Programming Pulse Width Assessment for Reliable and Low-Energy Endurance Performance in Al:HfO2-Based RRAM Arrays

Pérez, Eduardo; Ossorio, Óscar G.; Dueñas, S.; Castán, Helena; García, Héctor; Wenger, Christian

doi:10.3390/electronics9050864

Cited by 28 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MIM structures have an area of about 0.4 µm 2 . The endurance and retention of these devices has been studied in order to show the viability of this technology both for memory and neuromorphic applications [11,33,34]. The algorithms employed to program the conductive levels on the devices are writeverify approaches.…”

Section: Device Fabrication and Measurement Set-up A Multilevel Approachmentioning

confidence: 99%

Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

et al. 2021

Self Cite

View full text Add to dashboard Cite

A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including HfO2 dielectrics was employed, accounting for different multilevel schemes and the corresponding conductance quantization algorithms. The accuracy of the image recognition processes was studied in depth. This type of studies are essential prior to hardware implementation of neural networks. The obtained results support the use of CNNs for image domains. This is linked to the role played by convolutional layers at extracting image features and reducing the data complexity. In this case, the number of synaptic weights can be reduced in comparison to MLPs.

show abstract

Section: Device Fabrication and Measurement Set-up A Multilevel Approachmentioning

confidence: 99%

Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here, a pulse width of 1 µs was chosen. The pulse width has an influence on the necessary switching voltage as well as cell variability [19]. A pulse width of 1 µs is a compromise between programming speed and the necessary pulse height for programming.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Versatile, Voltage-Pulse Based Read and Programming Circuit for Multi-Level RRAM Cells

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this work, we present an integrated read and programming circuit for Resistive Random Access Memory (RRAM) cells. Since there are a lot of different RRAM technologies in research and the process variations of this new memory technology often spread over a wide range of electrical properties, the proposed circuit focuses on versatility in order to be adaptable to different cell properties. The circuit is suitable for both read and programming operations based on voltage pulses of flexible length and height. The implemented read method is based on evaluating the voltage drop over a measurement resistor and can distinguish up to eight different states, which are coded in binary, thereby realizing a digitization of the analog memory value. The circuit was fabricated in the 130 nm CMOS process line of IHP. The simulations were done using a physics-based, multi-level RRAM model. The measurement results prove the functionality of the read circuit and the programming system and demonstrate that the read system can distinguish up to eight different states with an overall resistance ratio of 7.9.

show abstract

“…Basically, low-resistance state (LRS) and high-resistance state (HRS) can be converted according to the applied voltage, and the state, once stored, has a nonvolatile characteristic that is maintained over time [4][5][6][7]. Among a lot of materials, metal oxides like TiO 2 , HfO 2 and Ta 2 O 5 are the most popular resistive switching materials due to their superior memory device behaviors, such as endurance, stability, repeatability, and reproducibility [8][9][10][11][12][13][14][15]. Regarding the resistive change switching effect of metal oxide-based RRAM devices, various mechanisms are known, depending on the resistive switching materials and the electrodes [16].…”

Section: Introductionmentioning

confidence: 99%

Improved Synaptic Device Properties of HfAlOx Dielectric on Highly Doped Silicon Substrate by Partial Reset Process

Kim¹,

Kwon²,

Ryu³

et al. 2021

Metals

View full text Add to dashboard Cite

This work demonstrates the synaptic properties of the alloy-type resistive random-access memory (RRAM). We fabricated the HfAlOx-based RRAM for a synaptic device in a neuromorphic system. The deposition of the HfAlOx film on the silicon substrate was verified by X-ray photoelectron spectroscopy (XPS) analysis. It was found that both abrupt and gradual resistive switching could be implemented, depending on the reset stop voltage. In the reset process, the current gradually decreased at weak voltage, and at strong voltage, it tended to decrease rapidly by Joule heating. The type of switching determined by the first reset process was subsequently demonstrated to be stable switching by successive set and reset processes. A gradual switching type has a much smaller on/off window than abrupt switching. In addition, retention maintained stability up to 2000 s in both switching cases. Next, the multiple current states were tested in the gradual switching case by identical pulses. Finally, we demonstrated the potentiation and depression of the Cu/HfAlOx/Si device as a synapse in an artificial neural network and confirmed that gradual resistive switching was suitable for artificial synapses, using neuromorphic system simulation.

show abstract

Programming Pulse Width Assessment for Reliable and Low-Energy Endurance Performance in Al:HfO2-Based RRAM Arrays

Cited by 28 publications

References 27 publications

Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

A Versatile, Voltage-Pulse Based Read and Programming Circuit for Multi-Level RRAM Cells

Improved Synaptic Device Properties of HfAlOx Dielectric on Highly Doped Silicon Substrate by Partial Reset Process

Contact Info

Product

Resources

About