Pub Date : 2025-01-24DOI: 10.1109/OJNANO.2025.3534518
{"title":"2024 Index IEEE Open Journal of Nanotechnology Vol. 5","authors":"","doi":"10.1109/OJNANO.2025.3534518","DOIUrl":"https://doi.org/10.1109/OJNANO.2025.3534518","url":null,"abstract":"","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"5 ","pages":"1-8"},"PeriodicalIF":1.8,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105669","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 : 2025-01-17DOI: 10.1109/OJNANO.2025.3531759
Roopesh Singh;Shivam Verma
Energy-efficient non-volatile memory that supports non-destructive read capabilities is in high demand for random-access memory applications. This article presents the proposal and demonstration of a 1T-1R non-volatile memory cell, which has distinct read and write paths that utilize a memristive variant of the ferroelectric field effect transistor (MFeFET) for data storage. Through a combination of experimentally calibrated models and TCAD-based mixed-mode simulations, the proposed MFeFET-based memory cell is demonstrated to achieve a non-destructive read operation and higher read current at low operating voltages. Furthermore, the memory cell demonstrates a 50% reduction in read latency compared to spin transfer torque (STT) magneto-resistive random-access memory (MRAM) technologies, positioning it as a highly efficient solution for next-generation non-volatile memory applications.
{"title":"Memristive Ferroelectric FET for 1T-1R Nonvolatile Memory With Non-Destructive Readout","authors":"Roopesh Singh;Shivam Verma","doi":"10.1109/OJNANO.2025.3531759","DOIUrl":"https://doi.org/10.1109/OJNANO.2025.3531759","url":null,"abstract":"Energy-efficient non-volatile memory that supports non-destructive read capabilities is in high demand for random-access memory applications. This article presents the proposal and demonstration of a 1T-1R non-volatile memory cell, which has distinct read and write paths that utilize a memristive variant of the ferroelectric field effect transistor (MFeFET) for data storage. Through a combination of experimentally calibrated models and TCAD-based mixed-mode simulations, the proposed MFeFET-based memory cell is demonstrated to achieve a non-destructive read operation and higher read current at low operating voltages. Furthermore, the memory cell demonstrates a 50% reduction in read latency compared to spin transfer torque (STT) magneto-resistive random-access memory (MRAM) technologies, positioning it as a highly efficient solution for next-generation non-volatile memory applications.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"6 ","pages":"27-34"},"PeriodicalIF":1.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107174","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 : 2025-01-06DOI: 10.1109/OJNANO.2025.3525915
{"title":"IEEE Open Journal of Nanotechnology Information for Authors","authors":"","doi":"10.1109/OJNANO.2025.3525915","DOIUrl":"https://doi.org/10.1109/OJNANO.2025.3525915","url":null,"abstract":"","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"6 ","pages":"C3-C3"},"PeriodicalIF":1.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938090","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}
Convolutional neural networks (CNNs), despite their broad applications, are constrained by high computational and memory requirements. Existing compression techniques often neglect approximation errors incurred during training. This work proposes approximation-aware-training, in which group of weights are approximated using a differential approximation function, resulting in a new weight matrix composed of approximation function's coefficients (AFC). The network is trained using backpropagation to minimize the loss function with respect to AFC matrix with linear and quadratic approximation functions preserving accuracy at high compression rates. This work extends to implement an compute-in-memory architecture for inference operations of approximate neural networks. This architecture includes a mapping algorithm that modulates inputs and map AFC to crossbar arrays directly, eliminating the need to predict approximated weights for evaluating output. This reduces the number of crossbars, lowering area and energy consumption. Integrating magnetic random-access memory-based devices further enhances performance by reducing latency and energy consumption. Simulation results on approximated LeNet-5, VGG8, AlexNet, and ResNet18 models trained on the CIFAR-100 dataset showed reductions of 54%, 30%, 67%, and 20% in the total number of crossbars, respectively, resulting in improved area efficiency. In the ResNet18 architecture, latency and energy consumption decreased by 95% and 93.3% with spin-orbit torque (SOT) based crossbars compared to RRAM-based architectures.
