International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Natural organic matter removal by coagulation during drinking water treatment: A review

The resistance switching behaviour of several materials has recently attracted considerable attention for its application in non-volatile memory (NVM) devices, popularly described as resistive random access memories (RRAMs). RRAM is a type of NVM that uses a material(s) that changes the resistance when a voltage is applied. Resistive switching phenomena have been observed in many oxides: (i) binary transition metal oxides (TMOs), e.g. TiO(2), Cr(2)O(3), FeO(x) and NiO; (ii) perovskite-type complex TMOs that are variously functional, paraelectric, ferroelectric, multiferroic and magnetic, e.g. (Ba,Sr)TiO(3), Pb(Zr(x) Ti(1-x))O(3), BiFeO(3) and Pr(x)Ca(1-x)MnO(3); (iii) large band gap high-k dielectrics, e.g. Al(2)O(3) and Gd(2)O(3); (iv) graphene oxides. In the non-oxide category, higher chalcogenides are front runners, e.g. In(2)Se(3) and In(2)Te(3). Hence, the number of materials showing this technologically interesting behaviour for information storage is enormous. Resistive switching in these materials can form the basis for the next generation of NVM, i.e. RRAM, when current semiconductor memory technology reaches its limit in terms of density. RRAMs may be the high-density and low-cost NVMs of the future. A review on this topic is of importance to focus concentration on the most promising materials to accelerate application into the semiconductor industry. This review is a small effort to realize the ambitious goal of RRAMs. Its basic focus is on resistive switching in various materials with particular emphasis on binary TMOs. It also addresses the current understanding of resistive switching behaviour. Moreover, a brief comparison between RRAMs and memristors is included. The review ends with the current status of RRAMs in terms of stability, scalability and switching speed, which are three important aspects of integration onto semiconductors.

https://iopscience.iop.org/article/10.1088/0034-4885/75/7/076502/pdf

Emerging memories: resistive switching mechanisms and current status.

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first revie...

https://www.tandfonline.com/doi/pdf/10.1080/23746149.2016.1259585

Neuromorphic computing using non-volatile memory

Polycyclic aromatic hydrocarbons (PAHs) are a large group of organic compounds with two or more fused aromatic rings. They have a relatively low solubility in water, but are highly lipophilic. Most of the PAHs with low vapour pressure in the air are adsorbed on particles. When dissolved in water or adsorbed on particulate matter, PAHs can undergo photodecomposition when exposed to ultraviolet light from solar radiation. In the atmosphere, PAHs can react with pollutants such as ozone, nitrogen oxides and sulfur dioxide, yielding diones, nitro- and dinitro-PAHs, and sulfonic acids, respectively. PAHs may also be degraded by some microorganisms in the soil. PAHs are widespread environmental contaminants resulting from incomplete combustion of organic materials. The occurrence is largely a result of anthropogenic emissions such as fossil fuel-burning, motor vehicle, waste incinerator, oil refining, coke and asphalt production, and aluminum production, etc. PAHs have received increased attention in recent years in air pollution studies because some of these compounds are highly carcinogenic or mutagenic. Eight PAHs (Car-PAHs) typically considered as possible carcinogens are: benzo(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene (B(a)P), dibenzo(a,h)anthracene, indeno(1,2,3-cd)pyrene and benzo(g,h,i)perylene. In particular, benzo(a)pyrene has been identified as being highly carcinogenic. The US Environmental Protection Agency (EPA) has promulgated 16 unsubstituted PAHs (EPA-PAH) as priority pollutants. Thus, exposure assessments of PAHs in the developing world are important. The scope of this review will be to give an overview of PAH concentrations in various environmental samples and to discuss the advantages and limitations of applying these parameters in the assessment of environmental risks in ecosystems and human health. As it well known, there is an increasing trend to use the behavior of pollutants (i.e. bioaccumulation) as well as pollution-induced biological and biochemical effects on human organisms to evaluate or predict the impact of chemicals on ecosystems. Emphasis in this review will, therefore, be placed on the use of bioaccumulation and biomarker responses in air, soil, water and food, as monitoring tools for the assessment of the risks and hazards of PAH concentrations for the ecosystem, as well as on its limitations.

