Carbon Nanotubes(CNT)in nanotechnology field are legendary for its strength and chemical inertness.Technically,we can alter carbon nanotubes based on our necessities and requirements such as single layered nanotube,do...Carbon Nanotubes(CNT)in nanotechnology field are legendary for its strength and chemical inertness.Technically,we can alter carbon nanotubes based on our necessities and requirements such as single layered nanotube,double layered nanotube,multi layered nanotube etc.In this paper usage of carbon nanotubes in semiconductor devices such as nanomaterials,molecular dynamics of nanomaterials,heterojunctions using carbon nanotubes,diodes and Graphene Field Effect Transistor(GFET),its characteristics and data analysis are discussed.The major application of carbon nanotubes in electronic circuits is not limiting to improves the electrical and thermal conductivity due to its high stretchability feature and they also have a long life span and better durability over traditional electronic circuit’s materials.展开更多
Code converters are essential in digital nano communication;therefore,a low-complexity optimal QCA layout for a BCD to Excess-3 code converter has been proposed in this paper.A QCA clockphase-based design technique wa...Code converters are essential in digital nano communication;therefore,a low-complexity optimal QCA layout for a BCD to Excess-3 code converter has been proposed in this paper.A QCA clockphase-based design technique was adopted to investigate integration with other complicated circuits.Using a unique XOR gate,the recommended circuit’s cell complexity has been decreased.The findings produced using the QCADesigner-2.0.3,a reliable simulation tool,prove the effectiveness of the current structure over earlier designs by considering the number of cells deployed,the area occupied,and the latency as design metrics.In addition,the popular tool QCAPro was used to estimate the energy dissipation of the proposed design.The proposed technique reduces the occupied space by∼40%,improves cell complexity by∼20%,and reduces energy dissipation by∼1.8 times(atγ=1.5EK)compared to the current scalable designs.This paper also studied the suggested structure’s energy dissipation and compared it to existing works for a better performance evaluation.展开更多
Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working circuit.PCBs play a crucial role in modern electronics by connecting various components.The trend of integrating mo...Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working circuit.PCBs play a crucial role in modern electronics by connecting various components.The trend of integrating more components onto PCBs is becoming increasingly common,which presents significant challenges for quality control processes.Given the potential impact that even minute defects can have on signal traces,the surface inspection of PCB remains pivotal in ensuring the overall system integrity.To address the limitations associated with manual inspection,this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm for real-time fault detection in PCBs.Specifically,we explore the effectiveness of two variants of the YOLOv8 architecture:YOLOv8 Small and YOLOv8 Nano.Through rigorous experimentation and evaluation of our dataset which was acquired from Peking University’s Human-Robot Interaction Lab,we aim to assess the suitability of these models for improving fault detection accuracy within the PCB manufacturing process.Our results reveal the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB faults.The model achieved a precision of 98.7%,a recall of 99%,an accuracy of 98.6%,and an F1 score of 0.98.These findings highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB manufacturing by providing a reliable and efficient solution for fault detection.展开更多
文摘Carbon Nanotubes(CNT)in nanotechnology field are legendary for its strength and chemical inertness.Technically,we can alter carbon nanotubes based on our necessities and requirements such as single layered nanotube,double layered nanotube,multi layered nanotube etc.In this paper usage of carbon nanotubes in semiconductor devices such as nanomaterials,molecular dynamics of nanomaterials,heterojunctions using carbon nanotubes,diodes and Graphene Field Effect Transistor(GFET),its characteristics and data analysis are discussed.The major application of carbon nanotubes in electronic circuits is not limiting to improves the electrical and thermal conductivity due to its high stretchability feature and they also have a long life span and better durability over traditional electronic circuit’s materials.
文摘Code converters are essential in digital nano communication;therefore,a low-complexity optimal QCA layout for a BCD to Excess-3 code converter has been proposed in this paper.A QCA clockphase-based design technique was adopted to investigate integration with other complicated circuits.Using a unique XOR gate,the recommended circuit’s cell complexity has been decreased.The findings produced using the QCADesigner-2.0.3,a reliable simulation tool,prove the effectiveness of the current structure over earlier designs by considering the number of cells deployed,the area occupied,and the latency as design metrics.In addition,the popular tool QCAPro was used to estimate the energy dissipation of the proposed design.The proposed technique reduces the occupied space by∼40%,improves cell complexity by∼20%,and reduces energy dissipation by∼1.8 times(atγ=1.5EK)compared to the current scalable designs.This paper also studied the suggested structure’s energy dissipation and compared it to existing works for a better performance evaluation.
文摘Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working circuit.PCBs play a crucial role in modern electronics by connecting various components.The trend of integrating more components onto PCBs is becoming increasingly common,which presents significant challenges for quality control processes.Given the potential impact that even minute defects can have on signal traces,the surface inspection of PCB remains pivotal in ensuring the overall system integrity.To address the limitations associated with manual inspection,this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm for real-time fault detection in PCBs.Specifically,we explore the effectiveness of two variants of the YOLOv8 architecture:YOLOv8 Small and YOLOv8 Nano.Through rigorous experimentation and evaluation of our dataset which was acquired from Peking University’s Human-Robot Interaction Lab,we aim to assess the suitability of these models for improving fault detection accuracy within the PCB manufacturing process.Our results reveal the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB faults.The model achieved a precision of 98.7%,a recall of 99%,an accuracy of 98.6%,and an F1 score of 0.98.These findings highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB manufacturing by providing a reliable and efficient solution for fault detection.