{"title":"Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture","authors":"Hemkant Nehete;Sandeep Soni;Tharun Kumar Reddy Bollu;Balasubramanian Raman;Brajesh Kumar Kaushik","doi":"10.1109/OJNANO.2024.3524265","DOIUrl":"https://doi.org/10.1109/OJNANO.2024.3524265","url":null,"abstract":"Convolutional neural networks (CNNs), despite their broad applications, are constrained by high computational and memory requirements. Existing compression techniques often neglect approximation errors incurred during training. This work proposes approximation-aware-training, in which group of weights are approximated using a differential approximation function, resulting in a new weight matrix composed of approximation function's coefficients (AFC). The network is trained using backpropagation to minimize the loss function with respect to AFC matrix with linear and quadratic approximation functions preserving accuracy at high compression rates. This work extends to implement an compute-in-memory architecture for inference operations of approximate neural networks. This architecture includes a mapping algorithm that modulates inputs and map AFC to crossbar arrays directly, eliminating the need to predict approximated weights for evaluating output. This reduces the number of crossbars, lowering area and energy consumption. Integrating magnetic random-access memory-based devices further enhances performance by reducing latency and energy consumption. Simulation results on approximated LeNet-5, VGG8, AlexNet, and ResNet18 models trained on the CIFAR-100 dataset showed reductions of 54%, 30%, 67%, and 20% in the total number of crossbars, respectively, resulting in improved area efficiency. In the ResNet18 architecture, latency and energy consumption decreased by 95% and 93.3% with spin-orbit torque (SOT) based crossbars compared to RRAM-based architectures.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"6 ","pages":"16-26"},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993286","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}
This study focuses on the synthesis and characterization of Superparamagnetic Iron Oxide Nanoparticles (IONPs) with potential biomedical and sensing applications. These nanoparticles are in high demand for their biocompatibility, biodegradability, and superparamagnetic properties. In contrast to traditional high-temperature synthesis methods, microwave-assisted co-precipitation provides notable benefits, such as improved superparamagnetic characteristics, a high surface-to-volume ratio, large surface area, and simplified separation processes. The synthesis process utilized microwave-assisted co-precipitation, and a range of characterization techniques, including XRD, FESEM, VSM, FTIR, and UV-spectroscopy, were employed to assess the properties of the iron oxide nanoparticles. Analysis of the XRD, FTIR, and UV-spectroscopy results confirmed the formation of IONPs, predominantly comprising magnetite (Fe3O4). The microwave-synthesized IONPs exhibited superparamagnetic behavior, featuring an average crystallite size of 9 nm and robust saturation magnetization values (up to 68 emu/g). These attributes render them highly suitable for applications such as MRI contrast agents, thermal mediators in hyperthermia, drug delivery systems, and advanced sensor technologies, including magnetic sensing and biosensing applications, where their high magnetic responsiveness and surface functionalization capabilities can be effectively leveraged.
{"title":"Microwave-Assisted Synthesis and Characterization of Iron Oxide Nanoparticles for Advanced Biomedical Sensing Applications","authors":"Vivek Pratap Singh;Chandra Prakash Singh;Santosh Kumar;Saurabh Kumar Pandey;Deepak Punetha","doi":"10.1109/OJNANO.2024.3514866","DOIUrl":"https://doi.org/10.1109/OJNANO.2024.3514866","url":null,"abstract":"This study focuses on the synthesis and characterization of Superparamagnetic Iron Oxide Nanoparticles (IONPs) with potential biomedical and sensing applications. These nanoparticles are in high demand for their biocompatibility, biodegradability, and superparamagnetic properties. In contrast to traditional high-temperature synthesis methods, microwave-assisted co-precipitation provides notable benefits, such as improved superparamagnetic characteristics, a high surface-to-volume ratio, large surface area, and simplified separation processes. The synthesis process utilized microwave-assisted co-precipitation, and a range of characterization techniques, including XRD, FESEM, VSM, FTIR, and UV-spectroscopy, were employed to assess the properties of the iron oxide nanoparticles. Analysis of the XRD, FTIR, and UV-spectroscopy results confirmed the formation of IONPs, predominantly comprising magnetite (Fe3O4). The microwave-synthesized IONPs exhibited superparamagnetic behavior, featuring an average crystallite size of 9 nm and robust saturation magnetization values (up to 68 emu/g). These attributes render them highly suitable for applications such as MRI contrast agents, thermal mediators in hyperthermia, drug delivery systems, and advanced sensor technologies, including magnetic sensing and biosensing applications, where their high magnetic responsiveness and surface functionalization capabilities can be effectively leveraged.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"6 ","pages":"10-15"},"PeriodicalIF":1.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10810447","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938088","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 : 2024-12-09DOI: 10.1109/OJNANO.2024.3514900
Raman Hissariya;Debanjan Bhowmik
Compute-in-memory (CIM) crossbar arrays of non-volatile memory (NVM) synapse devices have been considered very attractive for fast and energy-efficient implementation of various neural network (NN) algorithms. High retention time of the synaptic states and high linearity and symmetry of the synaptic weight update characteristics (long-term potentiation (LTP) and long-term depression (LTD)) are major requirements for the NVM synapses in order to obtain high classification accuracy upon implementation of the NN algorithms on the corresponding crossbar arrays. In this paper, with respect to the spin-orbit-torque-driven domain-wall synapse device, we show that addition of edge notches significantly helps in satisfying the aforementioned requirements. At finite temperatures, notches prevent the domain wall from moving due to stray dipole and thermal fields when SOT-causing current is not applied. This, in turn, improves linearity and asymmetry of the LTP and LTD characteristics of the device as well as the retention time of synaptic states. We have also studied how these synaptic properties depend on the spacing between the notches and the size of the notches in the device. We perform this analysis here through rigorous micromagnetic simulations carried out for room temperature (300K), with dipole and thermal fields taken into account.
{"title":"Improving Linearity and Symmetry of Synaptic Update Characteristics and Retentivity of Synaptic States of the Domain-Wall Device Through Addition of Edge Notches","authors":"Raman Hissariya;Debanjan Bhowmik","doi":"10.1109/OJNANO.2024.3514900","DOIUrl":"https://doi.org/10.1109/OJNANO.2024.3514900","url":null,"abstract":"Compute-in-memory (CIM) crossbar arrays of non-volatile memory (NVM) synapse devices have been considered very attractive for fast and energy-efficient implementation of various neural network (NN) algorithms. High retention time of the synaptic states and high linearity and symmetry of the synaptic weight update characteristics (long-term potentiation (LTP) and long-term depression (LTD)) are major requirements for the NVM synapses in order to obtain high classification accuracy upon implementation of the NN algorithms on the corresponding crossbar arrays. In this paper, with respect to the spin-orbit-torque-driven domain-wall synapse device, we show that addition of edge notches significantly helps in satisfying the aforementioned requirements. At finite temperatures, notches prevent the domain wall from moving due to stray dipole and thermal fields when SOT-causing current is not applied. This, in turn, improves linearity and asymmetry of the LTP and LTD characteristics of the device as well as the retention time of synaptic states. We have also studied how these synaptic properties depend on the spacing between the notches and the size of the notches in the device. We perform this analysis here through rigorous micromagnetic simulations carried out for room temperature (300K), with dipole and thermal fields taken into account.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"6 ","pages":"1-9"},"PeriodicalIF":1.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938087","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 : 2024-11-27DOI: 10.1109/OJNANO.2024.3494770
Juo Lee;Sungmin Lee;Iksong Byun;Myung Chul Lee;Jungsil Kim;Hoon Seonwoo
In bone tissue engineering, various approaches have been investigated to enhance osteogenic regeneration. Previous studies have predominantly employed scaffolds with aligned structures or reduced graphene oxide (RGO) to facilitate bone regeneration. However, current scaffold designs face limitations in combining structural guidance with effective electromagnetic stimulation. Additionally, delivering localized stimulation within scaffolds remains a challenge in maximizing the potential of these materials for bone regeneration. To address these limitations and strengthen previous approaches, this study presents a novel strategy in tissue engineering for enhanced osteogenic differentiation. RGO-incorporated nanofibers (RGO-NFs) were fabricated via electrospinning a 10% polycaprolactone (PCL) solution with RGO concentrations varying. The random fibers were deposited on a planar surface, while the aligned fibers were deposited on a rotating drum. The morphology and orientation of the fibers were confirmed through electron microscopy. X-ray diffraction spectrometry was employed to confirm the integration of RGO and PCL. All groups demonstrated optimal cell adhesion and viability. RGO-NFs exhibited higher osteogenesis-related protein expression than PCL-only scaffolds, further enhanced by pulsed electromagnetic field (PEMF) application. The application of PEMF stimulation within aligned RGO-NFs presents a potentially more efficient alternative to existing methods, offering a novel, non-invasive therapeutic strategy for bone defect regeneration.
{"title":"Pulsed Electromagnetic Field-Assisting Reduced Graphene Oxide-Incorporated Nanofibers for Osteogenic Differentiation of Human Dental Pulp Stem Cells","authors":"Juo Lee;Sungmin Lee;Iksong Byun;Myung Chul Lee;Jungsil Kim;Hoon Seonwoo","doi":"10.1109/OJNANO.2024.3494770","DOIUrl":"https://doi.org/10.1109/OJNANO.2024.3494770","url":null,"abstract":"In bone tissue engineering, various approaches have been investigated to enhance osteogenic regeneration. Previous studies have predominantly employed scaffolds with aligned structures or reduced graphene oxide (RGO) to facilitate bone regeneration. However, current scaffold designs face limitations in combining structural guidance with effective electromagnetic stimulation. Additionally, delivering localized stimulation within scaffolds remains a challenge in maximizing the potential of these materials for bone regeneration. To address these limitations and strengthen previous approaches, this study presents a novel strategy in tissue engineering for enhanced osteogenic differentiation. RGO-incorporated nanofibers (RGO-NFs) were fabricated via electrospinning a 10% polycaprolactone (PCL) solution with RGO concentrations varying. The random fibers were deposited on a planar surface, while the aligned fibers were deposited on a rotating drum. The morphology and orientation of the fibers were confirmed through electron microscopy. X-ray diffraction spectrometry was employed to confirm the integration of RGO and PCL. All groups demonstrated optimal cell adhesion and viability. RGO-NFs exhibited higher osteogenesis-related protein expression than PCL-only scaffolds, further enhanced by pulsed electromagnetic field (PEMF) application. The application of PEMF stimulation within aligned RGO-NFs presents a potentially more efficient alternative to existing methods, offering a novel, non-invasive therapeutic strategy for bone defect regeneration.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"5 ","pages":"124-133"},"PeriodicalIF":1.8,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736342","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 : 2024-11-18DOI: 10.1109/OJNANO.2024.3501293
Sateesh;Kaustubh Chakarwar;Shubham Sahay
The recent advancements in data mining, machine learning algorithms and cognitive systems have necessitated the development of neuromorphic processing engines which may enable resource and computationally intensive applications on the internet-of-Things (IoT) edge devices with unprecedented energy efficiency. Spintronics based magnetic memory devices can emulate synaptic behavior efficiently and are hailed as one of the most promising candidates for realizing compact and ultra-energy efficient neural network accelerators. Although ultra-dense magnetic memories with multi-bit capability (MLC) were proposed recently, their application in hybrid CMOS-non-volatile memory accelerators is limited due to their low dynamic range (memory window) and high cell currents (ON/OFF-state resistance in ∼kΩ). In this work, we propose a novel supercell to enable the use of MLC MRAMs for neuromorphic multiply-accumulate (MAC) accelerators. For proof-of-concept demonstration, we exploit an MLC MRAM based on c-MTJ for realizing a highly scalable 2-FinFET-1-MRAM supercell with large dynamic range, low supercell currents and high endurance. Furthermore, we perform a comprehensive design exploration of a time-domain MAC accelerator utilizing the proposed supercell. Our detailed analysis using the ASAP7 PDK from ARM for FinFETs and an experimentally calibrated compact model for c-MTJ-based MRAM indicates the possibility of realizing a significantly high energy-efficiency of 87.4 TOPS/W and a throughput of 2.5 TOPS for a 200×200 MAC operation with 4-bit precision.
{"title":"Utilizing MRAMs With Low Resistance and Limited Dynamic Range for Efficient MAC Accelerator","authors":"Sateesh;Kaustubh Chakarwar;Shubham Sahay","doi":"10.1109/OJNANO.2024.3501293","DOIUrl":"https://doi.org/10.1109/OJNANO.2024.3501293","url":null,"abstract":"The recent advancements in data mining, machine learning algorithms and cognitive systems have necessitated the development of neuromorphic processing engines which may enable resource and computationally intensive applications on the internet-of-Things (IoT) edge devices with unprecedented energy efficiency. Spintronics based magnetic memory devices can emulate synaptic behavior efficiently and are hailed as one of the most promising candidates for realizing compact and ultra-energy efficient neural network accelerators. Although ultra-dense magnetic memories with multi-bit capability (MLC) were proposed recently, their application in hybrid CMOS-non-volatile memory accelerators is limited due to their low dynamic range (memory window) and high cell currents (ON/OFF-state resistance in ∼kΩ). In this work, we propose a novel supercell to enable the use of MLC MRAMs for neuromorphic multiply-accumulate (MAC) accelerators. For proof-of-concept demonstration, we exploit an MLC MRAM based on c-MTJ for realizing a highly scalable 2-FinFET-1-MRAM supercell with large dynamic range, low supercell currents and high endurance. Furthermore, we perform a comprehensive design exploration of a time-domain MAC accelerator utilizing the proposed supercell. Our detailed analysis using the ASAP7 PDK from ARM for FinFETs and an experimentally calibrated compact model for c-MTJ-based MRAM indicates the possibility of realizing a significantly high energy-efficiency of 87.4 TOPS/W and a throughput of 2.5 TOPS for a 200×200 MAC operation with 4-bit precision.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"5 ","pages":"141-148"},"PeriodicalIF":1.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10756528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761417","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 : 2024-11-18DOI: 10.1109/OJNANO.2024.3499974
Vasileios P. Karkanis;Nikolaos I. Dourvas;Andrew Adamatzky;Panagiotis Dimitrakis;Georgios Ch. Sirakoulis
An engineered system that exhibits a variety of interesting properties, such as collective dynamics that are not inherited in their building blocks, is the artificial spin ice (ASI) meta-materials. The building block of such a system is a dipolar nanomagnet with sub-micrometer dimensions. These nanomagnets are arranged in specific designs usually in square or kagome shape and are coupled together by their magnetic interactions. With external magnetic fields, it is possible to create magnetic moments or monopoles that cause a frustration to the system. Because of the local interactions, those moments travel through the topology. The observation of such structures is a very challenging procedure, because of the extremely fast flipping process of the spins. This is why the researchers use mesoscopic systems with materials such as colloids or spheres of nanomagnets which are placed inside of islands in periodic lattices that generate frustration by design. The interactions between those nanomagnets are based on Coulomb forces and are usually modeled by Brownian equations. In this paper, we propose a simple yet effective Cellular Automata (CA) model that can describe effectively the dynamics between nanomagnets in a square lattice structure. The manipulation of the initial positions of nanomagnets via an external magnetic field and the movement of magnetic moments from one site to another are capable to create Boolean logic. Using the CA model we propose the design of logic gates, computing structures such as half adders and rewritable memory elements.
{"title":"Colloidal Spin Ice Cellular Automata for Logic Design","authors":"Vasileios P. Karkanis;Nikolaos I. Dourvas;Andrew Adamatzky;Panagiotis Dimitrakis;Georgios Ch. Sirakoulis","doi":"10.1109/OJNANO.2024.3499974","DOIUrl":"https://doi.org/10.1109/OJNANO.2024.3499974","url":null,"abstract":"An engineered system that exhibits a variety of interesting properties, such as collective dynamics that are not inherited in their building blocks, is the artificial spin ice (ASI) meta-materials. The building block of such a system is a dipolar nanomagnet with sub-micrometer dimensions. These nanomagnets are arranged in specific designs usually in square or kagome shape and are coupled together by their magnetic interactions. With external magnetic fields, it is possible to create magnetic moments or monopoles that cause a frustration to the system. Because of the local interactions, those moments travel through the topology. The observation of such structures is a very challenging procedure, because of the extremely fast flipping process of the spins. This is why the researchers use mesoscopic systems with materials such as colloids or spheres of nanomagnets which are placed inside of islands in periodic lattices that generate frustration by design. The interactions between those nanomagnets are based on Coulomb forces and are usually modeled by Brownian equations. In this paper, we propose a simple yet effective Cellular Automata (CA) model that can describe effectively the dynamics between nanomagnets in a square lattice structure. The manipulation of the initial positions of nanomagnets via an external magnetic field and the movement of magnetic moments from one site to another are capable to create Boolean logic. Using the CA model we propose the design of logic gates, computing structures such as half adders and rewritable memory elements.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"5 ","pages":"163-172"},"PeriodicalIF":1.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825858","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 : 2024-11-08DOI: 10.1109/OJNANO.2024.3494714
Kunal Aggarwal;Avinash Lahgere
In this paper, using calibrated simulation we have reported a dielectric modulated epitaxial tunnel layer TFET (DM ETL-TFET) for the label-free detection of biomolecules. We have shown that due to vertical tunneling direction, the ETL-TFET exhibits $sim$ 3 orders of improvement in the ON-state current in comparison to its counterpart conventional TFET. In addition, the proposed DM ETL-TFET biosensor shows $sim$ 4 orders, and $sim$ 1 order higher ON-state current sensitivity than the past reported core-shell junctionless NT-TFET, and DM NT-TFET biosensors, respectively. Moreover, in comparison to the lateral DM TFET, the proposed DM ETL-TFET shows $sim$ 310 mV higher threshold voltage sensitivity. Also, the subthreshold swing sensitivity of the proposed biosensor is found to be $sim$ 0.63 for the keratin biomolecule. Although the proposed biosensor shows almost the same selectivity, the proposed DM ETL-TFET biosensor does not need a complex fabrication process flow, hence, reducing the fabrication cost. Our findings that the proposed biosensor is a lucrative alternative to the FET-based biosensors.
{"title":"High-Performance Dielectric Modulated Epitaxial Tunnel Layer Tunnel FET for Label-Free Detection of Biomolecules","authors":"Kunal Aggarwal;Avinash Lahgere","doi":"10.1109/OJNANO.2024.3494714","DOIUrl":"https://doi.org/10.1109/OJNANO.2024.3494714","url":null,"abstract":"In this paper, using calibrated simulation we have reported a dielectric modulated epitaxial tunnel layer TFET (DM ETL-TFET) for the label-free detection of biomolecules. We have shown that due to vertical tunneling direction, the ETL-TFET exhibits \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00003 orders of improvement in the ON-state current in comparison to its counterpart conventional TFET. In addition, the proposed DM ETL-TFET biosensor shows \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00004 orders, and \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00001 order higher ON-state current sensitivity than the past reported core-shell junctionless NT-TFET, and DM NT-TFET biosensors, respectively. Moreover, in comparison to the lateral DM TFET, the proposed DM ETL-TFET shows \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u0000310 mV higher threshold voltage sensitivity. Also, the subthreshold swing sensitivity of the proposed biosensor is found to be \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00000.63 for the keratin biomolecule. Although the proposed biosensor shows almost the same selectivity, the proposed DM ETL-TFET biosensor does not need a complex fabrication process flow, hence, reducing the fabrication cost. Our findings that the proposed biosensor is a lucrative alternative to the FET-based biosensors.","PeriodicalId":446,"journal":{"name":"IEEE Open Journal of Nanotechnology","volume":"5 ","pages":"116-123"},"PeriodicalIF":1.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672185","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}
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