SET Kinetics of Ag/HfO2-Based Diffusive Memristors under Various Counter-Electrode Materials

Chekol, Solomon Amsalu; Nacke, Richard; Aussen, Stephan; Hoffmann‐Eifert, Susanne

doi:10.3390/mi14030571

Cited by 3 publications

(1 citation statement)

References 37 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This phenomenon occurs frequently in metal oxide layers, viz., ZnO, SiO 2 , ZrO 2 , and HfO 2 . [167,[182][183][184][185] Moreover, recent studies have demonstrated the growth of the conductive filament in the ZnO layer from the top electrode to the bottom electrode. [186,187]…”

Section: Cation-based Transitionmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

View full text Add to dashboard Cite

Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

show abstract

Section: Cation-based Transitionmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

View full text Add to dashboard Cite

show abstract

Self‐Healing Magnetic Field‐Assisted Threshold Switching Device Utilizing Dual Field‐Driven Filamentary Physics

Chu,

Han,

Kang

et al. 2024

Adv Elect Materials

View full text Add to dashboard Cite

Advanced filamentary devices are crucial for developing low‐power devices to implement high‐speed logic and neuromorphic devices. Among these, HfO2‐based filamentary devices have attracted attention as viable options due to their threshold‐switching characteristics and compatibility with complementary metal‐oxide‐semiconductor (CMOS) technology. However, the unpredictability of conventional filament formation/rupture driven by an electric field challenges consistency and reliability. A paradigm shift from conventional stochastic electric field‐driven ion migration to controllable ion‐based transportation is essential to devise functional low‐power devices capable of controlling the filament process. This work introduces a magnetic field‐assisted threshold switching (MA‐TS) device, which integrates a neodymium magnet and a nickel (Ni) barrier layer to enable controlled dual field‐driven ion transportation. The dual field‐driven process combining the conventional vertical electric field‐driven ion migration with lateral magnetic field‐driven ion transportation, reveals a distinctive aspect of ion movement. The MA‐TS device achieves superior performances characterized by an ultra‐low threshold voltage (≈0 V), minimized leakage current in the off‐state, a variation‐immune hysteresis‐free characteristic, enhanced yield, and revival‐ability (i.e., self‐healing) after a failed TS operation. By overcoming the limitations of conventional filamentary devices, the MA‐TS device opens up a promising avenue for efficient and stable low‐power applications.

show abstract

Unraveling the Roles of Switching and Relaxation Times in Volatile Electrochemical Memristors to Mimic Neuromorphic Dynamical Features

Dutta,

Brivio,

Spiga

2024

Adv Elect Materials

View full text Add to dashboard Cite

Computing through ensembles of interacting dynamical elements is the next frontier of the diverse field of neuromorphic computing. Spiking neural networks are one of the possible examples. Computation through dynamics and through time requires the development of novel technologies for devices with rich dynamics. Among the various candidates, the most promising ones are volatile electrochemical memristive systems that switch from high to low resistance state by voltage application and self‐recover the high resistance state after a tunable relaxation time. Such devices can perform a wide variety of computational primitives. However, a clear comprehensive picture of their possible dynamics and their physical interpretation is still missing. In the present manuscript, prototypical electrochemical silver/silicon oxide/platinum (Ag/SiOx/Pt) memristive devices are characterized to identify dynamical aspects, like integrative effects and stochastic switching. Integrative effects are evidenced both in high and low resistance states, associated to wake‐up phase and cumulative switching. All the dynamical aspects are related to characteristic switching times and relaxation times, and with reference to the electrochemical and physical processes involved in device operation. The various dynamical aspects are linked to short‐term memory effects and basic temporal processing functions based on paired‐pulse effects that are relevant for neuromorphic applications.

show abstract

SET Kinetics of Ag/HfO2-Based Diffusive Memristors under Various Counter-Electrode Materials

Cited by 3 publications

References 37 publications

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Self‐Healing Magnetic Field‐Assisted Threshold Switching Device Utilizing Dual Field‐Driven Filamentary Physics

Unraveling the Roles of Switching and Relaxation Times in Volatile Electrochemical Memristors to Mimic Neuromorphic Dynamical Features

Contact Info

Product

Resources

About