We demonstrate a unique energy efficient methodology to use Phase Change Memory (PCM) as synapse in ultra-dense large scale neuromorphic systems. PCM devices with different chalcogenide materials were characterized to demonstrate synaptic behavior. Multiphysical simulations were used to interpret the results. We propose special circuit architecture ("the 2-PCM synapse"), read, write, and reset programming schemes suitable for the use of PCM in neural networks. A versatile behavioral model of PCM which can be used for simulating large scale neural systems is introduced. First demonstration of complex visual pattern extraction from real world data using PCM synapses in a 2-layer spiking neural network (SNN) is shown. System power analysis for different scaled PCM technologies is also provided.

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In this work, we demonstrate how phase change memory (PCM) devices can be used to emulate biologically inspired synaptic functions in particular, potentiation and depression, important for implementing neuromorphic hardware. PCM devices with different chalcogenide materials are fabricated and characterized. The asymmetry between the potentiation and depression behaviors of the PCM is stressed. Detailed multi-physical simulations are performed to study the underlying physics of the synaptic behavior of PCM. A versatile behavioral model and a multi-level circuitcompatible model are developed for system and circuit-level neuromorphic simulations. We propose a unique low-power methodology named the 2-PCM Synapse, to use PCM devices as synapses in large scale neuromorphic systems. To show the strength of our proposed solution, we efficiently simulated fully connected feed-forward spiking neural network capable of complex visual pattern extraction from real world data. V C 2012 American Institute of Physics.

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Resistive switching (RS) based on the formation and rupture of conductive filament (CF) is promising in novel memory and logic device applications. Understanding the physics of RS and the nature of CF is of utmost importance to control the performance, variability and reliability of resistive switching memory (RRAM). Here, the RESET switching of HfO2-based RRAM was statistically investigated in terms of the CF conductance evolution. The RESET usually combines an abrupt conductance drop with a progressive phase ending with the complete CF rupture. RESET1 and RESET2 events, corresponding to the initial and final phase of RESET, are found to be controlled by the voltage and power in the CF, respectively. A Monte Carlo simulator based on the thermal dissolution model of unipolar RESET reproduces all of the experimental observations. The results contribute to an improved physics-based understanding on the switching mechanisms and provide additional support to the thermal dissolution model.

While Resistive RAM (RRAM) are seen as an alternative to NAND Flash, their variability and cycling understanding remain a major roadblock. Extensive characterizations of multi-kbits RRAM arrays during Forming, Set, Reset and cycling operations are presented allowing the quantification of the intrinsic variability factors. As a result, the fundamental variability limits of filament-based RRAM in the full resistance range are identified

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