Bio-inspired Neuromorphic Computing Using Memristor Crossbar Networks

Bio-inspired neuromorphic computing systems built with emerging devices such as memristors have become an active research field. Experimental demonstrations at the network-level have suggested memristor-based neuromorphic systems as a promising candidate to overcome the von-Neumann bottleneck in future computing applications. As a hardware system that offers co-location of memory and data processing, memristor-based networks represent an efficient computing platform with minimal data transfer and high parallelism. Furthermore, active utilization of the dynamic processes during resistive switching in memristors can help realize more faithful emulation of biological device and network behaviors, with the potential to process dynamic temporal inputs efficiently. In this thesis, I present experimental demonstrations of neuromorphic systems using fabricated memristor arrays as well as network-level simulation results. Models of resistive switching behavior in two types of memristor devices, conventional first-order and recently proposed second-order memristor devices, will be first introduced. Secondly, experimental demonstration of K-means clustering through unsupervised learning in a memristor network will be presented. The memristor based hardware systems achieved high classification accuracy (93.3%) on the standard IRIS data set, suggesting practical networks can be built with optimized memristor devices. Thirdly, implementation of a partial differential equation (PDE) solver in memristor arrays will be discussed. This work expands the capability of memristor-based computing hardware from ‘soft’ to ‘hard’ computing tasks, which require very high precision and accurate solutions. In general first-order memristors are suitable to perform tasks that are based on vector-matrix multiplications, ranging from K-means clustering to PDE solvers. On the other hand, utilizing internal device dynamics in second-order memristors can allow natural emulation of biological behaviors and enable network functions such as temporal data processing. An effort to explore second-order memristor devices and their network behaviors will be discussed. Finally, we propose ideas to build large-size passive memristor crossbar arrays, including fabrication approaches, guidelines of device structure, and analysis of the parasitic effects in larger arrays.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147610/1/yjjeong_1.pd

Uniaxial Strain Effects on Silicon Nanowire MOSFETs and Single-Electron/Hole Transistors at Room-Temperature

報告番号: ; 学位授与年月日: 2009-03-23 ; 学位の種別: 修士 ; 学位の種類: 修士(工学) ; 学位記番号: ; 研究科・専攻: 工学系研究科電子工学専

The experience of device failure after cochlear implantation

Abstract Background The present study describes the treatment of patients at a tertiary institution who experienced device failure after Cochlear Implantation (CI), as well as identifying prodromic symptoms that could assist in the timely identification and management of device failure. Study design Retrospective database review (January 2000–May 2017). Setting Single tertiary hospital. Methods Factors recorded included the etiology of hearing loss; age at first and revision CI surgeries; surgical information, including operation time and approach; electrical outcomes after implantation; device implanted; symptoms of device failure; history of head trauma; and audiologic outcomes as determined by categories of auditory performance (CAP). Results From January 2000 to May 2017, 1431 CIs were performed, with 27 (1.9%) undergoing revision surgeries due to device failure. The most common etiology of hearing loss was idiopathic (12/27), followed by cochlear hypoplasia (5/27). Mean age at initial CI was 11.8 (1–72) years, with 21 being pre-lingual and 6 being post-lingual. Of the total devices initially implanted, 80.5% were from Cochlear, 15.9% from MED-EL, and 3.5% from Advanced Bionics. The failure rates of these devices were 1.3%, 3.1%, and 10.0%, respectively. The most suggestive symptom of device failure was intermittent loss of signal. Mean CAP scores were 5.17 before reimplantation and 5.54 and 5.81 at 1- and 3-years, respectively, after reimplantation. Conclusion The most suggestive symptom preceding device failure was intermittent loss of signal. Patients who present with this symptom should undergo electrical examination for suspected device failure. Audiologic outcomes showed continuous development despite revision surgeries

Laser-Assisted Nanotexturing for Flexible Ultrathin Crystalline Si Solar Cells

Ultrathin (UT) crystalline Si wafers, which are more flexible than conventional ones, can apply to curved surfaces, enabling a wide range of applications such as building-integrated photovoltaics, vehicle-integrated photovoltaics, and wearable devices. Thinner wafers require more effective light trapping; thus, surface texturing in microscale is a common approach to compensate for the reduced thickness by enhancing the light pathlength. Microscale textures, however, deteriorate the mechanical flexibility due to stress concentration in the valley of the microtextures. In this study, a laser-assisted nanotexturing process is proposed for enhanced flexibility of the UT Si solar cells with a 50 & mu;m thickness while maintaining light-trapping performances. A nanolens array is used to focus laser onto the Si wafers, inducing the formation of nanoparticle etch masks for nanopyramid texturing in an alkaline solution. The origin of the enhanced flexibility of the nanotextured Si wafers is discussed by a micromechanics simulation study. Herein, nanotexturing technique is applied to UT Si-based passivated emitter rear cells and the enhanced flexibility of the cells with a 26 mm critical bending radius is demonstrated. Also, it is shown that the nanotextured Si wafer provides a higher efficiency of 18.68%, whereas the microtextured one exhibits 18.10%

<i>K</i>‑means Data Clustering with Memristor Networks

Memristor-based neuromorphic networks have been actively studied as a promising candidate to overcome the von-Neumann bottleneck in future computing applications. Several recent studies have demonstrated memristor network’s capability to perform supervised as well as unsupervised learning, where features inherent in the input are identified and analyzed by comparing with features stored in the memristor network. However, even though in some cases the stored feature vectors can be normalized so that the winning neurons can be directly found by the (input) vector–(stored) vector dot-products, in many other cases, normalization of the feature vectors is not trivial or practically feasible, and calculation of the actual Euclidean distance between the input vector and the stored vector is required. Here we report experimental implementation of memristor crossbar hardware systems that can allow direct comparison of the Euclidean distances without normalizing the weights. The experimental system enables unsupervised <i>K</i>-means clustering algorithm through online learning, and produces high classification accuracy (93.3%) for the standard IRIS data set. The approaches and devices can be used in other unsupervised learning systems, and significantly broaden the range of problems a memristor-based network can solve