Pub Date : 2022-11-23DOI: 10.1109/OJNANO.2022.3224061
DINESH JUNJARIYA;JAI NARAYAN TRIPATHI
Power delivery networks are responsible for supplying clean power to the integrated circuits. Power supply noise plays a critical role in determining the performance of high-speed very large scale integration circuits and systems. In order to maintain power integrity in high-speed systems, decoupling capacitors are used to maintain low impedance of the PDN to eventually minimize power supply noise. However, the discrete optimization problem of selecting decoupling capacitors becomes computationally challenging in the systems having stringent power integrity (PI) requirements. In this work, a novel approach using the Social-Learning Particle Swarm Optimization (SLPSO) technique along with Adaptive Region Search (ARS) is used to tackle the Large-Scale Optimization Problem (LSOP) of decoupling capacitor placement. Region Search (RS) is used to guide particles, followed by ARS to dynamical search for the local best positions and for particles to move faster across the search space while maintaining the diversity of the population. To demonstrate the proposed approach, three practical case studies are presented. The obtained results are compared with current state-of-the-art approaches. The proposed approach drastically reduces computation time and is consistent with better results than other approaches. This consistency of improvement in CPU time in the results of all the examples validates the proposed approach.
{"title":"Large-Scale Optimization of Decoupling Capacitors Using Adaptive Region Based Encoding Scheme in Particle Swarm Optimization","authors":"DINESH JUNJARIYA;JAI NARAYAN TRIPATHI","doi":"10.1109/OJNANO.2022.3224061","DOIUrl":"10.1109/OJNANO.2022.3224061","url":null,"abstract":"Power delivery networks are responsible for supplying clean power to the integrated circuits. Power supply noise plays a critical role in determining the performance of high-speed very large scale integration circuits and systems. In order to maintain power integrity in high-speed systems, decoupling capacitors are used to maintain low impedance of the PDN to eventually minimize power supply noise. However, the discrete optimization problem of selecting decoupling capacitors becomes computationally challenging in the systems having stringent power integrity (PI) requirements. In this work, a novel approach using the Social-Learning Particle Swarm Optimization (SLPSO) technique along with Adaptive Region Search (ARS) is used to tackle the Large-Scale Optimization Problem (LSOP) of decoupling capacitor placement. Region Search (RS) is used to guide particles, followed by ARS to dynamical search for the local best positions and for particles to move faster across the search space while maintaining the diversity of the population. To demonstrate the proposed approach, three practical case studies are presented. The obtained results are compared with current state-of-the-art approaches. The proposed approach drastically reduces computation time and is consistent with better results than other approaches. This consistency of improvement in CPU time in the results of all the examples validates the proposed approach.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"3 ","pages":"210-219"},"PeriodicalIF":1.7,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9961848","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-23DOI: 10.1109/OJNANO.2022.3223897
Kuniko Urashima
Since the beginning of the 20th century, plasma technology has been used in a variety of fields. In the 1980s, R&D related to arc plasma welding and waste disposal, as well as etching, painting, and gas removal equipment that used plasma technology in the processes associated with semiconductor manufacturing. In the 1990s, research on removing air pollutants using atmospheric pressure plasma technology became active. In the 2000s, research on the application of thermal/non-thermal plasma technology to air pollution, waste and water treatment became active. Electrostatic precipitators (ESP) can remove a wide variety of particles such as soot from thermal power plants, coal, and oil mist, resin powder, glass powder, dust, and iron powder generated from incinerators, boilers, and various manufacturing plants. Waste treatment aims to reduce the volume of garbage, recycle incinerated materials, and utilize waste heat from incineration, and plasma technology is used in each process. Various techniques have been used for making purified water. Water quality requirements vary according to the objective. Plasma technology uses an electrical field to encourage seed germination and growth. Due to the spread of such applied technology, plasma technology has attracted attention again in recent years.
{"title":"Review of Plasma Technologies for Contribution of Environmental Purification","authors":"Kuniko Urashima","doi":"10.1109/OJNANO.2022.3223897","DOIUrl":"10.1109/OJNANO.2022.3223897","url":null,"abstract":"Since the beginning of the 20th century, plasma technology has been used in a variety of fields. In the 1980s, R&D related to arc plasma welding and waste disposal, as well as etching, painting, and gas removal equipment that used plasma technology in the processes associated with semiconductor manufacturing. In the 1990s, research on removing air pollutants using atmospheric pressure plasma technology became active. In the 2000s, research on the application of thermal/non-thermal plasma technology to air pollution, waste and water treatment became active. Electrostatic precipitators (ESP) can remove a wide variety of particles such as soot from thermal power plants, coal, and oil mist, resin powder, glass powder, dust, and iron powder generated from incinerators, boilers, and various manufacturing plants. Waste treatment aims to reduce the volume of garbage, recycle incinerated materials, and utilize waste heat from incineration, and plasma technology is used in each process. Various techniques have been used for making purified water. Water quality requirements vary according to the objective. Plasma technology uses an electrical field to encourage seed germination and growth. Due to the spread of such applied technology, plasma technology has attracted attention again in recent years.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"3 ","pages":"159-165"},"PeriodicalIF":1.7,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9961850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/OJNANO.2022.3223712
Ka Wai Kong;Keer Wang;Alice Yeuk Lan Leung;Hongyu Zhang;Jiao Suo;Meng Chen;Guanglie Zhang;Fei Fei;Jiangang Shen;Wen Jung Li
We report a novel flexible capacitive pressure-pulse sensor array developed by integrating droplet-dispensed graphene oxide (GO) sensing elements and flexible electronics. The utilization of droplet-dispensing technology enables the fabrication multiple capacitive sensing elements rapidly while producing sensitive pressure sensors with excellent repeatability. The dispensed droplet volume (GO aqueous dispersion) ranged from around 33.5 to 65.4 pL with diameter ranging from 40 to 50 μm. The size (i.e., footprint and dielectric material thickness) of a sensing element can be controlled by the total GO dispersed per droplet. The fabrication process and preliminary characterization of these GO capacitive sensors are discussed in this paper. Thus far, we have shown that these sensors have a sensitivity of ∼10 −3