新型匹配负载网络的TLP测试系统研究

研究了传统传输线脉冲测试波形的失真机理,实现了一种基于R-2R网络的负载电路匹配特性优化设计方案,与传统传输线脉冲测试波形相比,优化之后的系统消除了传统设备所产生脉冲波形的过冲和振荡现象.从而提高了传输线脉冲测试效率.同时,在相同的预充电电压下,优化之后的系统能提供更大能量的测试脉冲,减小系统功耗.该工作对半导体器件的ESD保护电路设计和ESD模型研究具有实用价值.     

基于FPGA的脉冲神经网络模型设计与实现

现有的脉冲神经网络模型软件模拟通常具有处理速度慢、功耗高的缺点,同时利用硬件电路实现则具有开发难度大、灵活性差的缺点.为了探索合理实现脉冲神经网络模型的途径,在己有研究成果的基础上综合考虑两种方案的优缺点,提出了利用软件库模拟脉冲神经元数学模型以及网络的拓扑结构、并将网络运行时的关键计算任务以计算内核的方式交由基于OpenCL的FPGA并行计算的新思路.主要工作为:使用模块开发方式对脉冲神经网络软件开发库和OpenCL开发库进行了扩展、并将软件开发库中的重要模块重构成FPGA计算内核,使得软件开发库能够调用FPGA执行计算任务,最终达到利用两个库构建运行网络模型时能够同时满足易于开发、灵活性高、处理速度快、功耗低等要求的目的.基于MNIST图像数据集的图像分类实验表明,同一网络模型拓扑结构下,与在GPU上的软件模拟相比,提出方案的图像分类准确率并没有下降,同时以略微牺牲运行性能为代价,参考功率降低了约63.6%.     

单晶金纳米真空隧道结的超快电子发射研究

超快脉冲激光激发的纳米真空器件能够同时实现高频率和低功耗,并且有望将电子器件响应时间推进至飞秒甚至阿秒量级,从而进一步提高器件的工作频率,是未来高频电子器件的重要技术路线。本文利用原子级平整的单晶金设计并制备了一种基于领结型(bowtie)纳米隧道结的新型电子隧穿器件。重点研究了器件静态和超快激光激发的电子发射性能,利用模拟计算研究了bowtie结构对电子发射性能的影响,深入分析了器件的光电子发射机制,实现了具有四次幂的高非线性多光子发射电流,有望实现新型超快纳米真空电子器件。     

基于压差控制的脉冲除尘控制仪的设计

介绍了脉冲除尘器的工作原理,给出了基于单片机的脉冲反吹除尘控制仪主要电路的设计方案与实现方法,简要叙述了软件实现的主要模块。该控制仪结构简单、智能化程度高、功耗低,在工业生产领域具有广泛的应用价值。     

基于混沌脉冲开关键控的超宽带通信方案

该文基于混沌脉冲位置调制(CPPM)提出了一种新型可用于超宽带冲击无线电(UWB-IR)的通信方案混沌脉冲开关键控。该方案克服了混沌脉冲位置调制方法存在的问题,具有保密性好、可靠性高、设备的实现更加简单等优点。分析和仿真表明,在非理想定时情况下,该文提出的新系统具有比CPPM更好的解码性能。     

一种高速低功耗BiCMOS脉冲式触发器的通用结构

BiCMOS电路兼具CMOS电路高集成度,低功耗的优点和双极型电路高速大驱动能力的优势,已成为目前国际学术界研究的热点之一。本文提出了一种基于BiCMOS工艺的新型脉冲式触发器的通用结构和设计方法,并设计了两种结构简单的BiCMOS脉冲式D型触发器。应用TSMC 180nm工艺,采用HSPICE模拟表明:所设计的BiCMOS脉冲式D型触发器不仅具有正确的逻辑功能,而且具有高速低功耗大驱动能力的优点,与已有文献提出的BiCMOS D型触发器相比,功耗和PDP均有大幅度降低。     

纳秒大幅度快速脉冲发生器的设计

使用普通的晶体二极管或三极管很难获得超高速脉冲信号,特别是用于测量晶体管开关时间参数的激励快沿脉冲信号,文章基于对晶体管时间参数测量装置的研究,给出利用快速开关器件--阶跃恢复二极管实现纳秒大幅度正负快速脉冲发生器设计方案和各部分电路的设计方法,用此方案研制的脉冲发生器输出前沿速度快、幅度高、过冲小且电路结构简单,具有通用性.     

新型亚纳秒切断半导体开关器件研制

介绍了一种新型亚纳秒切断全固态高功率脉冲开关器件,阐述了其工作机理。该器件利用器件体内等离子体特殊的恢复过程来完成电流的迅速切断。采用多层Si片叠加的制造工艺技术,单个器件反向工作峰值电压大于2 kV,电流切断时间小于800 ps,脉冲峰值功率可达80 kW。该器件具有易串并联、抖动小等优点。基于该器件制作的脉冲发生器具有体积小、可靠性高、使用寿命长、脉冲重复频率高、输出波形稳定等特点。当采用器件串联电路连接时,可以获得几十千伏以上的高压快速脉冲。采用该器件制作的脉冲发生器在国防、医疗等领域具有广阔的应用前景。     

具有艾宾浩斯遗忘规则的忆阻联想记忆电路

忆阻器由于具有低功耗、记忆能力和纳米尺寸等特点,是实现人工神经突触的理想器件。为构建简洁、高效、功能全面地联想记忆电路,该文首先提出一种简单的神经元电路和基于压控阈值忆阻器的突触电路。然后根据巴甫洛夫联想记忆模型,设计了相应的联想记忆电路。电路结构简单,仅包含3个神经元电路和突触电路,可有效降低网络复杂度和功耗。尤为重要的是该电路可以模拟全功能的联想记忆行为,不但实现了学习、遗忘、加速学习、减速遗忘以及减速自然遗忘等功能,而且学习速率和自然遗忘速率能够根据学习的次数自动调整,使电路更具仿生性。此外,该电路与艾宾浩斯遗忘曲线相吻合,扩大了电路的适用范围。     

太赫兹脉冲整形技术研究进展

太赫兹(THz)脉冲整形技术在量子理论、生物医学成像、安全检查、亚毫米波通信等领域都具有重要的学术价值和应用前景。概述了基于飞秒脉冲的整形技术和新型太赫兹辐射材料整形技术,分析了太赫兹脉冲整形器件整形技术目前的研究状况,并对各种整形方法的发展进行了展望。     

Redox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing

The spiking neural network(SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CM...     

Electrolyte-gated transistors for neuromorphic applications

Von Neumann computers are currently failing to follow Moore’s law and are limited by the von Neumann bottleneck.To enhance computing performance,neuromorphic computing systems that can simulate the function of the human brain are being developed.Artificial synapses are essential electronic devices for neuromorphic architectures,which have the ability to perform signal processing and storage between neighboring artificial neurons.In recent years,electrolyte-gated transistors(EGTs)have been seen as promising devices in imitating synaptic dynamic plasticity and neuromorphic applications.Among the various electronic devices,EGT-based artificial synapses offer the benefits of good stability,ultra-high linearity and repeated cyclic symmetry,and can be constructed from a variety of materials.They also spatially separate“read”and“write”operations.In this article,we provide a review of the recent progress and major trends in the field of electrolyte-gated transistors for neuromorphic applications.We introduce the operation mechanisms of electric-double-layer and the structure of EGT-based artificial synapses.Then,we review different types of channels and electrolyte materials for EGT-based artificial synapses.Finally,we review the potential applications in biological functions.     

2D Material Based Synaptic Devices for Neuromorphic Computing

The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel computing primitives are required. Brain inspired neuromorphic computing systems, capable of combining analog computing and data storage at the device level, have drawn great attention recently. In addition, the basic electronic devices mimicking the biological synapse have achieved significant progress. Owing to their atomic thickness and reduced screening effect, the physical properties of 2D materials could be easily modulated by various stimuli, which is quite beneficial for synaptic applications. In this article, aiming at high-performance and functional neuromorphic computing applications, a comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of 2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications. Challenges and strategies for the future development of 2D synaptic devices are also discussed. This review will provide an insight into the design and preparation of 2D synaptic devices and their applications in neuromorphic computing.     

Linear Classification Function Emulated by Pectin-Based Polysaccharide-Gated Multiterminal Neuron Transistors

Neuromorphic computing, which merges learning and memory functions, is a new computing paradigm surpassing traditional von Neumann architecture. Apart from the plasticity of artificial synapses, the simulation of neurons’ multi-input signal integration is also of great significance to realize efficient neuromorphic computing. Since the structure of transistors and neurons is strikingly similar, capacitively coupled multi-terminal pectin-gated oxide electric double layer transistors are proposed here as artificial neurons for classification. In this work, the free logic switching of “AND” and “OR” is realized in the device with triple in-plane gates. More importantly, the linear classification function on a single neuron transistor is demonstrated experimentally for the first time. All the results obtained in this work indicate that the prepared artificial neuron can improve the efficiency of artificial neural networks and thus will play an important role in neuromorphic computing.     

Ultrasensitive Freestanding and Mechanically Durable Artificial Synapse with Attojoule Power Based on Na-Salt Doped Polymer for Biocompatible Neuromorphic Interface

The human brain, with high energy-efficient and parallel processing ability, inspires to mitigate power issues perplexing von Neumann architecture. As one of the essential components constructing the human brain, the emulation of biological synapses exploiting electronic devices consuming power at a biological level lays the foundation for the implementation of energy-efficient neuromorphic computing. Besides, signal matching between biologically-related stimuli and the driving voltage of artificial synapses helps to realize intelligent neuromorphic interfaces and sustainable energy. Here, ultra-sensitive artificial synapse stimulated at 1 mV with energy consumption of 132 attojoule/synaptic event is demonstrated. Biological signal matching and low power application are realized simultaneously based on sodium acetate (NaAc) doped polyvinyl alcohol (PVA) electrolyte. The biphasic current, which comprises the electrical- and ion-mediation current component, contributes to enrich synaptic functions compared to monophasic synaptic behavior. Moreover, freestanding NaAc-doped PVA membrane functions as both dielectric layer and mechanical support and facilitates to achieve flexible, transferable artificial synapse, which maintains functional stability at an ultralow voltage and power even after bending tests. Thus, encompassing superior sensitivity, low energy, and multiple functionalities with flexible, self-supported, biocompatible property, takes a step to construct energetically-efficient, complex neuromorphic systems for wearable, implantable medicines as well as smart bio-electronic interfaces.     

Synaptic plasticity modulation and coincidence detection emulated in multi-terminal neuromorphic transistors

Human brain is a powerful biological computer that can processing a large number of cognitive tasks simultaneously. Inspired by our brain, many emerging devices have been developed for neuromorphic computing and perception in recent years. Due to the interfacial electron/ion coupling, electric-double-layer (EDL) transistors gated by electrolytes are promising candidates for neuromorphic devices. Here, we demonstrate a multi-terminal indium-tin-oxide (ITO)-based EDL transistor gated by chitosan electrolyte and this device exhibits good electrical properties. Short-term synaptic plasticity modulation and neuron functions (temporal integration, coincidence detection) are investigated. Our results indicate that oxide-based EDL transistors are promising for neuromorphic application.     

