Layer‐Dependent Effects of Interfacial Phase‐Change Memory for an Artificial Synapse

Abstract

Recent research on artificial intelligence (AI) has focused on the computational performance of the human brain as a model to process large amounts of data to overcome the limits of current technology. [1,2] This is because the conventional von-Neumann architecture used to operate current AI algorithms causes a performance bottleneck between the computation required and the capacity of memory units. To achieve performance comparable to the biological brain using electronic devices, neuromorphic computing systems have been proposed. [3] This design, consisting of numerous artificial synapses that are essential for hardware advancement, has recently been demonstrated to have power-efficient computation and be extremely compact. [4] Therefore, the artificial synapse should be a simple twoterminal device to achieve brain-level (%10 15 synapses) compactness. Notably, artificial synaptic devices have low energy consumption for gradual conductance states to realize analog-like transitions, including long-term potentiation/longterm depression (LTP/LTD) and spike-timing-dependent plasticity. [5] Based on emerging nonvolatile memory technologies for realizing such characteristics, the devices are classified into phase-change synaptic devices, [6] resistive change synaptic devices, [7] and conductive-bridge synaptic devices relying on a physical switching mechanism. [8] Among them, phase-change synaptic devices have attracted attention because of their reliability and scalability down to the nanometer regime. [6,9] Ge 2 Sb 2 Te 5 alloys, a commonly used phase-change material, exhibit unique switching that creates a resistance difference between amorphous (RESET) and crystalline (SET) states by joule heating. However, a large electrical programming current pulse is needed for melting phase-change materials. Although several efforts have been made to reduce the RESET current, more energyefficient structures or materials are still needed, including stability improvements such as atomic migration, resistance drift, and phase segregation. [10] For this reason, an interfacial phase-change memory (iPCM) with a superlattice-like structure created by alternately depositing a GeTe thin film and Sb 2 Te 3 thin film was introduced by Tominaga et al. [11] The resistance difference is created by the behavior of Ge atoms in the GeTe film sandwiched by the van der Waals gap between the Sb 2 Te 3 films. Although the switching mechanism of iPCM has still not been elucidated, it operates at low energy consumption by restricting the atomic movement of Ge atoms. [12][13][14] Many approaches have been used to improve the properties of memory devices of the iPCM; however, the experimental configuration of an artificial synapse still needs to be investigated. In this article, we demonstrate the synaptic properties by fabricating different numbers of GeTe/Sb 2 Te 3 layers via sputtering.

Phase‐Change and Ovonic Materials (Third Edition)

“…This is first well illustrated through the paper of Zhou et al on the use of AIST‐based photonic memory cells as a promising platform for artificial neural networks and neuromorphic photonic computing hardware. [ 1 ] A similar goal has also motivated the work presented by Kang et al [ 2 ] showing that van der Waals‐layered [(GeTe) 1nm /(Sb 2 Te 3 ) 4nm ] n =4 and 8 super‐lattices, also commonly called iPCM devices, can act as synaptic devices featuring gradual and symmetric conductance update characteristics and can implement multilevel characteristics with quite stable operation. Thus, these results show the potential of iPCMs for applications in neuromorphic computing with high accuracy.…”

Characterization of undoped and N–Ti codoped Zn5Sb3Te chalcogenides

“…[5][6][7][8][9][10][11] Since there is an increasing need for processing information, researchers have extended this promising technology to the field of neuromorphic computing because of the perfect performance of the phase-change memory, such as multilevel possibility, large high/low resistance ratio, nonvolatility, and high reliability. [12][13][14][15][16][17][18][19][20] Among these technologies, phase change materials are undoubtedly the most important parameters to realize the functions to store or process information. Conventionally, GeSbTe-based alloys, such as Ge 2 Sb 2 Te 5 , are one of the chalcogenides widely used in phase-change memory or synapse.…”