Monte Carlo study of coaxially gated CNTFETs: capacitive effects and dynamic performance

Carbon Nanotube (CNT) appears as a promising candidate to shrink field-effect transistors (FET) to the nanometer scale. Extensive experimental works have been performed recently to develop the appropriate technology and to explore DC characteristics of carbon nanotube field effect transistor (CNTFET). In this work, we present results of Monte Carlo simulation of a coaxially gated CNTFET including electron-phonon scattering. Our purpose is to present the intrinsic transport properties of such material through the evaluation of electron mean-free-path. To highlight the potential of high performance level of CNTFET, we then perform a study of DC characteristics and of the impact of capacitive effects. Finally, we compare the performance of CNTFET with that of Si nanowire MOSFET.Comment: 15 pages, 14 figures, final version to be published in C. R. Acad. Sci. Pari

Electron effective mobility in strained Si/Si1-xGex MOS devices using Monte Carlo simulation

Based on Monte Carlo simulation, we report the study of the inversion layer mobility in n-channel strained Si/ Si1-xGex MOS structures. The influence of the strain in the Si layer and of the doping level is studied. Universal mobility curves mueff as a function of the effective vertical field Eeff are obtained for various state of strain, as well as a fall-off of the mobility in weak inversion regime, which reproduces correctly the experimental trends. We also observe a mobility enhancement up to 120 % for strained Si/ Si0.70Ge0.30, in accordance with best experimental data. The effect of the strained Si channel thickness is also investigated: when decreasing the thickness, a mobility degradation is observed under low effective field only. The role of the different scattering mechanisms involved in the strained Si/ Si1-xGex MOS structures is explained. In addition, comparison with experimental results is discussed in terms of SiO2/ Si interface roughness, as well as surface roughness of the SiGe substrate on which strained Si is grown.Comment: 25 pages, 8 figures, 1 table, revised version, discussions and references adde

Sequential Transport in a Two-Dot Device

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Introduction à la microélectronique : un TP de physique du composant

Nous présentons un TP de physique du composant, basé sur l'utilisation d'un logiciel de simulation des transistors MOSFET à inversion. Ce logiciel comporte deux volets. Le premier, PROF, permet de visualiser facilement et rapidement les caractéristiques électriques externes des MOSFET, issues d'un modèle SPICE. Le deuxième, MCARLO, offre la possibilité d'étudier quelques paramètres microscopiques caractérisant le transport des porteurs de charge dans le canal du transistor (vitesse, énergie, trajectoire), calculés à l'aide de modèles physiques simplifiés (“énergie-balance” et simulation particulaire Monte Carlo). Cette séance de TP aide à la compréhension des phénomènes physiques mis en jeu dans les MOSFET, et met en valeur les problèmes posés par ces phénomènes pour le dimensionnement des composants au sein des circuits logiques CMO

Phonon dispersion in silicon nanocrystals

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Electron/phonon interaction in silicon quantum dots

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Initiation à la microélectronique ultime CMOS

Dans cet article, nous présentons un projet proposé en 1ère année de master et permettant d'appréhender le contexte de la microélectronique CMOS, avec en particulier les enjeux actuels de la miniaturisation des composants. Pour cela, les étudiants mettent en oeuvre des logiciels de simulation physique de transistors et pratiquent la réalisation technologique de structures élémentaires en salle blanche, objets qui sont ensuite caractérisés électriquement

Suppression of the orientation effects on bandgap in graphene nanoribbons in the presence of edge disorder

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Approximate programming of magnetic memory elements for energy saving

International audienceThe high density of on-chip nonvolatile memory provided by memristive elements is highly desirable for many applications. However, it raises concerns about finding the best programming strategies to limit the energy consumption of such systems. Here, we highlight the case of magnetic memory, where several unconventional programming strategies can reduce energy consumption, especially for applications in neuromorphic computing. 1. STT-MTJ programming Magnetic Tunnel Junctions programmed through the Spin Transfer Torque effect (STT-MTJs), the basic cells of spin transfer torque magnetic RAM (STT-MRAM), feature fast programming, non-volatility and outstanding endurance. The behavior of such device is presented in Fig. 1 and is reminiscent of a binary bipolar memristor. The specificity of such devices appears through the stochastic nature of their programming. Indeed, under a programming pulse of given voltage í µí±‰ í µí±í µí±Ÿí µí±œí µí±” and duration ∆í µí±¡, the STT-MTJ has a non-100% probability í µí±ƒ(í µí±‰ í µí±í µí±Ÿí µí±œí µí±” , ∆í µí±¡) to switch state (Fig. 2). This effect has been extensively studied and the switching statistics can be described through comprehensive analytical models [1]. We consider a STT-MTJ corresponding to a 32nm technology. For any voltage amplitude, models allow us to derive the reduction of the programming bit error rate (ER) – the probability of failed switching – as the pulse duration (Fig. 3) and the programming energy (Fig. 4) increase. It appears that the pulse duration gives us an efficient handle to tune the ER. Different programming regimes can then be identified depending on targeted application and error rate. Starting from a programming regime for high-significance data, ensuring ER=10-10 , we evaluate the energy reduction that can be obtained by accepting an increase of the error rate (Fig. 5). 2. Disciplined approximate storage Given a programming voltage í µí±‰ í µí±í µí±Ÿí µí±œí µí±” =0.65V, allowing an increase of the ER to 10-2 grants a reduction of 62% of the energy expense. Considering the use of a STT-MRAM to store synaptic weights as floating point number in neural networks applications, strategies of disciplined approximate programming can be enforced to reduce the global energy expense [2]: identifying lower-significance part of the data i.e., least significant bits of the weight (LSB), and releasing them from low error-rate constraints. On Fig. 6, we expose the predicted energy reduction factor as a function of the number of bits labelled as lowly significant, and the error rate with which they are programmed. For instance, the reasonable choice of 26 LSB (half of the float significand) with ER=10-2 grants a 22% energy reduction when compared to a fully high-significance memory use. 3. MTJs as stochastic synapses Further increase of the ER above 10-2 corresponds to entering a regime of stochastic programming of the devices. This very low energy regime can be implemented to achieve memristive synapses with stochastic plasticity in hardware neural network [3,4]. In such applications, the devices state –thus the synapses conductance– evolves during the learning process according to a stochastic rule, with very high ER, possibly over 90%. In that case, the drastic reduction of the energy expense (86%) comes from exploiting the intrinsic device randomness as an essential feature of the system. In recent work [3], we highlighted that a STT-MTJ based hardware neural network has potential to achieve unsupervised learning by testing it against a task of vehicle counting (Fig. 7). 4. Sensibility to programming variability As can be seen on Fig. 4, the ER resilience to programming conditions variability (5% variability on the voltage are considered here) increases as the targeted error-rate increases. While releasing low error-rate constraints, systems then tends to become more robust to variability. Notably, neural networks based on stochastic STT-MTJs with ER=90% proved unchanged performances for devices variability up to 17% [3]. 5. Conclusion Deterministic programming strategies are associated to high energy cost and do not suit ideally bio-inspired applications. By contrast, strategies can be developed to consider higher ER programming for energy reduction