Evidence map›Paper›PMID 39774072›Full record

ArticleNature communications2025

In situ training of an in-sensor artificial neural network based on ferroelectric photosensors.

Haipeng Lin, Jiali Ou, Zhen Fan, Xiaobing Yan, Wenjie Hu, Boyuan Cui, Jikang Xu, Wenjie Li, Zhiwei Chen, Biao Yang and 7 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Multi-Modal Low-Power Adaptive Braille Recognition System Based on HfAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. Article
  3. Electrochemical in-biosensing computing.National science review · 2026
    Article
  4. Article
  5. Ferroelectric Devices for In-Memory and In-Sensor Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. 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

17 authors.

Haipeng Lin *Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Jiali Ou *Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Zhen FanInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China. fanzhen@m.scnu.edu.cn.ORCID http://orcid.org/0000-0002-1756-641X
Xiaobing YanKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, China. yanxiaobing@ime.ac.cn.ORCID http://orcid.org/0000-0002-6335-336X
Wenjie HuInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Boyuan CuiInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Jikang XuKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, China.ORCID http://orcid.org/0009-0000-8073-0799
Wenjie LiInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Zhiwei ChenInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Biao YangKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, China.
Kun LiuInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Linyuan MoInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Meixia LiInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.
Xubing LuInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.ORCID http://orcid.org/0000-0002-2552-9571
Guofu ZhouNational Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou, China.
Xingsen GaoInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.ORCID http://orcid.org/0000-0002-2725-0785
Jun-Ming LiuInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China.ORCID http://orcid.org/0000-0001-8988-8429

Funding

National Natural Science Foundation of China (National Science Foundation of China) 92163210 and 52172143
6 · The paper itself

Abstract

In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (<30 μs), and multilevel (>4 bits) photoresponses, as well as long retention (50 days), high endurance (10

Identifiers

PMID39774072
PMCPMC11707328

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.