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Author: Wenger, Christian × End date: 2024 × Start date: 2020 × DDC: 530 × Publisher: Basel : MDPI ×

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    Modulating the Filamentary-Based Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3 Layers
    (Basel : MDPI, 2022) Kalishettyhalli Mahadevaiah, Mamathamba; Perez, Eduardo; Lisker, Marco; Schubert, Markus Andreas; Perez-Bosch Quesada, Emilio; Wenger, Christian; Mai, Andreas
    The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2 O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I–V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit.
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    Optimization of Multi-Level Operation in RRAM Arrays for In-Memory Computing
    (Basel : MDPI, 2021) Pérez, Eduardo; Pérez-Ávila, Antonio Javier; Romero-Zaliz, Rocío; Mahadevaiah, Mamathamba Kalishettyhalli; Pérez-Bosch Quesada, Emilio; Roldán, Juan Bautista; Jiménez-Molinos, Francisco; Wenger, Christian
    Accomplishing multi-level programming in resistive random access memory (RRAM) arrays with truly discrete and linearly spaced conductive levels is crucial in order to implement synaptic weights in hardware-based neuromorphic systems. In this paper, we implemented this feature on 4-kbit 1T1R RRAM arrays by tuning the programming parameters of the multi-level incremental step pulse with verify algorithm (M-ISPVA). The optimized set of parameters was assessed by comparing its results with a non-optimized one. The optimized set of parameters proved to be an effective way to define non-overlapped conductive levels due to the strong reduction of the device-to-device variability as well as of the cycle-to-cycle variability, assessed by inter-levels switching tests and during 1 k reset-set cycles. In order to evaluate this improvement in real scenarios, the experimental characteristics of the RRAM devices were captured by means of a behavioral model, which was used to simulate two different neuromorphic systems: an 8 × 8 vector-matrix-multiplication (VMM) accelerator and a 4-layer feedforward neural network for MNIST database recognition. The results clearly showed that the optimization of the programming parameters improved both the precision of VMM results as well as the recognition accuracy of the neural network in about 6% compared with the use of non-optimized parameters.