Probing resistive switching in HfO2/Al2O3 bilayer oxides using in-situ transmission electron microscopy

In this work, we investigate the resistive switching in hafnium dioxide (HfO2) and aluminum oxide (Al2O3) bilayered stacks using in-situ transmission electron microscopy and X-ray energy dispersive spectroscopy. Conductance of the HfO2/Al2O3 stack changes gradually upon electrical stressing which is related to the formation of extended nanoscale physical defects at the HfO2/Al2O3 interface and the migration and re-crystallization of Al into the oxide bulk. The results suggest two competing physical mechanisms including the redistribution of oxygen ions and the migration of Al species from the Al electrode during the switching process. While the HfO2/Al2O3 bilayered stack appears to be a good candidate for RRAM technology, the low diffusion barrier of the active Al electrode causes severe Al migration in the bi-layered oxides leading to the device to fail in resetting, and thereby, largely limiting the overall switching performance and material reliability

Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices

Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells

Reliability Challenges with Materials for Analog Computing

Specialized hardware for deep learning using analog memory devices has the potential to outperform conventional GPUs by a large margin. At the core of such hardware are arrays of non-volatile-memory (NVM) devices that can perform the simple matrix operations needed for deep learning in parallel and in constant time. Several implementations can be found in the literature that use different materials as memory elements, including phase-change-memory (PCM), resistive-random-access-memory (RRAM), electrochemical-random-access-memory (ECRAM), and ferroelectric devices. While the current focus is to demonstrate functionality, there is an increasing concern about the reliability margins of this emerging technology. In this paper we will briefly describe operation and device requirements, and then focus on possible reliability exposure in terms of variability, stability and drift, retention and durability.1