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One-step Synthesis of Organic Terminal 2D Ti3C2TxMXene Nanosheets by Etching of Ti3AlC2in an Organic Lewis Acid Solvent

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Angewandte Chemie 2024年第23期136卷

作者： Fei Wang Feng Tian Xuewen Xia Dr. Zhongya Pang Dr. Shujuan Wang Dr. Xing Yu Dr. Guangshi Li Prof. Dr. Yufeng Zhao Prof. Dr. Qian Xu Prof. Dr. Shen Hu Li Ji Prof. Dr. Xingli Zou Prof. Dr. Xionggang Lu State Key Laboratory of Advanced Special Steel & School of Materials Science and Engineering Shanghai University Shanghai 200444 P. R. China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 P. R. China Institute of Sustainable Energy College of Sciences Shanghai University Shanghai 200444 P. R. China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai 200433 P. R. China

The surface and interface chemistry are critical for controlling the properties of two-dimensional transition metal carbides and nitrides (MXenes). Numerous efforts have been devoted to the functionalization of MXenes with small inorganic ligands; however, few etching methods have been reported on the direct bonding of organic groups to MXene surfaces. In this work, we demonstrated an efficient and rapid strategy for the direct synthesis of 2D Ti 3 C 2 T x MXene nanosheets with organic terminal groups in an organic Lewis acid (trifluoromethanesulfonic acid) solvent, without introducing additional intercalations. The dissolution of aluminum and the subsequent in situ introduction of trifluoromethanesulfonic acid resulted in the extraction of Ti 3 C 2 T x MXene (T=CF 3 SO 3 − ) (denoted as CF 3 SO 3 H−Ti 3 C 2 T x ) flakes with sizes reaching 15 μm and high productivity (over 70 %) of monolayers or few layers. More importantly, the large CF 3 SO 3 H−Ti 3 C 2 T x MXene nanosheets had high colloidal stability, making them promising as efficient electrocatalysts for the hydrogen evolution reaction.

关键词： MXene organic lewis acid CF3SO3− termianal free-standing film HER

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Exploring a Bayesian Optimization Framework Compatible with Digital Standard Flow for Soft-Error-Tolerant Circuit

Exploring a Bayesian Optimization Framework Compatible with ...

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Design Automation Conference

作者： Yan Li Xiaoyoung Zeng Zhengqi Gao Liyu Lin Jun Tao Jun Han Xu Cheng Mehdi Tahoori Xiaoyang Zeng State Key Lab. ASIC & System School of Microelectronics Fudan University Shanghai China Chair of Dependable Nano Computing Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Soft error is a major reliability concern in advanced technology nodes. Although mitigating Soft Error Rate (SER) will inevitably sacrifice area and power, few studies paid attention to optimization methods to explore trade-offs between area, power and SER. This paper proposes an optimization framework based on Bayesian approach for soft-error-tolerant circuit design. It comprises two steps:1) data preprocessing and 2) Bayesian optimization. In the preprocessing step, a strategy incorporating k-means algorithm and a novel sequencing algorithm is used to cluster Flip-Flops (FFs) with similar SER in order to reduce the dimensionality for the subsequent step. Bayesian Neural Network (BNN) is the applied surrogate model for acquiring the posterior distribution of three design metrics, while the Lower confidence bound (LCB) functions are employed as acquisition functions to select the next point based on BNN when optimizing. Finally, the non-dominated sorting genetic algorithm (NSGA-II) is used to search the Pareto Optimal Front (POF) solutions of three LCB functions. Experimental results demonstrate the proposed framework has a 1.4x improvement in accuracy and a 70% reduction in SER with acceptable increases in power and area.

关键词： Optical fibers Sequential analysis Neural networks Clustering algorithms Pareto optimization Bayes methods Circuit synthesis

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A 28nm 1.5Mb Embedded 1T2R RRAM with 14.8 Mb/mm2 using Sneaking Current Suppression and Compensation Techniques

A 28nm 1.5Mb Embedded 1T2R RRAM with 14.8 Mb/mm2 using Sneak...

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Symposium on VLSI Circuits

作者： Jianguo Yang Xiaoyong Xue Xiaoxin Xu Hangbing Lv Feng Zhang Xiaoyang Zeng Meng-Fan Chang Ming Liu Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics of the Chinese Academy of Sciences China State Key Laboratory of ASIC and System Fudan University China University of Chinese Academy of Sciences Department of Electrical Engineering National Tsing Hua University Taiwan

ISBN: (数字)9781728199429

ISBN: (纸本)9781728199436

For the first time, 1T2R RRAM cell using PMOS selector is proposed and demonstrated with improved reset voltage and high density for embedded NVM. Hierarchical bit line and 3-state cell storage confine the associated sneaking current locally within a small zone and further suppress it by >90%. Self-adaptive write driver with current limiter and sneaking current compensator realizes power saving and accurate current compliance for set. To suit PMOS selector, reverse read using current sensing with dummy reference brings fast and reliable read. A 1.5Mb RRAM test chip in 28nm improved the record storage density by 40% to 14.8 Mb/mm 2 . Reliable read is performed at low VDD of 0.6V@(-40, 125)°C.

关键词： Computer architecture Microprocessors Resistors Current limiters MOS devices Resistance Transistors

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A digitalized RRAM-based Spiking Neuron Network system with 3-bit weight and unsupervised online learning scheme

A digitalized RRAM-based Spiking Neuron Network system with ...

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International Conference on asic

作者： Danqing Wu Shilin Yan Haodi Tang Yu Wang Jiayun Feng Xianwu Hu Jiaxin Cao Yufeng Xie State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China

Resistive-switching Random Access Memory (RRAM) has emerged as a promising candidate for the artificial synaptic in neuromorphic computation circuits due to its similar electronic characteristics with the synaptic and features such as high integration density, non-volatile retention and supporting matrix-vector multiplication. In this paper, a digitalized RRAM-based fully-connected Spiking Neuron Network (SNN) system with 3-bit weight and unsupervised online learning scheme is proposed. It consists of 64 pre-neurons and 10 post-neurons, all the neurons are realized by digital circuits for low area overhead, low power consumption and high accuracy. An unsupervised online learning scheme based on binary STDP protocol is applied to train the synaptic weights. Experiments show that the system can be used to recognize the learned ten handwritten digits efficiently.

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An Efficient Memory Partitioning Approach for Multi-Pattern Data Access in STT-RAM

An Efficient Memory Partitioning Approach for Multi-Pattern ...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Binbin Liu Fan Yang Dian Zhou Xuan Zeng State Key Lab of ASIC & System School of Microelectronics Fudan University China Department of Electrical Engineering University of Texas at Dallas Richardson TX U.S.A.

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper presents an efficient memory partitioning algorithm for multi-pattern data access in Spintransfer torque random access memory (STT-RAM). The algorithm generates check patterns to solve read disturbance problem while ensuring multi-pattern data access in parallel. The implementation results imply that our proposed approach highly outperforms state-of-the-art algorithm in terms of bank number, searching time, performance and storage overhead.

关键词： Partitioning algorithms Arrays Memory management Random access memory Schedules Field programmable gate arrays Torque

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An Efficient Accelerator for Sparse Convolutional Neural Networks

An Efficient Accelerator for Sparse Convolutional Neural Net...

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International Conference on asic

作者： Weijie You Chang Wu State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China

In this paper, we propose a sparse convolutional neural network accelerator design on FPGAs. Similar to the DNNWEAVER architecture, our accelerator uses two-level hierarchy: multiple Processing Units (PUs) and each PU comprises a set of Processing Elements (PEs). To address the irregularity of sparse neural networks, we introduce a novel sparse dataflow for sparse CNN computing as well as weight merging method to balance the computation load on different PUs for better overall efficiency. We implement our design with 32 PUs and 14 PEs in each PU. When compared with DNNWEAVER on VGG16 network, our accelerator achieves 3.49× speedup and 3.05× energy saving on average when running at 150MHz on a Xilinx ZC706 board and reaches the speed of 400 GOPS.

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Mos2 transistor gated by PMMA-based electrolyte for sub-1 V operation

Mos2 transistor gated by PMMA-based electrolyte for sub-1 V ...

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International Conference on asic

作者： Hongwei Tang Fuyou Liao Xinzhi Zhang Jianan Deng Jing Wan Wenzhong Bao State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China State Key Laboratory of ASIC and System School of Information Science and Engineering Fudan University Shanghai China

The fabrication of high-performance MoS 2 transistor with a reliable gate dielectric layer remains an obstacle due to the lack of dangling bonds for dielectric deposition. Here, we demonstrate the employment of PMMA-based electrolyte film as the gate dielectric for Mos2 transistors. The electrolyte can be utilized to form high quality and uniform thin film by spin-coating method at room temperature. Based on this PMMA-based electrolyte film, top-gated Mos2 field effect transistors (FETs) are fabricated and exhibit exceptional device performance. The ultra-high capacitance of the electrolyte, resulting from the formation of an electric double layer (EDL), yields a current on/off ratio about 10 5 and a steep subthreshold swing (90 mV/dec) within a low supply voltage (1 V). Furthermore, we investigate the dynamic response of a Mos2 inverter with a switching speed over 100 Hz. This result provides the possibility for realizing low power logic circuit and nano-electronics.

