Abstract:We present an in silico study of the neuromorphic-computing behavior of the prototypical phase-change material, Ge2Sb2Te5, using ab initio molecular-dynamics simulations. Stepwise changes in structural order in response to temperature pulses of varying length and duration are observed, and a good reproduction of the spike-timing-dependent plasticity observed in nanoelectronic synapses is demonstrated. Short above-melting pulses lead to instantaneous loss of structural and chemical order, followed by delayed pa… Show more
“…These applications utilize the dynamic and accumulation effects of crystallization or amorphization processes . On the other hand, many artificial neuromorphic systems also have been demonstrated by PCM . Most recently, Tuma et al at IBM created an artificial neuron using PCM devices (Figure h,i) .…”
To meet the requirement of big data era and neuromorphic computations, nonvolatile memory with fast speed, high density, and low power consumption is urgently needed. As an emerging technology, phase-change memory is a promising candidate to solve this problem. However, the drawback of the high power consumption hinders their applications. Most recently, a new phase-change material of [(GeTe) x /(Sb 2 Te 3 ) y ] n superlattice attracts intensive attentions owing to its ultralow power consumption comparing with conventional phase-change memory devices. Many studies on this new material have been reported. However, there still lacks a comprehensive and unified understanding of its atomic picture and working mechanism. This article at first summarizes the broad applications for phase-change materials. Then, the major progresses of phase-change superlattices to understand the microscopic structure and working principles for data storage are discussed. Strategies on material optimizations to further enhance device performances are proposed. Finally, an outlook on new applications with these advanced superlattice materials is suggested.
“…These applications utilize the dynamic and accumulation effects of crystallization or amorphization processes . On the other hand, many artificial neuromorphic systems also have been demonstrated by PCM . Most recently, Tuma et al at IBM created an artificial neuron using PCM devices (Figure h,i) .…”
To meet the requirement of big data era and neuromorphic computations, nonvolatile memory with fast speed, high density, and low power consumption is urgently needed. As an emerging technology, phase-change memory is a promising candidate to solve this problem. However, the drawback of the high power consumption hinders their applications. Most recently, a new phase-change material of [(GeTe) x /(Sb 2 Te 3 ) y ] n superlattice attracts intensive attentions owing to its ultralow power consumption comparing with conventional phase-change memory devices. Many studies on this new material have been reported. However, there still lacks a comprehensive and unified understanding of its atomic picture and working mechanism. This article at first summarizes the broad applications for phase-change materials. Then, the major progresses of phase-change superlattices to understand the microscopic structure and working principles for data storage are discussed. Strategies on material optimizations to further enhance device performances are proposed. Finally, an outlook on new applications with these advanced superlattice materials is suggested.
“…Differing from aforementioned models, the response of PCM-based synapse to the time delay between pre-post spikes has recently been re-examined by an ab initio molecular-dynamics (AIMD) model that consists of 180 atom models created in cubic supercells with periodic boundary conditions at a fixed density of 6110 kg/m 3 [95]. PAW pseudopotentials are deployed in this case to handle the outer s and p electrons as valence electrons [96], associated with the Perdew-Burke-Enzerhof (PBE) exchange-correlation functional and a plane-wave kinetic-energy cutoff of 175 eV [97].…”
Section: Reviewmentioning
confidence: 99%
“…e Change in the number of fourfold rings at the end of the sequence for LTP simulation. Reprinted with permission from [95]Fig. 14
a Effect of a sequence of 50 ps heating pulses with gradually increased magnitudes ( top ) on the number of fourfold rings ( bottom ).…”
Section: Reviewmentioning
confidence: 99%
“…Atoms forming parts of fourfold rings are coloured purple , and the colour coding of the other atoms is as follows: Ge, blue ; Sb, red ; Te, green . Reprinted with permission from [95]
…”
Section: Reviewmentioning
confidence: 99%
“…e Change in the number of fourfold rings at the end of the sequence for LTP simulation. Reprinted with permission from [95]…”
Realization of brain-like computer has always been human’s ultimate dream. Today, the possibility of having this dream come true has been significantly boosted due to the advent of several emerging non-volatile memory devices. Within these innovative technologies, phase-change memory device has been commonly regarded as the most promising candidate to imitate the biological brain, owing to its excellent scalability, fast switching speed, and low energy consumption. In this context, a detailed review concerning the physical principles of the neuromorphic circuit using phase-change materials as well as a comprehensive introduction of the currently available phase-change neuromorphic prototypes becomes imperative for scientists to continuously progress the technology of artificial neural networks. In this paper, we first present the biological mechanism of human brain, followed by a brief discussion about physical properties of phase-change materials that recently receive a widespread application on non-volatile memory field. We then survey recent research on different types of neuromorphic circuits using phase-change materials in terms of their respective geometrical architecture and physical schemes to reproduce the biological events of human brain, in particular for spike-time-dependent plasticity. The relevant virtues and limitations of these devices are also evaluated. Finally, the future prospect of the neuromorphic circuit based on phase-change technologies is envisioned.
Traditional von Neumann computing architecture with separated computation and storage units has already impeded the data processing performance and energy efficiency, calling for emerging neuromorphic electronic and optical devices and systems which can mimic the human brain to shift this paradigm. Material-level innovation has become the key component to this revolution of information technology. Chalcogenide phase-change material (PCM) as a well-acknowledged data-storage medium is a promising candidate to tackle this challenge. In this review, the use of PCMs to implement artificial neurons and synapses from both the electronic and optical respects is discussed, and in particular, the structure-property physics and transition dynamics that enable such brain-inspired and in-memory computing applications are emphasized. Recent advances on the atomic-level amorphous and crystalline structures, transition mechanisms, materials optimization and design, neural and synaptic devices, brain-inspired chips, and computing systems, as well as the future opportunities of PCMs, are summarized and discussed.
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