Composition-Controlled Atomic Layer Deposition of Phase-Change Memories and Ovonic Threshold Switches with High Performance.

Chalcogenide compounds are the main characters in a revolution in electronic memories. These materials are used to produce ultrafast ovonic threshold switches (OTSs) with good selectivity and moderate leakage current and phase-change memories (PCMs) with excellent endurance and short read/write times when compared with state-of-the-art flash-NANDs. The combination of these two electrical elements is used to fabricate nonvolatile memory arrays with a write/access time orders of magnitude shorter than that of state-of-the-art flash-NANDs. These devices have a pivotal role for the advancement of fields such as artificial intelligence, machine learning, and big-data. Chalcogenide films, at the moment, are deposited by using physical vapor deposition (PVD) techniques that allow for fine control over the stoichiometry of solid solutions but fail in providing the conformality required for developing large-memory-capacity integrated 3D structures. Here we present conformal ALD chalcogenide films with control over the composition of germanium, antimony, and tellurium (GST). By developing a technique to grow elemental Te we demonstrate the ability to deposit conformal, smooth, composition-controlled GST films. We present a thorough physical and chemical characterization of the solids and an in-depth electrical test. We demonstrate the ability to produce both OTS and PCM materials. GeTe4 OTSs exhibit fast switching times of ∼13 ns. Ge2Sb2Te5 ALD PCMs exhibit a wide memory window exceeding two orders of magnitude, short write times (∼100 ns), and a reset current density as low as ∼107 A/cm2-performance matching or improving upon state-of-the-art PVD PCM devices.

A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems.

Resistive (or memristive) switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses) with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP) is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i) the natural variability of set/reset processes in the nanoscale switch, and (ii) the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns.