A device-level characterization approach to quantify the impacts of different random variation sources in FinFET technology

A simple device-level characterization approach to quantitatively evaluate the impacts of different random variation sources in FinFETs is proposed. The impacts of random dopant fluctuation are negligible for FinFETs with lightly doped channel, leaving metal gate granularity and line-edge roughness as the two major random variation sources. The variations of Vth induced by these two major categories are theoretically decomposed based on the distinction in physical mechanisms and their influences on different electrical characteristics. The effectiveness of the proposed method is confirmed through both TCAD simulations and experimental results. This letter can provide helpful guidelines for variation-aware technology development

Impact of self-heating on the statistical variability in bulk and SOI FinFETs

In this paper for the first time we study the impact of self-heating on the statistical variability of bulk and SOI FinFETs designed to meet the requirements of the 14/16nm technology node. The simulations are performed using the GSS ‘atomistic’ simulator GARAND using an enhanced electro-thermal model that takes into account the impact of the fin geometry on the thermal conductivity. In the simulations we have compared the statistical variability obtained from full-scale electro-thermal simulations with the variability at uniform room temperature and at the maximum or average temperatures obtained in the electro-thermal simulations. The combined effects of line edge roughness and metal gate granularity are taken into account. The distributions and the correlations between key figures of merit including the threshold voltage, on-current, subthreshold slope and leakage current are presented and analysed

Decoupled Local Aggregation for Point Cloud Learning

The unstructured nature of point clouds demands that local aggregation be adaptive to different local structures. Previous methods meet this by explicitly embedding spatial relations into each aggregation process. Although this coupled approach has been shown effective in generating clear semantics, aggregation can be greatly slowed down due to repeated relation learning and redundant computation to mix directional and point features. In this work, we propose to decouple the explicit modelling of spatial relations from local aggregation. We theoretically prove that basic neighbor pooling operations can too function without loss of clarity in feature fusion, so long as essential spatial information has been encoded in point features. As an instantiation of decoupled local aggregation, we present DeLA, a lightweight point network, where in each learning stage relative spatial encodings are first formed, and only pointwise convolutions plus edge max-pooling are used for local aggregation then. Further, a regularization term is employed to reduce potential ambiguity through the prediction of relative coordinates. Conceptually simple though, experimental results on five classic benchmarks demonstrate that DeLA achieves state-of-the-art performance with reduced or comparable latency. Specifically, DeLA achieves over 90\% overall accuracy on ScanObjectNN and 74\% mIoU on S3DIS Area 5. Our code is available at https://github.com/Matrix-ASC/DeLA