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An Artificial Neutral Network Chip Based on Two-Dimensional Semiconductor

Research Square

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Research Square 2020年

作者： Ma, Shunli Wu, Tianxiang Chen, Xinyu Wang, Yin Tang, Hongwei Yao, Yuting Wang, Yan Deng, Jianan Wan, Jing Sun, Zhengzong Xu, Zihan Riaud, Antoine Wu, Chenjian Chai, Yang Zhou, Peng Ren, Junyan Bao, Wenzhong Zhang, David Wei State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China State Key Laboratory of ASIC and System School of Information Science and Technology Fudan University Shanghai200433 China Shenzhen Sixcarbon Technology 188 Jiangshi Road Shenzhen518106 China School of Electronic and Information Engineering Soochow University Suzhou China Department of Applied Physics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Two-dimensional semiconductors can be used to build integrated circuits for running artificial neural networks (ANN) with higher energy efficiency. The implementation of an ANN with 2D semiconductors has been held back by the large-scale and high-quality transistors required for running machine learning algorithms. Here we demonstrate the first functional MoS2 analog ANN integrated circuit, including memory, multiply-and-accumulate (MAC), activation function, and weight update circuits. The ANN integrated circuit is realized through 818 field effect transistors (FETs) with wafer-scale and high-homogeneity MoS2 film. The large current on/off ratio and output linearity of these MoS2 FETs allow the realization of convolutional and activation function circuits with a few number of transistors. This ANN can be used for recognizing tactile digit, showing the recognition rate exceeding 97%. Our work demonstrates wafer-scale processing of a 2D semiconductor for building integrated circuits with the functions of AI computation. © 2020, CC BY.

关键词： Timing circuits

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Separation of particles and bio-samples by tilted-angle standing surface acoustic waves: A theoretical analysis

arXiv

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arXiv 2019年

作者： Wang, Wei Riaud, Antoine ASIC and System State Key Laboratory School of Microelectronics Fudan University Shanghai200433 China

Separation of particles and bio-samples by the standing surface acoustic wave (SSAW) has attracted considerable interest in many biological protocols. In this work, we thoroughly analyze the behavior of particles in an SSAW field that is introduced at a random angle to the flow direction, numerically and analytically. An explicit transition threshold is deduced by the acoustic to drag force ratio between the drift mode and the locked mode, while the trajectories of particles in both of these modes are demonstrated in an analytical way. The deviation in the drift mode is revealed fundamentally nonlinear, whose slope is proportional to the sixth power of the particle radius, the fourth power of the acoustic pressure, the square of the acoustic contrast factor, and reciprocal of the square of the flow velocity. Our analytical formula shows a good agreement with numerical calculations and experimental data even in the transition regime. It provides a basis for understanding how particles and bio-samples behave in an SSAW field, and an approach to effectively evaluate the design of the acoustophoretic system. Copyright © 2019, The Authors. All rights reserved.

关键词： Acoustic waves

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Structural disorder by octahedral tilting in inorganic halide perovskites: New insight with Bayesian optimization

arXiv

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arXiv 2023年

作者： Li, Jingrui Pan, Fang Zhang, Guo-Xu Liu, Zenghui Dong, Hua Wang, Dawei Jiang, Zhuangde Ren, Wei Ye, Zuo-Guang Todorović, Milica Rinke, Patrick Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education International Center for Dielectric Research School of Electronic Science and Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology Xi’an Jiaotong University Xi’an710049 China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin150001 China Key Laboratory for Physical Electronics and Devices of the Ministry of Education Shaanxi Key Lab of Information Photonic Technique School of Electronic Science and Engineering Xi’an Jiaotong University Xi’an710049 China School of Microelectronics Key Lab of Micro-Nano Electronics and System Integration of Xi’an City Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology Xi’an Jiaotong University Xi’an710049 China Department of Chemistry and 4D LABS Simon Fraser University British Columbia BurnabyV5A 1S6 Canada Department of Mechanical and Materials Engineering University of Turku TurkuFI-20014 Finland Department of Applied Physics Aalto University AALTO P.O.Box 11100 FI-00076 Finland

Structural disorder is common in metal-halide perovskites and important for understanding the functional properties of these materials. First-principles methods can address structure variation on the atomistic scale, but they are often limited by the lack of structure-sampling schemes required to characterize the disorder. In this work, structural disorder in the benchmark inorganic halide perovskites CsPbI3 and CsPbBr3 is computationally studied in terms of the three octahedral-tilting angles. The consequent variation in energetics and properties are described by three-dimensional potential-energy surfaces (PESs) and property landscapes, delivered by Bayesian Optimization Structure Search method with integrated density-functional-theory (DFT) calculations. The rapid convergence of the PES with about 200 DFT data points in three-dimensional searches demonstrates the power of active learning and strategic sampling with Bayesian optimization. Further analysis indicates that disorder grows with increasing temperature, and reveals that the materials band gap at finite temperatures is a statistical mean over disordered structures. © 2023, CC BY.

关键词： Lead compounds

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