Dominique Vuillaume

Low-voltage, 30 nm channel length, organic transistors with a self-assembled monolayer as gate insulating films

We made nanometer-scale (gate length of 30 nm) organic thin-film transistors using a self-assembled monolayer (2 nm thick) as a gate insulator. The fabrication steps combine electron-beam lithography and lift-off techniques for the deposition of both metal electrodes and organic semiconductors with a chemical approach (self-assembly of organic molecules) to fabricate the gate insulator. Good performances of these transistors (with a record subthreshold slop of 350 mV/decade and a cutoff frequency of 20 kHz) and low-voltage operation (<2 V) are demonstrated down to a gate length of 200 nm. A gate voltage modulation of the source-to-drain tunnel current is demonstrated for the 30 nm gate length device.

An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse

Molecule-based devices are envisioned to complement silicon devices by providing new functions or by implementing existing functions at a simpler process level and lower cost, by virtue of their self-organization capabilities. Moreover, they are not bound to von Neuman architecture and this feature may open the way to other architectural paradigms. Neuromorphic electronics is one of them. Here, a device made of molecules and nanoparticles-a nanoparticle organic memory field-effect transistor (NOMFET)—that exhibits the main behavior of a biological spiking synapse is demonstrated. Facilitating and depressing synaptic behaviors can be reproduced by the NOMFET and can be programmed. The synaptic plasticity for real-time computing is evidenced and described by a simple model. These results open the way to rate-coding utilization of the NOMFET in dynamical neuromorphic computing circuits.

Metal∕organic∕metal bistable memory devices

We report a bistable organic memory made of a single organic layer embedded between two electrodes, and compare to the organic∕metal nanoparticle∕organic tri-layers device [Ma, Liu, and Yang, Appl. Phys. Lett. 80, 2997 (2002)]. We demonstrate that the two devices exhibit similar temperature-dependent behaviors, a thermally activated behavior in their low conductive state (off-state) and a slightly “metallic” behavior in their high conductive state (on-state). This feature emphasizes a similar origin for the memory effect. Owing to their similar behavior, the one layer memory is advantageous in terms of fabrication cost and simplicity.

Molecular Rectifying Diodes from Self-Assembly on Silicon

We demonstrate a molecular rectifying junction made from a sequential self-assembly on silicon. The device structure consists of only one conjugated (π) group and an alkyl spacer chain. We obtain rectification ratios up to 37 and threshold voltages for rectification between −0.3 and −0.9 V. We show that rectification occurs from resonance through the highest occupied molecular orbital of the π group in good agreement with our calculations and internal photoemission spectroscopy. This approach allows us to fabricate molecular rectifying diodes compatible with silicon nanotechnologies for future hybrid circuitries.

Nano-field effect transistor with an organic self-assembled monolayer as gate insulator

We demonstrate the realization and functioning of a hybrid (organic/silicon) nanometer-size field effect transistor (nano-FET) having a gate length of 25 nm. The gate insulator is an organic self-assembled monolayer (SAM) of alkyltrichlorosilanes (∼2 nm thick). We have used densely packed SAMs with functionalized end groups (–CH3, –CH=CH2, –COOH) that all exhibit reduced leakage current density (10−8–10−5 A/cm2). This nano-FET is free of punchthrough down to 50 nm, and shows a good field effect behavior at 25 nm. This demonstrates the compatibility of these SAMs with semiconductor device processes and their wide capability for applications in nanometer-scale electronics.

Theory of electrical rectification in a molecular monolayer

The current-voltage characteristics in Langmuir-Blodgett monolayers of \ensuremath{\gamma}-hexadecylquinolinium tricyanoquinodimethanide

Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses

({\mathrm{C}}_{16}{\mathrm{H}}_{33}\mathrm{Q}\ensuremath{-}3\mathrm{CNQ})

Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction

sandwiched between Al or Au electrodes is calculated, combining ab initio and self-consistent tight-binding techniques. The rectification current depends not only on the position of the LUMO and HOMO relative to the Fermi levels of the electrodes as in the Aviram-Ratner mechanism, but also on the profile of the electrostatic potential which is extremely sensitive to where the electroactive part of the molecule lies in the monolayer. This second effect can produce rectification in the direction opposite to the Aviram-Ratner prediction.

A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

In this paper, we present an alternative approach to neuromorphic systems based on multilevel resistive memory synapses and deterministic learning rules. We demonstrate an original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stochastic learning rules. New circuit architecture, programming strategy, and probabilistic spike-timing dependent plasticity (STDP) learning rule for two different CBRAM configurations with-selector (1T-1R) and without-selector (1R) are proposed. We show two methods (intrinsic and extrinsic) for implementing probabilistic STDP rules. Fully unsupervised learning with binary synapses is illustrated through two example applications: 1) real-time auditory pattern extraction (inspired from a 64-channel silicon cochlea emulator); and 2) visual pattern extraction (inspired from the processing inside visual cortex). High accuracy (audio pattern sensitivity > 2, video detection rate > 95%) and low synaptic-power dissipation (audio 0.55 μW, video 74.2 μW) are shown. The robustness and impact of synaptic parameter variability on system performance are also analyzed.

Gold nanoparticle-pentacene memory-transistors

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. Multi-physical 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.