 62204015National Natural Science Foundation of China 62404017National Natural Science Foundation of China 62461160330National Natural Science Foundation of China 62574022Post-doctoral Fellowship Program of CPSF 2025T181112Post-doctoral Fellowship Program of CPSF GZB20240938Research Grants Council of the Hong Kong Special Administrative Region CRS_CityU101/24
6 · The paper itself

Abstract

Acquiring and processing full-motion details in machine vision typically consumes a substantial amount of energy. In contrast, a hierarchical processing architecture, combining a low-power standby front end with an on-demand activated back end, provides an optimized energy-performance tradeoff. To achieve this, the complete acquisition and decoupling of static (brightness) and dynamic (amplitude and polarity) output at the sensory level are essential for activating on-demand vision function. Here, we report a differential image sensor (DIS) that leverages differential photodiodes with decoupled differential and tunneling modes. These modes can be read out via conventional ROICs, paving the way for the up-scaled integration (e.g., 640 × 512). With on-demand activated dynamic and static modes, the DIS implements a hierarchical motion-processing pipeline-from sparse motion detection to optical flow and depth analysis. This work provides a power-efficient and scalable strategy for advancing vision-based AIoT applications.

Indexed as

autonomous drivingCMOSdepth estimationdifferential image sensordynamic vision sensoroptical flowsparse data

Identifiers

PMID42374916
PMCPMC13431855

What OpenQuestion holds

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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.