Systematic DC/AC Performance Benchmarking of Sub-7-nm Node FinFETs and Nanosheet FETs

In this paper, we systematically evaluate dc/ac performances of sub-7-nm node fin field-effect transistors (FinFETs) and nanosheet FETs (NSEETs) using fully calibrated 3-D TCAD. The stress effects of all the devices were carefully considered in terms of carrier mobility and velocity averaged within the active regions. For detailed AC analysis, the parasitic capacitances were extracted and decomposed into several components using TCAD RF simulation platform. FinFETs improved the gate electrostatics by decreasing fin widths to 5 nm, but the fin heights were unable to improve RC delay due to the trade-off between on-state currents and gate capacitances. The NSEETs have better on-state currents than do the FinFETs because of larger effective widths (W-eff) under the same device area. Particularly p-type NSEETs have larger compressive stress within the active regions affected by metal gate encircling all around the channels, thus improving carrier mobility and velocity much. On the other hand, the NSEETs have larger gate capacitances because larger W-eff increase the gate-to-source/drain overlap and outer-fringing capacitances. In spite of that, sub-7-nm node NSEETs attain better RC delay than sub-7-nm node as well as 10-nm node FinFETs for standard and high performance applications, showing better chance for scaling down to sub-7-nm node and beyond.11Ysciescopu

Bottom oxide Bulk FinFETs Without Punch-Through-Stopper for Extending Toward 5-nm Node

Structural advancements of 5-nm node bulk fin-shaped field-effect transistors (FinFETs) without punch-through-stopper (PTS) were introduced using fully calibrated TCAD for the first time. It is challenging to scale down conventional bulk FinFETs into 5-nm technology node due to the sub-fin leakage increase. Meanwhile, bottom oxide deposition after anisotropic etching for source/drain (S/D) epi formation prevents the sub-fin leakage effectively even without the PTS doping, thus achieving better gate-to-channel controllability. Bottom oxide FinFETs also have smaller gate capacitances than do conventional FinFETs because the parasitic capacitances decrease by smaller S/D epi separated from the bottom Si layer, which reduces junction and outer-fringing capacitances. But smaller S/D epi decreases the stresses along the channel direction, and the effective widths decrease by the bottom oxide layer blocking the current paths at the bottom side of fin channels. Furthermore, increase of the interconnect resistance and capacitance parasitics down to 5-nm node diminishes the improvements of total delays as the interconnect wire length increases greatly. In spite of these drawbacks, 5-nm node bottom oxide FinFETs achieve smaller total delays than do the 7-nm node conventional FinFETs, especially for low-power applications, thus promising for the scalability of bulk FinFETs along with simple and reliable process by avoiding PTS step.11Ysciescopu

Source/Drain Patterning FinFETs as Solution for Physical Area Scaling Toward 5-nm Node

A novel and feasible process scheme to downsize the source/drain (S/D) epitaxy of 5-nm node bulk fin-shaped field-effect transistors (FinFETs) were introduced by using fully-calibrated TCAD for the first time. The S/D epitaxy formed by selective epitaxial growth was diamond-shaped and occupied a large proportion of the device size irrespective of the active channel area. However, this problem was solved by patterning the low-k regions prior to S/D formation by preventing the lateral overgrowth of S/D epitaxy; the so-called S/D patterning (SDP). Its smaller S/D epitaxy decreased the average longitudinal channel stresses and drive currents for NFETs. However, the small diffusions of the boron dopants into the channel regions improved the short-channel effects and alleviated the drive current reduction for PFETs. Gate capacitances decreased greatly by reducing outer-fringing capacitances between the metal-gate stack and S/D regions. Through SPICE simulation based on the virtual source model, operation frequencies and dynamic powers of 15-stage ring oscillators were studied. SDP FinFETs have better circuit performances than the conventional and bottom oxide bulk FinFETs along with smaller active areas, promising for further area scaling through simple and reliable S/D process.11Ysciescopu

Gate-All-Around FETs: Nanowire and Nanosheet Structure

DC/AC performances of 3-nm-node gate-all-around (GAA) FETs having different widths and the number of channels (Nch) from 1 to 5 were investigated thoroughly using fully-calibrated TCAD. There are two types of GAAFETs: nanowire (NW) FETs having the same width (WNW) and thickness of the channels, and nanosheet (NS) FETs having wide width (WNS) but the fixed thickness of the channels as 5 nm. Compared to FinFETs, GAAFETs can maintain good short channel characteristics as the WNW is smaller than 9 nm but irrespective of the WNS. DC performances of the GAAFETs improve as the Nch increases but at decreasing rate because of the parasitic resistances at the source/drain epi. On the other hand, gate capacitances of the GAAFETs increase constantly as the Nch increases. Therefore, the GAAFETs have minimum RC delay at the Nch near 3. For low power applications, NWFETs outperform FinFETs and NSFETs due to their excellent short channel characteristics by 2-D structural confinement. For standard and high performance applications, NSFETs outperform FinFETs and NWFETs by showing superior DC performances arising from larger effective widths per footprint. Overall, GAAFETs are great candidates to substitute FinFETs in the 3-nm technology node for all the applications

Efficient Privacy-Preserving Matrix Factorization via Fully Homomorphic Encryption

