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

L'invention concerne une memoire non volatile (1), comprenant : -des transistors de selection (3, 4) comportant chacun: -une couche en materiau semi-conducteur avec une zone de canal (30) et des premiere et deuxieme electrodes de conduction (32, 31); -un empilement de grille (33) incluant une electrode de grille (332) et un isolant de grille (331) ; -une tranchee d'isolation (60) disposee entre les transistors de selection (3, 2) ; -une structure de memorisation (4, 5) de type RRAM comportant : -une electrode de commande (434) ; -une couche de dielectrique (40) formee sous l'electrode de commande (434) et dans le meme materiau que l'isolant de grille, comportant une partie mediane (401) a la verticale de la tranchee d'isolation (60) et des extremites (403, 402) s'etendant a la verticale d'electrodes de conduction (32, 22), et configuree pour former des filaments conducteurs entre l'electrode de commande (434) et ces electrodes de conduction.

Memoire non volatile favorisant une grande densite d'integration

Bayesian reasoning is a machine learning approach that provides explainable outputs and excels in small-data situations with high uncertainty. However, it requires intensive memory access and computation and is, therefore, too energy-intensive for extreme edge contexts. Near-memory computation with memristors (or RRAM) can greatly improve the energy efficiency of its computations. Here, we report two fabricated integrated circuits in a hybrid CMOS-memristor process, featuring each sixteen tiny memristor arrays and the associated near-memory logic for Bayesian inference. One circuit performs Bayesian inference using stochastic computing, and the other uses logarithmic computation; these two paradigms fit the area constraints of near-memory computing well. On-chip measurements show the viability of both approaches with respect to memristor imperfections. The two Bayesian machines also operated well at low supply voltages. We also designed scaled-up versions of the machines. Both scaled-up designs can perform a gesture recognition task using orders of magnitude less energy than a microcontroller unit. We also see that if an accuracy lower than 86.9% is sufficient for this sample task, stochastic computing consumes less energy than logarithmic computing; for higher accuracies, logarithmic computation is more energy-efficient. These results highlight the potential of memristor-based near-memory Bayesian computing, providing both accuracy and energy efficiency.

Energy-Efficient Bayesian Inference Using Near-Memory Computation with Memristors

We present an integrated circuit fabricated in a process co-integrating CMOS and hafnium-oxide memristor technology, which provides a prototyping platform for projects involving memristors. Our circuit includes the periphery circuitry for using memristors within digital circuits, as well as an analog mode with direct access to memris-tors. The platform allows optimizing the conditions for reading and writing memristors, as well as developing and testing innovative memristor-based neuromorphic concepts.

https://dl.acm.org/doi/pdf/10.1145/3566097.3567944

A Multimode Hybrid Memristor-CMOS Prototyping Platform Supporting Digital and Analog Projects

This paper introduces a new way of monitoring critical parameters directly inside circuits. It describes a flow able to transform a circuit into a test vehicle: the concept is called topological exchange. The principle is to remap existing standard cells to create monitoring functions. The flow is detailed through a specific example of oxide thickness monitoring and the method is validated with post-layout simulations.

An innovative standard cells remapping method for in-circuit critical parameters monitoring

This paper presents a new approach to analyze nanometres SRAM response to SEE attributed to proton-Silicon interactions. It couples the MUlti SCAles Single-Event Phenomena Predictive Platform (MUSCA SEP3) with SPICE modelling to study multi-injections phenomena.

Jean-Michel Portal

Papers

Memoire non volatile favorisant une grande densite d'integration

Energy-Efficient Bayesian Inference Using Near-Memory Computation with Memristors

A Multimode Hybrid Memristor-CMOS Prototyping Platform Supporting Digital and Analog Projects

An innovative standard cells remapping method for in-circuit critical parameters monitoring

Effect of multiple injections on the SEEs in SRAM cell