Fabrication of a Cu‐Cone‐Shaped Cation Source Inserted Conductive Bridge Random Access Memory and Its Improved Switching Reliability

Banerjee

et al. 2021

Adv Elect Materials

Electrochemical metallization cell–based threshold switching (TS) devices are promising candidates for selectors in high‐density cross‐point memory arrays. However, TS characteristics in density‐ and stoichiometry‐engineered solid electrolyte systems have not been studied. By adopting TS‐based stoichiometric and substoichiometric solid electrolyte HfO2 layers, the localized atomic scale movement of Ag ions can be effectively controlled in ultrathin bilayers. The stoichiometric HfO2 thickness is crucial to this. This study proposes defect‐ and density‐engineered bilayer TS with a 0.5 nm critical thickness of the stoichiometric HfO2 layer, which maximizes various switching characteristics. The unstable filament in the ultrathin stoichiometric HfO2 layer prevents the formation of stable Ag clusters owing to limited Ag injection into the dense and stoichiometric HfO2 layer. In addition, the substoichiometric HfO1.91 (1.5 nm) buffer layer prevents direct injection of Ag ions from the top electrode into the dense HfO2 layer. These factors enable the bilayer design to achieve a high turn‐off speed of 100 ns, excellent endurance above 107 cycles, low off current of ≈pA, and tight Vth distributions even for sub‐2 nm devices. These exceptional results demonstrate the possibility of designing density‐graded bilayer TS devices with high stability, fast switching, and high endurance.

Section: Resultsmentioning

confidence: 89%

Improved Threshold Switching and Endurance Characteristics Using Controlled Atomic‐Scale Switching in a 0.5 nm Thick Stoichiometric HfO₂Layer

Banerjee

et al. 2021

Adv Elect Materials

“…From the microscopic point of view, the stochasticity in the formation of the CF induces the nonuniformity in . In this regard, many efforts have been made to reduce this nonuniformity: Li et al showed that utilizing copper ions over silver ions improves the uniformity in CB-RAM because of the faster diffusion of the former 18 , whereas Kim et al used a cone-shaped Cu-ion source in CB-RAM 19 . Niu et al engineered field crowding using a narrow bottom electrode, whose edge forms a field-crowding location and in turn a CF growth site, to improve the stability of a HfO 2 ReRAM 20 .…”

Section: Resultsmentioning

confidence: 99%

Investigation of switching uniformity in resistive memory via finite element simulation of conductive-filament formation

Min

Jung

Kwon

2021

Sci Rep

Herein, we present simulations of conductive filament formation in resistive random-access memory using a finite element solver. We consider the switching material, which is typically an oxide, as a two-phase material comprising low- and high-resistance phases. The low-resistance phase corresponds to a defective and conducting region with a high anion vacancy concentration, whereas the high-resistance phase corresponds to a non-defective and insulating region with a low anion-vacancy concentration. We adopt a phase variable corresponding to 0 and 1 in the insulating and conducting phases, respectively, and we change the phase variable suitably when new defects are introduced during voltage ramp-up for forming. Initially, some defects are embedded in the switching material. When the applied voltage is ramped up, the phase variable changes from 0 to 1 at locations wherein the electric field exceeds a critical value, which corresponds to the introduction of new defects via vacancy generation. The applied voltage at which the defects percolate to form a filament is considered as the forming voltage. Here, we study the forming-voltage uniformity using simulations, and we find that for typical planar-electrode devices, the forming voltage varies significantly owing to the stochastic location of the initial defects at which the electric field is “crowded.” On the other hand, a protruding electrode can improve the switching uniformity drastically via facilitating the deterministic location of electric-field crowding, which also supported by the reported experimental results.

Advanced Intelligent Systems

“…It is found that constructing metal pyramidal electrode, nanocone electrode, or inserting metal nanodots can concentrate the electric field in a confined region and thus guide the growth of CFs. [ 17,101,119–121 ] As shown in Figure 4d, the electric field is highly concentrated between the tip end of the Ag nanocones and SiO 2 , which serves to guide the formation of CFs. [ 17 ] In addition to constructing different structures in the memristor, the uniform resistive switching parameters can also be achieved by maximizing the uniformity of resistive materials.…”

Section: Key Requirements and Progress For Large‐scale Memristor Cbasmentioning

confidence: 99%

Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays

Ang

2020

124

Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to overcome the intrinsic energy and speed issues of traditional von Neumann based computing architecture. With the ability to perform vector‐matrix multiplications and flexible tunable conductance, the memristor crossbar array (CBA) structure is one of the most promising candidates to realize neural cognitive systems. The boom in the development of memristive synapses and neurons has propelled the developments of artificial neural networks (ANNs) to emulate the highly hierarchically organized network of human brain in the past decade. To achieve this, realizing large scale, high‐density memristive CBAs is a prerequisite to constructing complex ANNs. Herein, the stringent requirements in device performance and array parameters for hardware ANNs are analyzed, and the efforts in addressing the associated challenges are discussed. Recent progress on the experimental demonstration of neuromorphic computing systems (NCSs) is presented. Recommendations for further performance optimization at the device, circuit, and algorithm levels are proposed. This Report serves as a guide for the hardware implementation of NCS based on large‐scale CBAs.