A comparison of modeling approaches for current transport in polysilicon‑channel nanowire and macaroni GAA MOSFETs

AbstractIn this paper, we compare quantitatively the results obtained from the numerical simulation of current transport in polysilicon-channel MOSFETs under different modeling assumptions typically adopted to reproduce the basic physics of the devices, including the effective medium approximation and the description of polysilicon as the haphazard ensemble of monocrystalline silicon grains separated by highly defective grain boundaries. In the latter case, both pure drift-diffusion transport and a mix of intra-grain drift-diffusion and inter-grain thermionic emission are considered. Interest is focused on cylindrical nanowire and macaroni gate-all-around structures, due to their relevance in the field of 3-Dimensional NAND Flash memories, focusing not only on the average behavior but also on the variability in the electrical characteristics of the devices

Memristive and CMOS Devices for Neuromorphic Computing

Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed

Investigation of the Meyer-Neldel rule in Si MOSFETs

We show that a Meyer-Neldel (MN) regime, usually reported for disordered materials, is found in state-of-the-art monocrystalline Si MOSFETs. This unexpected result is explained via device simulation in terms of the self-consistency between the mobile charge and the device electrostatics. The dependence on device parameters is then discussed, showing that neglecting this effect can lead to interpretation errors for data evaluated at low activation energies