Recommendation systems become popular in our daily life. It is well known that the more the release of users’ personal data, the better the quality of recommendation. However, such services raise serious privacy concerns for users. In this paper, focusing on matrix factorization-based recommendation systems, we propose the first privacy-preserving matrix factorization using fully homomorphic encryption. On inputs of encrypted users\u27 ratings, our protocol performs matrix factorization over the encrypted data and returns encrypted outputs so that the recommendation system knows nothing on rating values and resulting user/item profiles. It provides a way to obfuscate the number and list of items a user rated without harming the accuracy of recommendation, and additionally protects recommender\u27s tuning parameters for business benefit and allows the recommender to optimize the parameters for quality of service. To overcome performance degradation caused by the use of fully homomorphic encryption, we introduce a novel data structure to perform computations over encrypted vectors, which are essential operations for matrix factorization, through secure 2-party computation in part. With the data structure, the proposed protocol requires dozens of times less computation cost over those of previous works. Our experiments on a personal computer with 3.4 GHz 6-cores 64 GB RAM show that the proposed protocol runs in 1.5 minutes per iteration. It is more efficient than Nikolaenko et al.\u27s work proposed in CCS 2013, in which it took about 170 minutes on two servers with 1.9 GHz 16-cores 128 GB RAM

Iterative Compression of End-to-End ASR Model using AutoML

Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research have shown that AutoML-based Low Rank Factorization (LRF) technique, when applied to an end-to-end Encoder-Attention-Decoder style ASR model, can achieve a speedup of up to 3.7x, outperforming laborious manual rank-selection approaches. However, we show that current AutoML-based search techniques only work up to a certain compression level, beyond which they fail to produce compressed models with acceptable word error rates (WER). In this work, we propose an iterative AutoML-based LRF approach that achieves over 5x compression without degrading the WER, thereby advancing the state-of-the-art in ASR compression

Metodologias alternativas no ensino de física

Screening a compound library of quinolinone derivatives identified compound 11a as a new P2X7 receptor antagonist. To optimize its activity, we assessed structure-activity relationships (SAR) at three different positions, R_1, R_2 and R_3, of the quinolinone scaffold. SAR analysis suggested that a carboxylic acid ethyl ester group at the R_1 position, an adamantyl carboxamide group at R_2 and a 4-methoxy substitution at the R_3 position are the best substituents for the antagonism of P2X7R activity. However, because most of the quinolinone derivatives showed low inhibitory effects in an IL-1β ELISA assay, the core structure was further modified to a quinoline skeleton with chloride or substituted phenyl groups. The optimized antagonists with the quinoline scaffold included 2-chloro-5-adamantyl-quinoline derivative (16c) and 2-(4-hydroxymethylphenyl)-5-adamantyl-quinoline derivative (17k), with IC_(50) values of 4 and 3 nM, respectively. In contrast to the quinolinone derivatives, the antagonistic effects of the quinoline compounds (16c and 17k) were paralleled by their ability to inhibit the release of the pro-inflammatory cytokine, IL-1β, from LPS/IFN-γ/BzATP-stimulated THP-1 cells (IC_(50) of 7 and 12 nM, respectively). In addition, potent P2X7R antagonists significantly inhibited the sphere size of TS15-88 glioblastoma cells

Sectional Information-Based Collision Warning System Using Roadside Unit Aggregated Connected-Vehicle Information for a Cooperative Intelligent Transport System

Vehicular collision and hazard warning is an active field of research that seeks to improve road safety by providing an earlier warning to drivers to help them avoid potential collision danger. In this study, we propose a new type of a collision warning system based on aggregated sectional information, describing vehicle movement processed by a roadside unit (RSU). The proposed sectional information-based collision warning system (SCWS) overcomes the limitations of existing collision warning systems such as the high installation costs, the need for high market penetration rates, and the lack of consideration of traffic dynamics. The proposed SCWS gathers vehicle operation data through on-board units (OBUs) and shares this aggregated information through an RSU. All the data for each road section are locally processed by the RSU using edge computing, allowing the SCWS to effectively estimate the information describing the vehicles surrounding the subject vehicle in each road section. The performance of the SCWS was evaluated through comparison with other collision warning systems such as the vehicle-to-vehicle communication-based collision warning system (VCWS), which solely uses in-vehicle sensors; the hybrid collision warning system (HCWS), which uses information from both infrastructure and in-vehicle sensors; and the infrastructure-based collision warning system (ICWS), which only uses data from infrastructure. In this study, the VCWS with a 100% market penetration rate was considered to provide the most theoretically similar result to the actual collision risk. The comparison results show that in both aggregation and disaggregation level analyses, the proposed SCWS exhibits a similar collision risk trend to the VCWS. Furthermore, the SCWS shows a high potential for practical application because it provides acceptable performance even with a low market penetration rate (30%) at the relatively low cost of OBU installation, compared to the VCWS requirement of a high market penetration rate at a high installation cost

Advanced One-Dimensional Entrained-Flow Gasifier Model Considering Melting Phenomenon of Ash

A one-dimensional model is developed to represent the ash-melting phenomenon, which was not considered in the previous one-dimensional (1-D) entrained-flow gasifier model. We include sensible heat of slag and the fusion heat of ash in the heat balance equation. To consider the melting of ash, we propose an algorithm that calculates the energy balance for three scenarios based on temperature. We also use the composition and the thermal properties of anorthite mineral to express ash. gPROMS for differential equations is used to solve this algorithm in a simulation; the results include coal conversion, gas composition, and temperature profile. Based on the Texaco pilot plant gasifier, we validate our model. Our results show good agreement with previous experimental data. We conclude that the sensible heat of slag and the fusion heat of ash must be included in the entrained flow gasifier model