News

[August-2015] Dr. Yuchao Yang joined the faculty of Peking University

posted May 22, 2017, 1:35 PM by Wei Lu


[June-2016] Dr. Bing Chen joined the faculty of Zhejiang University

posted May 22, 2017, 1:31 PM by Wei Lu   [ updated May 22, 2017, 1:37 PM ]


[28-November-2016] Chao Du defended his PhD Thesis. Congratulations to Dr. Du!

posted May 22, 2017, 1:28 PM by Wei Lu   [ updated May 23, 2017, 3:01 PM by Fuxi Cai ]

Metal oxide memristors, a two-­‐terminal nanoscale semiconductor device whose resistance/conductance can be regulated according to the history of applied stimulations, are initially proposed as a promising candidate for the next generation non-­‐volatile memory. Bearing the similarity to the weight change of synapses in human brain, they are recently being intensively investigated as a critical component in neural network for neuromorphic applications. 

The resistive switching mechanism is attributed to the redistribution of oxygen vacancies under electric field and spontaneous diffusion. Based on this understanding, 2nd order switching dynamics is discovered and thoroughly investigated for the first time in both WOx memristor and Ta2O5-­‐TaOx memristor and more comprehensive resistive switching models are proposed to quantitively capture the internal ionic dynamics. The dynamics is utilized to implement important synaptic functions including paired pulse facilitation, spike-­‐timing dependent plasticity, experience dependent plasticity, in single cell and in a bio-­‐realistic fashion. WOx memristor crossbar network is used to implement several important neuromorphic applications including: 1) sparse coding, as the network can easily conduct matrix operation, especially dot product and the resistance of each cell at the crosspoint can be regulated to store information needed for computation, 2) temporal information processing through memristor-­‐based liquid state machine, as WOx memristor has the ability to process temporal information due to its short-­‐term memory which is caused by its spontaneous decay characteristics. Improvement of both single cell performance towards better synaptic behaviors and memristor crossbar network performance for large scale applications are achieved by the optimization of fabrication methods.

[17-August-2016] Jiantao defended his PhD Thesis. Congratulations to Dr. Zhou!

posted May 22, 2017, 1:26 PM by Wei Lu   [ updated May 23, 2017, 2:59 PM by Fuxi Cai ]

The demand for data storage - from mobile devices to enterprise applications - has been driving the explosive development of non-volatile memories (NVMs). However, as the Moore’s Law approaches its fundamental limit, the aggressive scaling of Flash memory is faced with challenges from technical issues and economic concerns. Therefore, the next-generation NVM solution is urgently desired. Among various candidates, resistive switching memory (RRAM) has attracted broad interest due to its simple structure, high speed, long retention, excellent endurance and low power. 

In this dissertation, we focus on RRAMs and related reconfigurable devices. Through simulation, we systematically discuss the sneak-path issue of passive RRAM arrays and benchmark the selector devices for large-scale crossbar integration. Next, we develop a tantalum oxide (TaO x ) selector device showing high nonlinearity and good uniformity. To meet the requirement of energy-constrained applications, we further develop ultralow-current RRAM cells with self-rectifying characteristics and high performance. In addition,we explore coupling the ionic migration process in resistive switching with the transistor operation, and demonstrate nonvolatile conductance modulation on a novel reconfigurable device based on the LaAlO 3 /SrTiO 3 heterointerface. Finally, an energy-efficient in-memory computing architecture using crossbar RRAM arrays has been proposed to break the boundary between computing and memory and offer high parallelism.

[7-July-2016] Ugo defended his PhD Thesis. Congratulations to Dr. Otuonye!

posted May 22, 2017, 1:24 PM by Wei Lu   [ updated May 23, 2017, 3:02 PM by Fuxi Cai ]

As the demand for cheaper and faster computing continues to increase, the semiconductor industry has relied on transistor scaling to meet this demand. With transistor size approaching the atomic limit, there needs to be a fundamental change from the traditional improvement methods employed in the past. Improvement of data transfer between the microprocessor and other peripheral units could provide an immediate boost to computing speed. The bus lines connecting the CPU to other components are made up of metal interconnects. The speed of metal interconnects are highly limited due to parasitic effects. Switching to optical interconnects will eliminate most of the parasitic effects experienced with metal interconnects and will provide an immediate improvement in computing speed. 

