scholarly journals TCAD Modeling of Resistive-Switching of HfO2 Memristors: Efficient Device-Circuit Co-Design for Neuromorphic Systems

2021 ◽  
Vol 3 ◽  
Author(s):  
Andre Zeumault ◽  
Shamiul Alam ◽  
Zack Wood ◽  
Ryan J. Weiss ◽  
Ahmedullah Aziz ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Oxygen Vacancies ◽  
Temporal Dynamics ◽  
Electronic Device ◽  
Dynamic State ◽  
Recent Experimental Data ◽  
Long Term Memory ◽  
Strong Electron ◽  
Variable Weight ◽  
Tcad Sentaurus

In neuromorphic computing, memristors (or “memory resistors”) have been primarily studied as key elements in artificial synapse implementations, where the memristor provides a variable weight with intrinsic long-term memory capabilities, based on its modifiable resistive-switching characteristics. Here, we demonstrate an efficient methodology for simulating resistive-switching of HfO2 memristors within Synopsys TCAD Sentaurus—a well established, versatile framework for electronic device simulation, visualization and modeling. Kinetic Monte Carlo is used to model the temporal dynamics of filament formation and rupture wherein additional band-to-trap electronic transitions are included to account for polaronic effects due to strong electron-lattice coupling in HfO2. The conductive filament is modeled as oxygen vacancies which behave as electron traps as opposed to ionized donors, consistent with recent experimental data showing p-type conductivity in HfOx films having high oxygen vacancy concentrations and ab-initio calculations showing the increased thermodynamic stability of neutral and charged oxygen vacancies under conditions of electron injection. Pulsed IV characteristics are obtained by inputting the dynamic state of the system—which consists of oxygen ions, unoccupied oxygen vacancies, and occupied oxygen vacancies at various positions—into Synopsis TCAD Sentaurus for quasi-static simulations. This allows direct visualization of filament electrostatics as well as the implementation of a nonlocal, trap-assisted-tunneling model to estimate current-voltage characteristics during switching. The model utilizes effective masses and work functions of the top and bottom electrodes as additional parameters influencing filament dynamics. Together, this approach can be used to provide valuable device- and circuit-level insight, such as forming voltage, resistance levels and success rates of programming operations, as we demonstrate.

scholarly journals Narrowing the band gap to enhance the resistive switching properties of Pr3+-doped ZnO thin films by Cd-ion doping

RSC Advances ◽  
2017 ◽  
Vol 7 (61) ◽  
pp. 38757-38764 ◽  
Author(s):  
Shuai He ◽  
Aize Hao ◽  
Ni Qin ◽  
Dinghua Bao
Keyword(s):  
Thin Films ◽  
Band Gap ◽  
Resistive Switching ◽  
Oxygen Vacancies ◽  
Zno Thin Films ◽  
Switching Performance ◽  
Ion Doping ◽  
Doped Zno

The resistive switching performance of ZnO thin films can be enhanced by decreasing the band gap and controlling oxygen vacancies.

Role of oxygen vacancies for resistive switching in noble metal sandwiched Pr0.67Ca0.33MnO3-δ

2017 ◽  
Vol 110 (24) ◽  
pp. 243502 ◽  
Author(s):  
Thilo Kramer ◽  
Malte Scherff ◽  
Daniel Mierwaldt ◽  
Joerg Hoffmann ◽  
Christian Jooss
Keyword(s):  
Noble Metal ◽  
Resistive Switching ◽  
Oxygen Vacancies

scholarly journals The role of oxygen vacancies on resistive switching properties of oxide materials

2020 ◽  
Vol 7 (5) ◽  
pp. 665-683
Author(s):  
Hang Meng ◽  
◽  
Shihao Huang ◽  
Yifeng Jiang
Keyword(s):  
Resistive Switching ◽  
Oxygen Vacancies ◽  
Oxide Materials

scholarly journals The role of oxygen vacancies in the high cycling endurance and quantum conductance in BiVO 4 ‐based resistive switching memory

InfoMat ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 960-967 ◽  
Author(s):  
Mengting Zhao ◽  
Xiaobing Yan ◽  
Long Ren ◽  
Mengliu Zhao ◽  
Fei Guo ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Oxygen Vacancies ◽  
Quantum Conductance ◽  
Resistive Switching Memory ◽  
Switching Memory

scholarly journals Enhancing Short-Term Plasticity by Inserting a Thin TiO2 Layer in WOx-Based Resistive Switching Memory

Coatings ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 908 ◽  
Author(s):  
Hyojong Cho ◽  
Sungjun Kim
Keyword(s):  
Photoelectron Spectroscopy ◽  
Oxygen Vacancies ◽  
Short Term Memory ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
X Ray ◽  
Short Term Plasticity ◽  
Good Retention

