Filters

2019 - 20202

More filters

2021 - 20222
Reset filters

Settings

Author: Wenger, Christian × Start date: 2010 × DDC: 600 × Type: Text × Subject: RRAM ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Operando diagnostic detection of interfacial oxygen ‘breathing’ of resistive random access memory by bulk-sensitive hard X-ray photoelectron spectroscopy
    (London [u.a.] : Taylor & Francis, 2019) Niu, Gang; Calka, Pauline; Huang, Peng; Sharath, Sankaramangalam Ulhas; Petzold, Stefan; Gloskovskii, Andrei; Fröhlich, Karol; Zhao, Yudi; Kan, Jinfeng; Schubert, Markus Andreas; Bärwolf, Florian; Ren, Wei; Ye, Zuo-Guang; Perez, Eduardo; Wenger, Christian; Alff, Lambert; Schroeder, Thomas
    The HfO2-based resistive random access memory (RRAM) is one of the most promising candidates for non-volatile memory applications. The detection and examination of the dynamic behavior of oxygen ions/vacancies are crucial to deeply understand the microscopic physical nature of the resistive switching (RS) behavior. By using synchrotron radiation based, non-destructive and bulk-sensitive hard X-ray photoelectron spectroscopy (HAXPES), we demonstrate an operando diagnostic detection of the oxygen ‘breathing’ behavior at the oxide/metal interface, namely, oxygen migration between HfO2 and TiN during different RS periods. The results highlight the significance of oxide/metal interfaces in RRAM, even in filament-type devices. IMPACT STATEMENT: The oxygen ‘breathing’ behavior at the oxide/metal interface of filament-type resistive random access memory devices is operandoly detected using hard X-ray photoelectron spectroscopy as a diagnostic tool. © 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
  • Thumbnail Image
    Item
    Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zarrin, Pouya Soltani; Zahari, Finn; Mahadevaiah, Mamathamba K.; Perez, Eduardo; Kohlstedt, Hermann; Wenger, Christian
    Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s).