Role of Al doping in the filament disruption in HfO₂resistance switches

Abstract

Resistance switching devices, whose operation is driven by formation (SET) and dissolution (RESET) of conductive paths shorting and disconnecting the two metal electrodes, have recently received great attention and a deep general comprehension of their operation has been achieved. However, the link between switching characteristics and material properties is still quite weak. In particular, doping of the switching oxide layer has often been investigated only for looking at performance upgrade and rarely for a meticulous investigation of the switching mechanism. In this paper, the impact of Al doping of HfO devices on their switching operations, retention loss mechanisms and random telegraph noise traces is investigated. In addition, phenomenological modeling of the switching operation is performed for device employing both undoped and doped HfO. We demonstrate that Al doping influences the filament disruption process during the RESET operation and, in particular, it contributes in preventing an efficient restoration of the oxide with respect to undoped devices.

Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing

“…S6 . The long-term retention of the boundary conductance levels up to 10 years at elevated temperatures has been demonstrated in similar devices by the same authors in previous works 15 , 16 .…”

“…The analysed devices present a TiN/HfO 2 /Ti/TiN structure and their operation is based on a filamentary switching mechanism, according to previous papers 16 , 43 , 44 , initiated by a forming process as described in the Methods section. Conductance increase (potentiation) and decrease (depression) dynamics is characterized by pulsed measurements in a wide space of the programming parameters varying the time width and voltage amplitude (Δ t and Δ V ) of the applied pulses while keeping constant the initial state of the memory cell.…”

“…Because of the large number of synapses compared to neurons 7 , 8 , the full potential in terms of scalability, low power and high density can be unlocked only by developing a dedicated device with the ability to dynamically change the synaptic efficacy in response to variable input conditions 9 . In this respect, memristive devices and especially resistance random access memories (RRAM) 10 , 11 have been recently identified as ideal candidates 8 , 12 , 13 because of their simple two terminal structure and promising scalability potential 14 , non volatile memory 15 , 16 and low power consumption 17 . Examples of network applications including RRAM devices have been demonstrated both in small scale arrays 18 , 19 and at simulation level to implement pattern recognition 20 – 26 and other classification tasks 27 .…”

Stochastic circuit breaker network model for bipolar resistance switching memories

“…The simplicity of the simulation allows a low computational load for each device simulation, which is not possible for complex physical phenomenological models that have been developed for describing the switching mechanisms. [24,37,38] The proposed model is benchmarked to experimental results otained on bipolar HfO 2 -based RRAM devices. In particular the simulation of the following peculiar aspects of bipolar RRAM operation will be investigated: (i) the need of a preliminary forming step that brings the device from the initial resistance to a low resistance state (LRS); (ii) the possibility of partially resetting the device to a high resistance state (HRS) with a lower resistance value than the initial pre-forming one, thanks to the application of a voltage with opposite polarity with respect to forming; (iii) the occurence of a SET transition from HRS to LRS at lower absolute value of the voltage than that of forming but same polarity; (iv) the different switching dynamics of RESET and SET, featuring, respectively, gradual LRS to HRS decreases and almost vertical HRS to LRS transitions; (v) multilevel operation through tuning of RESET stop voltage and SET compliance current.…”

An Organic Flexible Artificial Bio-Synapses with Long-Term Plasticity for Neuromorphic Computing

“…Flexible electronics are more portable and deformable in comparison with silicon-based devices [ 10 , 11 , 12 ]. However, most RRAM-based synaptic memories are composed of various inorganic materials, such as HfO x , Al 2 O 3 , and ZnO [ 13 , 14 , 15 ]. These inorganic materials usually require high temperature treatment steps with poor stretchability.…”