One consequence of the continued downward scaling of transistors is the reliance on only a few discrete atoms to dope the channel, and random fluctuations in the number of these dopants are already a major issue in the microelectronics industry. Although single dopant signatures have been observed at low temperatures, the impact on transistor performance of a single dopant atom at room temperature is not well understood. Here, we show that a single arsenic dopant atom dramatically affects the off-state room-temperature behaviour of a short-channel field-effect transistor fabricated with standard microelectronics processes. The ionization energy of the dopant is measured to be much larger than it is in bulk, due to its proximity to the buried oxide, and this explains the large current below threshold and large variability in ultra-scaled transistors. The results also suggest a path to incorporating quantum functionalities into silicon CMOS devices through manipulation of single donor orbitals.

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.

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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Phase change memory can provide a remarkable artificial synapse for neuromorphic systems, as it features excellent reliability and can be used as an analog memory. However, this approach is complicated by the fact that crystallization and amorphization differ radically: crystallization can be realized in a very gradual manner, very similarly to synaptic potentiation, while the amorphization process tends to be abrupt, unlike synaptic depression. Addressing this non‐biorealism of amorphization requires system‐level solutions that have considerable energy cost or limit the generality of the approach. This work demonstrates experimentally that an adaptation of the memory structure associated with an initialization electrical pulse followed by a sequence of identical fast pulses can overcome this challenge. A single device can then naturally implement gradual long‐term potentiation and depression, much like synapses in biology. This study evidences through statistical measurements the reproducibility of the approach, discusses its physical origin, as well as the importance of the device architecture and of the initial electrical pulse. Through the use of system‐level simulation, it is shown that this device is especially adapted to a neuroscience‐inspired learning. These results highlight how nanodevices can be suitable for bioinspired applications while retaining the qualities of industrial technology.

In this work, we present an experimental and theoretical analysis of scaled (down to 10nm) Al 2 O 3 /CuTeGe based CBRAM. We focus on the understanding of the physical mechanisms responsible for the failure of high and low resistance states at high temperature. Using a numerical model combined with ab-initio calculations, we elucidate for the 1 st time at our knowledge the role of the filament morphology on the resistance instability. We demonstrate that an optimized filament shape (tuned by adjusting the operating conditions) significantly improves the memory window stability at high temperatures.

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