To implement an optical interconnect system, nanoscale photodetectors and modulators will be required. We have demonstrated a single 20nm diameter germanium nanowire photodetector, with current gain of more than 10^3 and responsivity of 25A/W, operating at 1.55um wavelength. The device photocurrent is highly scalable based on the number of active nanowires connected in a parallel formation. We have also demonstrated a multilevel modulating device based on the integration of two memristors on a photonic crystal waveguide. Our device enabled the modulation of an optical channel by multiple electrical signals with distinct optical output for every combination of the modulating input signals. The demonstrated device operates at the telecommunication wavelength of 1.55um. 

Integration of nanowire photodetector and nanoscale modulator as a single device will potentially enable high-­‐speed and high-­‐density optical interconnect link between the microprocessor and other peripheral units.

Jo's memristor synapse paper cited more than 1000 times

posted Feb 16, 2016, 12:25 PM by Mohammed Zidan   [ updated Feb 19, 2016, 11:22 AM by Wei Lu ]

In March 2010, Sung Hyun ("Jo"), Kuk-Hwan and our collaborators published the first experimental study showing memristors can effectively act as synapses. The work titled “Nanoscale Memristor Device as Synapse in Neuromorphic Systems”, published in Nano Letters, has stimulated significant interest from research communities around the world and formed the basis for several research programs towards bio-inspired computing. According to Google Scholar, the manuscript has now been cited over 1000 times. The work was also featured 
in Nature, EE Times, New Scientist, PhysicsOrg, Chemistry World and other news outlets.

Paper Abstract: A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal−oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.

Congratulations to Jo and the other authors! 

Lin Chen defended his PhD Thesis

posted Feb 16, 2016, 12:01 PM by Mohammed Zidan   [ updated Feb 16, 2016, 12:08 PM ]

Lin successfully defended his PhD thesis titled "Vertical Integration of Germanium Nanowires on Silicon Substrates for Nanoelectronics
". In his dissertation he focus on Vapor-Liquid-Solid (VLS) synthesized Germanium nanowires with nanoscale size, and investigates their potential as electronic devices. We discuss a strategy to grow vertical, taper-free Ge nanowires on Si with good flexibility and controllability. Specifically, he verifies the presence of high-quality, abrupt Ge/Si heterojunction as a result of vertical integration. In addition, several single-nanowire vertical devices based on this material system are demonstrated, including heterodiode, junctionless transistor and tunnel transistor. Experimental characterization of these devices are complemented by various device models and good agreement is reached.  

Thesis Abstract: In this dissertation, we Rapid development of semiconductor industry in recent years has been primarily driven by continuous scaling, which allows for manufacturing integrated circuits with higher computing power at a reduced cost. As the size of the transistors approaches tens of nanometers, we are now faced with new technological challenges brought by aggressive scaling. To this end, unconventional semiconductor material and novel device structure have attracted  a lot of interests as promising candidates to replace MOSFET with Si channel and help extend the Moore’s law.

Congratulations Dr. Chen!

Patrick Sheridan defended his PhD Thesis

posted Feb 16, 2016, 11:52 AM by Mohammed Zidan   [ updated Feb 16, 2016, 12:08 PM ]

Patrick successfully defended his PhD thesis titled "Neuromorphic Computing with Resistive Switching Devices". His dissertation presents the investigation of tungsten oxide based resistive switching devices for use in neuromorphic computing applications. Device structure, fabrication, and integration are described and physical models are developed to describe the behavior of the devices. These models are used to develop array-scale simulations in support of neuromorphic computing approaches. Several signal processing algorithms are adapted for acceleration using arrays of resistive switches. Both simulation and experimental results are reported. Finally, guiding principles and proposals for future work are discussed.

Thesis Abstract: Resistive switches, commonly referred to as resistive memory (RRAM) devices and modeled as memristors, are an emerging nanoscale technology that can revolutionize data storage and computing approaches. Enabled by the advancement of nanoscale semiconductor fabrication and detailed understanding of the physical and chemical processes occurring at the atomic scale, resistive switches offer high speed, low-power, and extremely dense nonvolatile data storage. Further, the analog capabilities of resistive switching devices enable neuromorphic computing approaches which can achieve massively parallel computation with a power and area budget that is orders of magnitude lower than today’s conventional, digital approaches. 

Congratulations Dr. Sheridan!

[20-Apr-2015] Shinhyun Choi defended his PhD Thesis! Congratulations to Dr. Choi!

posted Feb 16, 2016, 11:42 AM by Mohammed Zidan   [ updated Feb 16, 2016, 11:42 AM ]


[14-Aug-2014] Jong Hoon Shin & YeonJoo Jeong joined the group. Welcome Jong Hoon and YeonJoo!

posted Feb 16, 2016, 11:41 AM by Mohammed Zidan   [ updated Feb 16, 2016, 11:42 AM ]


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