Pub Date : 2021-12-07DOI: 10.1109/OJNANO.2021.3133213
Akash Jain;Heman Vaghasiya;Jai Narayan Tripathi
In the era of advanced nanotechnology where billions of transistors are fabricated in a single chip, high-speed operations are challenging due to packaging related issues. In High-Speed Very Large Scale Integration (VLSI) systems, decoupling capacitors are essentially used in power delivery networks to reduce power supply noise and to maintain a low impedance of the power delivery networks. In this paper, the cumulative impedance of a power delivery network is reduced below the target impedance by using state-of-the-art metaheuristic algorithms to choose and place decoupling capacitors optimally. A Matrix-based Evolutionary Computing (MEC) approach is used for efficient usage of metaheuristic algorithms. Two case studies are presented on a practical system to demonstrate the proposed approach. A comparative analysis of the performance of state-of-the-art metaheuristics is presented with the insights of practical implementation. The consistency of results in both the case studies confirms the validity of the proposed appraoch.
{"title":"Efficient Selection and Placement of In-Package Decoupling Capacitors Using Matrix-Based Evolutionary Computation","authors":"Akash Jain;Heman Vaghasiya;Jai Narayan Tripathi","doi":"10.1109/OJNANO.2021.3133213","DOIUrl":"https://doi.org/10.1109/OJNANO.2021.3133213","url":null,"abstract":"In the era of advanced nanotechnology where billions of transistors are fabricated in a single chip, high-speed operations are challenging due to packaging related issues. In High-Speed Very Large Scale Integration (VLSI) systems, decoupling capacitors are essentially used in power delivery networks to reduce power supply noise and to maintain a low impedance of the power delivery networks. In this paper, the cumulative impedance of a power delivery network is reduced below the target impedance by using state-of-the-art metaheuristic algorithms to choose and place decoupling capacitors optimally. A Matrix-based Evolutionary Computing (MEC) approach is used for efficient usage of metaheuristic algorithms. Two case studies are presented on a practical system to demonstrate the proposed approach. A comparative analysis of the performance of state-of-the-art metaheuristics is presented with the insights of practical implementation. The consistency of results in both the case studies confirms the validity of the proposed appraoch.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"2 ","pages":"191-200"},"PeriodicalIF":1.7,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782713/9316416/09640572.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3500653","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 : 2021-12-07DOI: 10.1109/OJNANO.2021.3133325
Jai Narayan Tripathi;Heman Vaghasiya;Dinesh Junjariya;Aksh Chordia
Interconnects are essential components of any electronic system. Their design, modeling and optimization are becoming complex and computationally expensive with the evolution of semiconductor technology as the devices of nanometer dimensions are being used. In high-speed applications, system level simulations are needed to ensure the robustness of a system in terms of signal and power quality. The simulations are becoming very expensive because of the large dimensional systems and their full-wave models. Machine learning techniques can be used as computationally efficient alternatives in the design cycle of the interconnects. This paper presents a review of the applications of machine learning techniques for design, optimization and analysis of interconnects in high-speed electronic systems. A holistic discussion is presented, including the basics of interconnects, their impact on the system performance, popular machine learning techniques and their applications related to the interconnects. The performance evaluation, optimization and variability analysis of interconnects are discussed in detail. Future scope and overlook that are presented in the literature are also discussed.
{"title":"Machine Learning Techniques for Modeling and Performance Analysis of Interconnects","authors":"Jai Narayan Tripathi;Heman Vaghasiya;Dinesh Junjariya;Aksh Chordia","doi":"10.1109/OJNANO.2021.3133325","DOIUrl":"https://doi.org/10.1109/OJNANO.2021.3133325","url":null,"abstract":"Interconnects are essential components of any electronic system. Their design, modeling and optimization are becoming complex and computationally expensive with the evolution of semiconductor technology as the devices of nanometer dimensions are being used. In high-speed applications, system level simulations are needed to ensure the robustness of a system in terms of signal and power quality. The simulations are becoming very expensive because of the large dimensional systems and their full-wave models. Machine learning techniques can be used as computationally efficient alternatives in the design cycle of the interconnects. This paper presents a review of the applications of machine learning techniques for design, optimization and analysis of interconnects in high-speed electronic systems. A holistic discussion is presented, including the basics of interconnects, their impact on the system performance, popular machine learning techniques and their applications related to the interconnects. The performance evaluation, optimization and variability analysis of interconnects are discussed in detail. Future scope and overlook that are presented in the literature are also discussed.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"2 ","pages":"178-190"},"PeriodicalIF":1.7,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782713/9316416/09640578.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3500094","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 : 2021-12-02DOI: 10.1109/OJNANO.2021.3131653
Alvin Chao-Yu Chen;Yun-Wen Tong;Chih-Hao Chiu;Kin Fong Lei
Periosteum-derived progenitor cells (PDPCs) are highly promising cell sources for bone fracture healing because of their stem cell-like multipotency to undergo osteogenesis and chondrogenesis. Both externally physical stimulation and internally biochemical signal were reported to enhance osteogenic differentiation of bone tissues. Electric stimulation (ES) could trigger the differentiation of stem cells, like mesenchymal stem cells (MSCs) and adipose-derived stem cells (ADSCs). But the effect is still unclear on PDPCs. In order to investigate the differentiation ability of PDPCs co-induced by ES and ADSCs, a biomimetic 3-dimensional (3D) co-culture system was developed for providing ES and co-culturing with ADSCs. Gene expression was studied after a 3-day culture course. From our results, osteogenic differentiation of PDPCs was significantly activated under the ES of 0.7 V/cm, 80 kHz, and 3 hrs/day. Moreover, co-culturing with ADSCs during the ES treatment was found to have synergistic effect of osteogenic differentiation. In addition, chondrogenic differentiation was shown when the PDPCs were cultured for a long culture course. In summary, osteogenic differentiation of PDPCs was shown to be co-induced by ES and ADSCs. This study provides significant insights of the PDPC therapy for bone tissue regeneration.
{"title":"Osteogenic Effect of Rabbit Periosteum-Derived Precursor Cells Co-Induced by Electric Stimulation and Adipose-Derived Stem Cells in a 3D Co-Culture System","authors":"Alvin Chao-Yu Chen;Yun-Wen Tong;Chih-Hao Chiu;Kin Fong Lei","doi":"10.1109/OJNANO.2021.3131653","DOIUrl":"https://doi.org/10.1109/OJNANO.2021.3131653","url":null,"abstract":"Periosteum-derived progenitor cells (PDPCs) are highly promising cell sources for bone fracture healing because of their stem cell-like multipotency to undergo osteogenesis and chondrogenesis. Both externally physical stimulation and internally biochemical signal were reported to enhance osteogenic differentiation of bone tissues. Electric stimulation (ES) could trigger the differentiation of stem cells, like mesenchymal stem cells (MSCs) and adipose-derived stem cells (ADSCs). But the effect is still unclear on PDPCs. In order to investigate the differentiation ability of PDPCs co-induced by ES and ADSCs, a biomimetic 3-dimensional (3D) co-culture system was developed for providing ES and co-culturing with ADSCs. Gene expression was studied after a 3-day culture course. From our results, osteogenic differentiation of PDPCs was significantly activated under the ES of 0.7 V/cm, 80 kHz, and 3 hrs/day. Moreover, co-culturing with ADSCs during the ES treatment was found to have synergistic effect of osteogenic differentiation. In addition, chondrogenic differentiation was shown when the PDPCs were cultured for a long culture course. In summary, osteogenic differentiation of PDPCs was shown to be co-induced by ES and ADSCs. This study provides significant insights of the PDPC therapy for bone tissue regeneration.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"2 ","pages":"153-160"},"PeriodicalIF":1.7,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782713/9316416/09633183.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3482739","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}
Femtosecond laser processing is fast becoming a pervasive method for fabricating micro/nanostructures because it can be used to produce micro/nanostructures on myriads of materials with high precision and resolution, requires little control over environmental conditions, and is simple to implement. Here, we review recent developments in the use of femtosecond lasers for the fabrication of micro/nanostructures through ablation and two-photon polymerization (TPP). Moreover, the applications of some of the fabricated micro/nanostructures are also discussed. We highlight the advantages of femtosecond laser processing by explaining the underlying principles of laser ablation and TPP. We also show the use of this method to fabricate new devices with outstanding performance in several application realm, such as sensors, optical devices, microfluidic chips, and soft robotics.
{"title":"Recent Advances in Femtosecond Laser Fabrication: From Structures to Applications","authors":"Yangdong Wen;Haibo Yu;Yuzhao Zhang;Ye Qiu;Peiwen Li;Xiaoduo Wang;Boliang Jia;Lianqing Liu;Wen Jung Li","doi":"10.1109/OJNANO.2021.3131818","DOIUrl":"https://doi.org/10.1109/OJNANO.2021.3131818","url":null,"abstract":"Femtosecond laser processing is fast becoming a pervasive method for fabricating micro/nanostructures because it can be used to produce micro/nanostructures on myriads of materials with high precision and resolution, requires little control over environmental conditions, and is simple to implement. Here, we review recent developments in the use of femtosecond lasers for the fabrication of micro/nanostructures through ablation and two-photon polymerization (TPP). Moreover, the applications of some of the fabricated micro/nanostructures are also discussed. We highlight the advantages of femtosecond laser processing by explaining the underlying principles of laser ablation and TPP. We also show the use of this method to fabricate new devices with outstanding performance in several application realm, such as sensors, optical devices, microfluidic chips, and soft robotics.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"2 ","pages":"161-177"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782713/9316416/09632350.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3482578","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}
The phonon transport in the lateral direction for gap-controlled Si nanopillar (NP) /SiGe interlayer composite materials was investigated to eliminate heat generation in the channel area for advanced MOS transistors. The gap-controlled Si NP/SiGe composite layer showed 1/250 times lower thermal conductivity than Si bulk. Then, the phonon transport behavior in lateral direction could be predicted by the combination between the 3-omega measurement method for thermal conductivity and the Landauer approach for phonon transport in Si NP/Si 0.7