In this work, we emulate biological synaptic properties such as long-term plasticity (LTP) and short-term plasticity (STP) in an artificial synaptic device with a TiN/TiO2/WOx/Pt structure. The graded WOx layer with oxygen vacancies is confirmed via X-ray photoelectron spectroscopy (XPS) analysis. The control TiN/WOx/Pt device shows filamentary switching with abrupt set and gradual reset processes in DC sweep mode. The TiN/WOx/Pt device is vulnerable to set stuck because of negative set behavior, as verified by both DC sweep and pulse modes. The TiN/WOx/Pt device has good retention and can mimic long-term memory (LTM), including potentiation and depression, given repeated pulses. On the other hand, TiN/TiO2/WOx/Pt devices show non-filamentary type switching that is suitable for fine conductance modulation. Potentiation and depression are demonstrated in the TiN/TiO2 (2 nm)/WOx/Pt device with moderate conductance decay by application of identical repeated pulses. Short-term memory (STM) is demonstrated by varying the interval time of pulse inputs for the TiN/TiO2 (6 nm)/WOx/Pt device with a quick decay in conductance.

scholarly journals Tracking Your Mind's Eye during Recollection: Decoding the Long-Term Recall of Short Audiovisual Clips

2020 ◽  
Vol 32 (1) ◽  
pp. 50-64
Author(s):  
Christelle Larzabal ◽  
Nadège Bacon-Macé ◽  
Sophie Muratot ◽  
Simon J. Thorpe
Keyword(s):  
Temporal Dynamics ◽  
Long Term Memory ◽  
Memory Representation ◽  
Gradual Loss ◽  
Neural Processes ◽  
Consolidation Model ◽  
Retention Intervals ◽  
Multivariate Pattern ◽  
Mind's Eye

Unlike familiarity, recollection involves the ability to reconstruct mentally previous events that results in a strong sense of reliving. According to the reinstatement hypothesis, this specific feature emerges from the reactivation of cortical patterns involved during information exposure. Over time, the retrieval of specific details becomes more difficult, and memories become increasingly supported by familiarity judgments. The multiple trace theory (MTT) explains the gradual loss of episodic details by a transformation in the memory representation, a view that is not shared by the standard consolidation model. In this study, we tested the MTT in light of the reinstatement hypothesis. The temporal dynamics of mental imagery from long-term memory were investigated and tracked over the passage of time. Participant EEG activity was recorded during the recall of short audiovisual clips that had been watched 3 weeks, 1 day, or a few hours beforehand. The recall of the audiovisual clips was assessed using a Remember/Know/New procedure, and snapshots of clips were used as recall cues. The decoding matrices obtained from the multivariate pattern analyses revealed sustained patterns that occurred at long latencies (>500 msec poststimulus onset) that faded away over the retention intervals and that emerged from the same neural processes. Overall, our data provide further evidence toward the MTT and give new insights into the exploration of our “mind's eye.”

scholarly journals Resistive switching behavior in α-In2Se3 nanoflakes modulated by ferroelectric polarization and interface defects

RSC Advances ◽  
2019 ◽  
Vol 9 (52) ◽  
pp. 30565-30569 ◽  
Author(s):  
Pengfei Hou ◽  
Siwei Xing ◽  
Xin Liu ◽  
Cheng Chen ◽  
Xiangli Zhong ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Oxygen Vacancies ◽  
Switching Behavior ◽  
Ferroelectric Polarization ◽  
Resistive Switching Behavior ◽  
Free Carriers ◽  
Interface Defects ◽  
Out Of Plane ◽  
Planar Device

A planar device based on an α-In2Se3 nanoflake, in which the in-plane/out-of-plane polarization, free carriers and oxygen vacancies in SiO2 contribute to the resistive switching behavior of the device.

Balancing the Source and Sink of Oxygen Vacancies for the Resistive Switching Memory

2018 ◽  
Vol 10 (25) ◽  
pp. 21445-21450 ◽  
Author(s):  
Tae Hyung Park ◽  
Young Jae Kwon ◽  
Hae Jin Kim ◽  
Hyo Cheon Woo ◽  
Gil Seop Kim ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Oxygen Vacancies ◽  
Resistive Switching Memory ◽  
Source And Sink ◽  
Switching Memory

scholarly journals On the effects of memory and topology on the controllability of complex dynamical networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Panagiotis Kyriakis ◽  
Sérgio Pequito ◽  
Paul Bogdan
Keyword(s):  
Complex Networks ◽  
Temporal Dynamics ◽  
Complex Dynamical Networks ◽  
Long Term Memory ◽  
Topological Properties ◽  
Control Effort ◽  
Dynamical Networks ◽  
Term Memory ◽  
Minimum Number

Abstract Recent advances in network science, control theory, and fractional calculus provide us with mathematical tools necessary for modeling and controlling complex dynamical networks (CDNs) that exhibit long-term memory. Selecting the minimum number of driven nodes such that the network is steered to a prescribed state is a key problem to guarantee that complex networks have a desirable behavior. Therefore, in this paper, we study the effects of long-term memory and of the topological properties on the minimum number of driven nodes and the required control energy. To this end, we introduce Gramian-based methods for optimal driven node selection for complex dynamical networks with long-term memory and by leveraging the structure of the cost function, we design a greedy algorithm to obtain near-optimal approximations in a computationally efficiently manner. We investigate how the memory and topological properties influence the control effort by considering Erdős–Rényi, Barabási–Albert and Watts–Strogatz networks whose temporal dynamics follow a fractional order state equation. We provide evidence that scale-free and small-world networks are easier to control in terms of both the number of required actuators and the average control energy. Additionally, we show how our method could be applied to control complex networks originating from the human brain and we discover that certain brain cortex regions have a stronger impact on the controllability of network than others.

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