arXiv subjects

Yuan Dai

Publications and source records attributed to Yuan Dai.

4 recordsLinked to original sources

arXiv2022

Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, the tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64x64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch), by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ~385 kPa-1 for MWCNTs concentration of 6%, while the 14% one exhibited fast response time of ~3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could pave the road to building large-scale intelligent sensor networks for next-generation smart robotics.

cond-mat.mtrl-sci↗

arXiv2022

SA-DNet: A on-demand semantic object registration network adapting to non-rigid deformation

As an essential processing step before the fusing of infrared and visible images, the performance of image registration determines whether the two images can be fused at correct spatial position. In the actual scenario, the varied imaging devices may lead to a change in perspective or time gap between shots, making significant non-rigid spatial relationship in infrared and visible images. Even if a large number of feature points are matched, the registration accuracy may still be inadequate, affecting the result of image fusion and other vision tasks. To alleviate this problem, we propose a Semantic-Aware on-Demand registration network (SA-DNet), which mainly purpose is to confine the feature matching process to the semantic region of interest (sROI) by designing semantic-aware module (SAM) and HOL-Deep hybrid matching module (HDM). After utilizing TPS to transform infrared and visible images based on the corresponding feature points in sROI, the registered images are fused using image fusion module (IFM) to achieve a fully functional registration and fusion network. Moreover, we point out that for different demands, this type of approach allows us to select semantic objects for feature matching as needed and accomplishes task-specific registration based on specific requirements. To demonstrate the robustness of SA-DNet for non-rigid distortions, we conduct extensive experiments by comparing SA-DNet with five state-of-the-art infrared and visible image feature matching methods, and the experimental results show that our method adapts better to the presence of non-rigid distortions in the images and provides semantically well-registered images.

cs.CV↗

arXiv2016

SANTA: Self-Aligned Nanotrench Ablation via Joule Heating for Probing Sub-20 nm Devices

Manipulating materials at the nanometer scale is challenging, particularly if alignment with nanoscale electrodes is desired. Here we describe a lithography-free, self-aligned nanotrench ablation (SANTA) technique to create nanoscale trenches in a polymer like poly(methyl) methacrylate (PMMA). The nanotrenches are self-aligned with carbon nanotube (CNT) and graphene ribbon electrodes through a simple Joule heating process. Using simulations and experiments we investigate how the Joule power, ambient temperature, PMMA thickness, and substrate properties can improve the spatial resolution of this technique. We achieve sub-20 nm nanotrenches for the first time, by lowering the ambient temperature and reducing the PMMA thickness. We also demonstrate a functioning nanoscale resistive memory (RRAM) bit self-aligned with a CNT control device, achieved through the SANTA approach. This technique provides an elegant and inexpensive method to probe nanoscale devices using self-aligned electrodes, without the use of conventional alignment or lithography steps.

cond-mat.mes-hall↗

arXiv2015

Nanoscale Phase Change Memory with Graphene Ribbon Electrodes

Phase change memory (PCM) devices are known to reduce in power consumption as the bit volume and contact area of their electrodes are scaled down. Here, we demonstrate two types of low-power PCM devices with lateral graphene ribbon electrodes: one in which the graphene is patterned into narrow nanoribbons and the other where the phase change material is patterned into nanoribbons. The sharp graphene "edge" contacts enable switching with threshold voltages as low as ~3 V, low programming currents (<1 μA SET, <10 μA RESET) and ON/OFF ratios >100. Large-scale fabrication with graphene grown by chemical vapor deposition also enables the study of heterogeneous integration and that of variability for such nanomaterials and devices.

cond-mat.mes-hall↗