/pdf/monitoring-of-environmental-exposure-to-polycyclic-aromatic-34edfn0ke5.pdf

Monitoring of environmental exposure to polycyclic aromatic hydrocarbons: a review

This paper is devoted to an exhaustive presentation of a fast computation numerical tool, dedicated to the simulation of transient currents induced by stochastic events in microelectronic devices. This is a part of a numerical platform, SITARe, combining a spice simulator with the semi-analytical model presented here. The paper describes the theoretical model, the calibration. An instance of application illustrates the ability of the tool.

/pdf/sitare-a-fast-simulation-tool-for-the-analysis-of-disruptive-2qk12ogmv9.pdf

SITARe: a fast simulation tool for the analysis of disruptive effects on electronics

The advent of deep learning has considerably accelerated machine learning development The deployment of deep neural networks at the edge is however limited by their high memory and energy consumption requirements With new memory technology available, emerging Binarized Neural Networks (BNNs) are promising to reduce the energy impact of the forthcoming machine learning hardware generation, enabling machine learning on the edge devices and avoiding data transfer over the network In this work, after presenting our implementation employing a hybrid CMOS - hafnium oxide resistive memory technology, we suggest strategies to apply BNNs to biomedical signals such as electrocardiography and electroencephalography, keeping accuracy level and reducing memory requirements We investigate the memory-accuracy trade-off when binarizing whole network and binarizing solely the classifier part We also discuss how these results translate to the edge-oriented Mobilenet~V1 neural network on the Imagenet task The final goal of this research is to enable smart autonomous healthcare devices

In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications

Problem Formulation: Resistive Random Access Memory (ReRAM) is a form of nonvolatile storage that operates by changing the resistance of a specially formulated solid dielectric material [1]. Among ReRAMs, Oxide-based Resistive RAMs (so-called OxRRA M) are promising candidates due their compatibility with CMOS processes and high ON/OFF r esistance ratio. Common problems with OxRRAM are related to high variability in operating conditions and low yield. OxRRAM variability mainly impact ON/OFF resistance ratio. This ratio i s a key parameter to determine the overall performance of an OxRRAM memory. In this context, t he presented built-in structure allows collecting statistical data related to the OxRRAM memory array (ON/OFF resistance distributions) for reliability assessment of the technology.

https://hal.archives-ouvertes.fr/hal-01745729/document

A Built-In Self-Test Structure (BIST) for Resistive RAMs Characterization: Application to Bipolar OxRRAMs

L'invention concerne une memoire non volatile comprenant une pluralite de cellules elementaires (300), chaque cellule comportant : un premier element de stockage (101) a resistance programmable connecte entre des premier (SL ; DL) et deuxieme (n) noeuds de la cellule ; un premier transistor d'acces (103) reliant le deuxieme noeud (n) a un troisieme noeud (BL) de la cellule ; et un deuxieme transistor d'acces (305) reliant le deuxieme noeud (n) a un quatrieme noeud (DL ; SL) de la cellule.

Non-volatile memory with programmable resistance

The objective of this paper is to evaluate the delay impact of staggered metal filling on the standard cells and their associated local interconnect on several metal levels. A Design of Experiment (DOE) is used to define a large range of filling pattern shapes and positions. This set of filling patterns is then inserted in a Ring Oscillator (RO). From the filled RO simulations, the RO delay is expressed as a function of the filling pattern features. The maximal timing error between the model and the simulation is 1.3%, validating the model. The filling impact on RO delay magnifies the one introduced by the front-end process variations (PVT). Consequently, the filling influence is introduced for the minimal, typical and maximal corners, defined now with Process (P), Voltage (V), Temperature (T) and Filling density (F) characteristics.

https://www.sbmicro.org.br/jics/html/artigos/vol4no1/02.pdf

Study of the Metal Filling Impact on Standard Cells and their Associated Interconnects Using Ring Oscillators: Definition of the Metal Fill Corner Concept

Jean-Michel Portal

Papers

SITARe: a fast simulation tool for the analysis of disruptive effects on electronics

In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications

A Built-In Self-Test Structure (BIST) for Resistive RAMs Characterization: Application to Bipolar OxRRAMs

Non-volatile memory with programmable resistance

Study of the Metal Filling Impact on Standard Cells and their Associated Interconnects Using Ring Oscillators: Definition of the Metal Fill Corner Concept