Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.This work was partially supported by by FEDER funds through MINECO project TIN2017-85827-P, and ELKARTEK funded projects ENSOL2 and CODISAVA2 (KK-202000077 and KK-202000044) supported by the Basque Governmen

Resistive Switching Device Technology Based on Silicon Oxide for Improved ON-OFF Ratio--Part II: Select Devices

The cross-point architecture for memory arrays is widely considered as one of the most attractive solutions for storage and memory circuits thanks to simplicity, scalability, small cell size, and consequently high density and low cost. Cost-scalable vertical 3-D cross-point architectures, in particular, offer the opportunity to challenge Flash memory with comparable density and cost. To develop scalable cross-point arrays, however, select devices with sufficient ON-OFF ratio, current capability, and endurance must be available. This paper presents a select device technology based on volatile resistive switching with Cu and Ag top electrode and silicon oxide (SiOₓ) switching materials. The select device displays ultrahigh resistance window and good current capability exceeding 2 MAcm⁻². Retention study shows a stochastic voltage-dependent ON-OFF transition time in the 10 μs-1 ms range, which needs to be further optimized for fast memory operation in storage class memory arrays

Experimental verification of memristor-based material implication NAND operation

Memristors are being considered as promising devices for highly dense memory systems as well as the potential basis of new computational paradigms. In this scenario, and in relation with data processing, one of the more specific and differential logic functions is the material implication logic also named as IMPLY logic. Many papers have been published in this framework but few of them are related with experimental works using real memristor devices. In the paper authors show the verification of the IMPLY function by using Ni/HfO2/Si manufactured devices and laboratory measurements. The proper behavior of the IMPLY structure (2 memristors) has been shown. The paper also verifies the proper operation of a two-step IMPLY-based NAND gate implementation, showing the electrical behavior of the circuit in a cycling operation. A new procedure to implement a NAND gate that requires only one step is experimentally shown as well

Non-homogeneuos conduction of conductive filaments in Ni/HfO2/Si resistive switching structures observed with CAFM

Altres ajuts: ERDF/TEC2011-2792-C02-02Conductive filaments (CFs) in Ni/HfO₂/Si resistive switching structures are analysed at the nanoscale by means of Conductive Atomic Force Microscopy (CAFM). Differences in the CF conductivity are measured depending on the resistive state of the device. Moreover, for both resistance states, non-homogeneous conduction across the CF area is observed, in agreement with a tree-shaped CF

Prácticas de observaciones astronómicas remotas con telescopios profesionales a través de internet

Este proyecto se resume como un intento de implementar unas prácticas de laboratorio que permitan llevar a cabo observaciones astronómicas de calidad científica mediante el uso remoto a través de Internet de telescopios profesionales situados en un gran observatorio astronómico

Experimental verification of memristor-based material implication NAND operation

Memristors are being considered as promising devices for highly dense memory systems as well as the potential basis of new computational paradigms. In this scenario, and in relation with data processing, one of the more specific and differential logic functions is the material implication logic also named as IMPLY logic. Many papers have been published in this framework but few of them are related with experimental works using real memristor devices. In the paper authors show the verification of the IMPLY function by using $\mathrm{Ni}/\mathrm{HfO}_{2}/\mathrm{Si}$ manufactured devices and laboratory measurements. The proper behavior of the IMPLY structure (2 memristors) has been shown. The paper also verifies the proper operation of a two-steps IMPLY-based NAND gate implementation, showing the electrical behavior of the circuit in a cycling operation. A new procedure to implement a NAND gate that requires only one step is experimentally shown as well.Postprint (author's final draft

2020 IEEE Latin America Electron Devices Conference (LAEDC)

Producción CientíficaThree topologies of TiN/Ti/HfO 2 /W resistive switching memories (RRAM) are proposed in this work: crossbar, isolated and isolated-crossbar configurations. All configurations use the same sequence of technological processes. The different topologies are obtained by customizing the layouts corresponding to the bottom electrode (W), and the silicon oxide layer that is deposited on the bottom electrode. A comparative study of the resistive switching mechanisms in the three configurations has been carried out. DC current-voltage cycles and small signal conductance memory maps of single RRAM show relevant differences among the three topologies. Complex structures containing various devices (series, anti-series, parallel, antiparallel) have also been fabricated. Switching loops and memory maps obtained for these complex structures demonstrate that they are fully operative, validating the technological route to manufacture complete RRAM memory chips.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (grants TEC2017-84321- C4-1-R and TEC2017-84321-C4-2-R

Self-organizing neural networks based on OxRAM devices under a fully unsupervised training scheme

A fully-unsupervised learning algorithm for reaching self-organization in neuromorphic architectures is provided in this work. We experimentally demonstrate spike-timing dependent plasticity (STDP) in Oxide-based Resistive Random Access Memory (OxRAM) devices, and propose a set of waveforms in order to induce symmetric conductivity changes. An empirical model is used to describe the observed plasticity. A neuromorphic system based on the tested devices is simulated, where the developed learning algorithm is tested, involving STDP as the local learning rule. The design of the system and learning scheme permits to concatenate multiple neuromorphic layers, where autonomous hierarchical computing can be performed