Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (14/14 displayed)

  • 2022Flower-like nanosheets FeCo2O4 for application in supercapacitor and dye-sensitized solar cell9citations
  • 2021Synaptic behaviour of TiO x/HfO2RRAM enhanced by inserting ultrathin Al2O3layer for neuromorphic computing26citations
  • 2021Transformation of digital to analog switching in TaO x -based memristor device for neuromorphic applications51citations
  • 2021Transformation of digital to analog switching in TaOx-based memristor device for neuromorphic applications51citations
  • 2020Facile and One-Step in Situ Synthesis of Pure Phase Mesoporous Li2MnSiO4/CNTs Nanocomposite for Hybrid Supercapacitors37citations
  • 2020The synergistic effect of iron cobaltite compare to its single oxides as cathode in supercapacitor21citations
  • 2020Barrier layer induced switching stability in Ga:ZnO nanorods based electrochemical metallization memory20citations
  • 2019Synthesis of Free-Standing Flexible rGO/MWCNT Films for Symmetric Supercapacitor Application60citations
  • 2018The impact of TiW barrier layer thickness dependent transition from electro-chemical metallization memory to valence change memory in ZrO 2 -based resistive switching random access memory devices31citations
  • 2018The impact of TiW barrier layer thickness dependent transition from electro-chemical metallization memory to valence change memory in ZrO2-based resistive switching random access memory devices31citations
  • 2018Controlled resistive switching characteristics of ZrO2-based electrochemical metallization memory devices by modifying the thickness of the metal barrier layer25citations
  • 2017Peroxide induced volatile and non-volatile switching behavior in ZnO-based electrochemical metallization memory cell34citations
  • 2017Ternary Au/ZnO/rGO nanocomposites electrodes for high performance electrochemical storage devices37citations
  • 2014Forming-free bipolar resistive switching in nonstoichiometric ceria films101citations

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Chart of shared publication
Kumar, Amit
4 / 39 shared
Saaid, Farish Irfal
2 / 2 shared
Winie, Tan
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Kasim, Muhd Firdaus
1 / 1 shared
Yang, Chien Chih
1 / 1 shared
Chandrasekharan, S.
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Pradhan, A.
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Sze, S. M.
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Pattanayak, B.
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Chu, C. A.
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Panda, D.
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Simanjuntak, Firman
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Rajasekaran, Sailesh
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Prodromakis, Themis
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Saleem, Aftab
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Chandrasekaran, Sridhar
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Simanjuntak, Firman Mangasa
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Sharma, Yogesh
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Singh, Meetesh
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Kumar, Nagesh
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Yang, Chih Chieh
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Azman, N. S. H.
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Arsyad, Akmal
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Panda, Debashis
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Pattanayak, Bhaskar
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Singh, Pragya
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Simanjuntak, Firman M.
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Leu, Jim
1 / 1 shared
Aluguri, Rakesh
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Lin, Chun Chieh
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Chaudhary, Manchal
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Doong, Ruey An
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Rana, Anwar Manzoor
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Jieng, Jheng Hong
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Lin, Chun An
1 / 1 shared
Ismail, Muhammad
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Nadeem, Muhammad Younus
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Chand, Umesh
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Talib, Ijaz
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Hung, Chung Jung
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Tsai, Tsung Ling
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Ahmed, Ejaz
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Huang, Chun Yang
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Chart of publication period
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Co-Authors (by relevance)

  • Kumar, Amit
  • Saaid, Farish Irfal
  • Winie, Tan
  • Kasim, Muhd Firdaus
  • Yang, Chien Chih
  • Chandrasekharan, S.
  • Pradhan, A.
  • Sze, S. M.
  • Pattanayak, B.
  • Chu, C. A.
  • Panda, D.
  • Simanjuntak, Firman
  • Rajasekaran, Sailesh
  • Prodromakis, Themis
  • Saleem, Aftab
  • Chandrasekaran, Sridhar
  • Simanjuntak, Firman Mangasa
  • Sharma, Yogesh
  • Singh, Meetesh
  • Kumar, Nagesh
  • Yang, Chih Chieh
  • Azman, N. S. H.
  • Arsyad, Akmal
  • Panda, Debashis
  • Pattanayak, Bhaskar
  • Singh, Pragya
  • Simanjuntak, Firman M.
  • Leu, Jim
  • Aluguri, Rakesh
  • Lin, Chun Chieh
  • Chaudhary, Manchal
  • Doong, Ruey An
  • Rana, Anwar Manzoor
  • Jieng, Jheng Hong
  • Lin, Chun An
  • Ismail, Muhammad
  • Nadeem, Muhammad Younus
  • Chand, Umesh
  • Talib, Ijaz
  • Hung, Chung Jung
  • Tsai, Tsung Ling
  • Ahmed, Ejaz
  • Huang, Chun Yang
OrganizationsLocationPeople

article

Transformation of digital to analog switching in TaOx-based memristor device for neuromorphic applications

  • Rajasekaran, Sailesh
  • Prodromakis, Themis
  • Saleem, Aftab
  • Simanjuntak, Firman Mangasa
  • Tseng, Tseung-Yuen
  • Chandrasekaran, Sridhar
Abstract

<p>An oxidizable metal diffusion barrier inserted between the active metal electrode and the switching layer decreases the electroforming voltage and enhances the switching stability and synaptic performances in TaOx-based conducting bridge memristor devices. The TiW barrier layer avoids an excessive metal ion diffusion into the switching layer, while the TiWOx interfacial layer is formed between the barrier and the switching layer. It modulates the oxygen vacancy distribution at the top interface and contributes to the formation and rupture of the metal ion-oxygen vacancy hybrid conducting bridge. We observe that the device that relies upon non-hybrid (metal ions only) conducting bridge suffers from poor analogous performance. Meanwhile, the device made with the barrier layer is capable of providing 2-bit memory and robust 50 stable epochs. TaOx also acts as resistance for suppressing and a thermal enhancement layer, which helps to minimize overshooting current. The enhanced analog device with high linear weight update shows multilevel cell characteristics and stable 50 epochs. To validate the neuromorphic characteristic of the devices, a simulated neural network of 100 synapses is used to recognize 10 x 10 pixel images.</p>

Topics
  • impedance spectroscopy
  • Oxygen
  • interfacial
  • vacancy