Electrolyte‐Gated Synaptic Transistor with Oxygen Ions

Artificial synaptic devices are the essential hardware of neuromorphic computing systems, which can simultaneously perform signal processing and information storage between two neighboring artificial neurons. Emerging electrolyte‐gated transistors have attracted much attention for efficient synaptic emulation by using an addition gate terminal. Here, an electrolyte‐gated synaptic device based on the SrCoO_x (SCO) films is proposed. It is demonstrated that the reversible modulation of SCO phase transforms the brownmillerite SrCoO_2.5 and perovskite SrCoO_3?δ , through controlling the insertion and extraction of oxygen ions with electrolyte gating. Nonvolatile multilevel conduction states can be realized in the SCO films following this route. The synaptic functions such as the long‐term potentiation and depression of synaptic weight, spike‐timing‐dependent plasticity, as well as spiking logic operations in the device are successfully mimicked. These results provide an alternative avenue for future neuromorphic devices via electrolyte‐gated transistors with oxygen ions.     

Sub-Femtojoule-Energy-Consumption Conformable Synaptic Transistors Based on Organic Single-Crystalline Nanoribbons

Inspired from powerful functionalities of human brain, artificial synapses are innovated continuously for the construction of brain-like neuromorphic electronics. The quest to rival the ultralow energy consumption of biological synapses has become highly compelling, but remains extremely difficult due to the lack of appropriate materials and device construction. In this study, organic single-crystalline nanoribbon active layer and elastic embedded photolithographic electrodes are first designed in synaptic transistors to reduce energy consumption of single device. The minimum energy consumption (0.29 fJ) of one synaptic event is far lower than that of biological synapse (10 fJ). Notably, sub-femtojoule-energy-consumption synaptic transistors can simulate various biological plastic behaviors even under different tensile and compressive strains, offering a new guidance for the construction of ultralow-energy-consuming neuromorphic electronic devices and the development of flexible artificial intelligence electronics in the future.     

Spatially-correlated neuron transistors with ion-gel gating for brain-inspired applications

In this paper, ion-gel gated transistors based on solution-processed indium-gallium-zinc-oxide (In-Ga-Zn-O) semiconductors were fabricated. These transistors consisted of a spatial distribution configuration of multi-in-plane gates. Spike pluses applied on multi-in-plane gates are analogy to massive synaptic inputs from various dendritic positions. The basic neuromorphic functions, such as potentiation or depression behaviors, synaptic plasticity and frequency-dependent filtering, were demonstrated in these devices by applied a spiking on an in-plane gate. The output signal of neuromorphic devices is greatly relevant to the gate position-correlated input signal and the spatially-correlated information processing could advance the capacity of neuromorphic performance. Orientation selectivity was a broadly investigated phenomenon. More importantly, by using the spatial summation functions of dendritic integration, the orientation identification was successfully realized in our transistor with multi-in-plane gates. The spatially-correlated neuromorphic devices are exceedingly promising for the neural information processing and sensing.     

A Flexible Mott Synaptic Transistor for Nociceptor Simulation and Neuromorphic Computing

Designing transparent flexible electronics with multi-biological neuronal functions and superior flexibility is a key step to establish wearable artificial intelligence equipment. Here, a flexible ionic gel-gated VO₂ Mott transistor is developed to simulate the functions of the biological synapse. Short-term and long-term plasticity of the synapse are realized by the volatile electrostatic carrier accumulation and nonvolatile proton-doping modulation, respectively. With the achievement of multi-essential synaptic functions, an important sensory neuron, nociceptor, is perfectly simulated in our synaptic transistors with all key characteristics of threshold, relaxation, and sensitization. More importantly, this synaptic transistor exhibits high tolerance to the bending deformation, and the cycle-to-cycle variations of multi-conductance states in potentiation and depression properties are maintained within 4%. This superior stability further indicates that our flexible device is suitable for neuromorphic computing. Simulation results demonstrate that high recognition accuracy of handwritten digits (>95%) can be achieved in a convolution neural network built from these synaptic transistors. The transparent and flexible Mott transistor based on electrically-controlled VO₂ metal-insulator transition is believed to open up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems.