Advanced Transistor Process Technology from 22- to 14-nm Node

Transistor performance meets great technical challenges as the critical dimension (CD) shrinking beyond 32/28-nm nodes. A series of innovated process technologies such as high-k/metal gate, strain engineering, and 3D FinFET to overcome these challenges are reviewed in this chapter. The principle, developing route, and main prosperities of these technologies are systematically described with theoretical analysis and experimental results. Especially, the material choice, film stack design, and process flow integration approach with high-k/metal gate for sub-22-nm node is introduced; the film growth technique, process optimization, and flow integration method with advanced strain engineering are investigated; the architecture design, critical process definition, and integration scheme matching with traditional planar 2D transistor for 14-nm 3D FinFET are summarized

The multidimensional dependence of halo bias in the eye of a machine: a tale of halo structure, assembly and environment

We develop a novel approach in exploring the joint dependence of halo bias on multiple halo properties using Gaussian process regression. Using a $\Lambda$CDM $N$-body simulation, we carry out a comprehensive study of the joint bias dependence on halo structure, formation history and environment. We show that the bias is a multivariate function of halo properties that falls into three regimes. For massive haloes, halo mass explains the majority of bias variation. For early-forming haloes, bias depends sensitively on the recent mass accretion history. For low-mass and late-forming haloes, bias depends more on the structure of a halo such as its shape and spin. Our framework enables us to convincingly prove that $V_\mathrm{max}/V_\mathrm{vir}$ is a lossy proxy of formation time for bias modelling, whereas the mass, spin, shape and formation time variables are non-redundant with respect to each other. Combining mass and formation time largely accounts for the mass accretion history dependence of bias. Combining all the internal halo properties fully accounts for the density profile dependence inside haloes, and predicts the clustering variation of individual haloes to a $20\%$ level at $\sim 10\mathrm{Mpc}h^{-1}$. When an environmental density is measured outside $1\mathrm{Mpc}h^{-1}$ from the halo centre, it outperforms and largely accounts for the bias dependence on the internal halo structure, explaining the bias variation above a level of $30\%$.Comment: MNRAS accepte

Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model

The outbreak of novel coronavirus pneumonia (COVID-19) caused thousands of deaths worldwide, and the number of total infections is still rising. However, the development of effective vaccine for this novel virus would take a few months. Thus it is urgent to identify some potentially effective old drugs that can be used immediately. Fortunately, some compounds that can inhibit coronavirus in vitro have been reported. In this study, the coronavirus-specific dataset was used to fine-tune our pre-trained multi-task deep model. Next we used the re-trained model to select available commercial drugs against targeted proteins of SARS-CoV-2. The results show that abacavir, a powerful nucleoside analog reverse transcriptase inhibitor used to treat HIV, is predicted to have high binding affinity with several proteins of SARS-CoV-2. Almitrine mesylate and roflumilast which are used for respiratory diseases such as chronic obstructive pulmonary disease are also predicted to have inhibitory effect. Overall, ten drugs are listed as potential inhibitors and the important sites for these binding by our model are exhibited. We hope these results would be useful in the fight against SARS-CoV-2

LS-DTKMS: A Local Search Algorithm for Diversified Top-k MaxSAT Problem

The Maximum Satisfiability (MaxSAT), an important optimization problem, has a range of applications, including network routing, planning and scheduling, and combinatorial auctions. Among these applications, one usually benefits from having not just one single solution, but k diverse solutions. Motivated by this, we study an extension of MaxSAT, named Diversified Top-k MaxSAT (DTKMS) problem, which is to find k feasible assignments of a given formula such that each assignment satisfies all hard clauses and all of them together satisfy the maximum number of soft clauses. This paper presents a local search algorithm, LS-DTKMS, for DTKMS problem, which exploits novel scoring functions to select variables and assignments. Experiments demonstrate that LS-DTKMS outperforms the top-k MaxSAT based DTKMS solvers and state-of-the-art solvers for diversified top-k clique problem

Knowledge Graph Reasoning over Entities and Numerical Values

A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate from? Knowledge graph reasoning-based applications, such as dialogue systems and interactive search engines, rely on the ability to answer complex logic queries as a fundamental task. In most knowledge graphs, edges are typically used to either describe the relationships between entities or their associated attribute values. An attribute value can be in categorical or numerical format, such as dates, years, sizes, etc. However, existing complex query answering (CQA) methods simply treat numerical values in the same way as they treat entities. This can lead to difficulties in answering certain queries, such as which Australian Pulitzer award winner is born before 1927, and which drug is a pain reliever and has fewer side effects than Paracetamol. In this work, inspired by the recent advances in numerical encoding and knowledge graph reasoning, we propose numerical complex query answering. In this task, we introduce new numerical variables and operations to describe queries involving numerical attribute values. To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures. During the numerical encoding process, NRN employs a parameterized density function to encode the distribution of numerical values. During the entity encoding process, NRN uses established query encoding methods for the original CQA problem. Experimental results show that NRN consistently improves various query encoding methods on three different knowledge graphs and achieves state-of-the-art results