Manuel Le Gallo

Stochastic phase-change neurons

Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited. Here, we show that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase-change device. By exploiting the physics of reversible amorphous-to-crystal phase transitions, we show that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale. Moreover, we show that this is inherently stochastic because of the melt-quench-induced reconfiguration of the atomic structure occurring when the neuron is reset. We demonstrate the use of these phase-change neurons, and their populations, in the detection of temporal correlations in parallel data streams and in sub-Nyquist representation of high-bandwidth signals.

Crystal growth within a phase change memory cell

In spite of the prominent role played by phase change materials in information technology, a detailed understanding of the central property of such materials, namely the phase change mechanism, is still lacking mostly because of difficulties associated with experimental measurements. Here, we measure the crystal growth velocity of a phase change material at both the nanometre length and the nanosecond timescale using phase-change memory cells. The material is studied in the technologically relevant melt-quenched phase and directly in the environment in which the phase change material is going to be used in the application. We present a consistent description of the temperature dependence of the crystal growth velocity in the glass and the super-cooled liquid up to the melting temperature.

Neuromorphic Computing Using Non-Volatile Memory

Abstract Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and ‘Memcomputing’. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix–vector multiplication needed for algorithms such as backpropagation in an analog yet massively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices – including phase change memory, conductive-bridging RAM, filamentary and non-filamentary RRAM, and other NVMs – have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability. Graphical Abstract

Evidence for thermally assisted threshold switching behavior in nanoscale phase-change memory cells

In spite of decades of research, the details of electrical transport in phase-change materials are still debated. In particular, the so-called threshold switching phenomenon that allows the current density to increase steeply when a sufficiently high voltage is applied is still not well understood, even though there is wide consensus that threshold switching is solely of electronic origin. However, the high thermal efficiency and fast thermal dynamics associated with nanoscale phase-change memory (PCM) devices motivate us to reassess a thermally assisted threshold switching mechanism, at least in these devices. The time/temperature dependence of the threshold switching voltage and current in doped Ge2Sb2Te5 nanoscale PCM cells was measured over 6 decades in time at temperatures ranging from 40 °C to 160 °C. We observe a nearly constant threshold switching power across this wide range of operating conditions. We also measured the transient dynamics associated with threshold switching as a function of the a...

Temporal correlation detection using computational phase-change memory

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.New computing paradigms, such as in-memory computing, are expected to overcome the limitations of conventional computing approaches. Sebastian et al. report a large-scale demonstration of computational phase change memory (PCM) by performing high-level computational primitives using one million PCM devices.

Detecting Correlations Using Phase-Change Neurons and Synapses

As the conventional von Neumann-based computational architectures reach their scalability and performance limits, alternative computational frameworks inspired by biological neuronal networks hold promise to revolutionize the way we process information. Here, we present a bioinspired computational primitive that utilizes an artificial spiking neuron equipped with plastic synapses to detect temporal correlations in data streams in an unsupervised manner. We demonstrate that the internal states of the neuron and of the synapses can be efficiently stored in nanoscale phase-change memory devices and show computations with collocated storage in an experimental setting.

A collective relaxation model for resistance drift in phase change memory cells

Phase change memory (PCM) cells rely on the orders of magnitude difference in resistivity between the crystalline and amorphous phases to store information. However, the temporal evolution of the resistance of the amorphous phase, commonly referred to as resistance drift, is a key challenge for the realization of multi-level PCM. In this article, we present a comprehensive description of the time-temperature dependence of the resistance variation in a PCM cell. Our model consists of a structural relaxation model and an electrical transport model. The structural relaxation model is based on the idea that the atomic configuration of the melt-quenched amorphous phase as a whole collectively relaxes towards a more favorable equilibrium state. Experimental results obtained over a wide range of temperatures and times show remarkable agreement with the proposed model.

Subthreshold electrical transport in amorphous phase-change materials

Chalcogenide-based phase-change materials play a prominent role in information technology. In spite of decades of research, the details of electrical transport in these materials are still debated. In this article, we present a unified model based on multiple-trapping transport together with 3D Poole–Frenkel emission from a two-center Coulomb potential. With this model, we are able to explain electrical transport both in as-deposited phase-change material thin films, similar to experimental conditions in early work dating back to the 1970s, and in melt-quenched phase-change materials in nanometer-scale phase-change memory devices typically used in recent studies. Experimental measurements on two widely different device platforms show remarkable agreement with the proposed mechanism over a wide range of temperatures and electric fields. In addition, the proposed model is able to seamlessly capture the temporal evolution of the transport properties of the melt-quenched phase upon structural relaxation.

Mixed-precision in-memory computing

As complementary metal–oxide–semiconductor (CMOS) scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to extend the performance of today’s computers substantially. In-memory computing is a promising approach in which nanoscale resistive memory devices, organized in a computational memory unit, are used for both processing and memory. However, to reach the numerical accuracy typically required for data analytics and scientific computing, limitations arising from device variability and non-ideal device characteristics need to be addressed. Here we introduce the concept of mixed-precision in-memory computing, which combines a von Neumann machine with a computational memory unit. In this hybrid system, the computational memory unit performs the bulk of a computational task, while the von Neumann machine implements a backward method to iteratively improve the accuracy of the solution. The system therefore benefits from both the high precision of digital computing and the energy/areal efficiency of in-memory computing. We experimentally demonstrate the efficacy of the approach by accurately solving systems of linear equations, in particular, a system of 5,000 equations using 998,752 phase-change memory devices.A hybrid system that combines a von Neumann machine with a computational memory unit can offer both the high precision of digital computing and the energy/areal efficiency of in-memory computing, which is illustrated by accurately solving a system of 5,000 equations using 998,752 phase-change memory devices.

Inherent stochasticity in phase-change memory devices

A two terminal nanoscale device showing inherent stochastic behavior can be a key enabler for a wide range of applications such as stochastic computing, machine learning and neuromorphic engineering. In this article we investigate the inherent stochasticity associated with two key attributes of phase-change memory devices, namely, threshold switching and memory switching. The physical origin of this stochasticity is traced to the differences in the atomic configurations of the amorphous phase created via the melt-quench process after each RESET operation, which is validated by simulation and experimental results. We also present experimental results for one specific application namely, a true random number generator.