In order to improve the efficiency of embedded software running on processor core, this paper proposes a hard-ware/software co-optimization approach for embedded software from the system point of view. The proposed st...In order to improve the efficiency of embedded software running on processor core, this paper proposes a hard-ware/software co-optimization approach for embedded software from the system point of view. The proposed stepwise methods aim at exploiting the structure and the resources of the processor as much as possible for software algorithm optimization. To achieve low memory usage and low frequency need for the same performance, this co-optimization approach was used to optimize embedded software of MP3 decoder based on a 16-bit fixed-point DSP core. After the optimization, the results of decoding 128 kbps, 44.1 kHz stereo MP3 on DSP evaluation platform need 45.9 MIPS and 20.4 kbytes memory space. The optimization rate achieves 65.6% for memory and 49.6% for frequency respectively compared with the results by compiler using floating-point computation. The experimental result indicates the availability of the hardware/software co-optimization approach depending on the algorithm and architecture.展开更多
Extreme ultraviolet(EUV)lithography with high numerical aperture(NA)is a future technology to manufacture the integrated circuit in sub-nanometer dimension.Meanwhile,source mask co-optimization(SMO)is an extensively u...Extreme ultraviolet(EUV)lithography with high numerical aperture(NA)is a future technology to manufacture the integrated circuit in sub-nanometer dimension.Meanwhile,source mask co-optimization(SMO)is an extensively used approach for advanced lithography process beyond 28 nm technology node.This work proposes a novel SMO method to improve the image fidelity of high-NA EUV lithography system.A fast high-NA EUV lithography imaging model is established first,which includes the effects of mask three-dimensional structure and anamorphic magnification.Then,this paper develops an efficient SMO method that combines the gradient-based mask optimization algorithm and the compressivesensing-based source optimization algorithm.A mask rule check(MRC)process is further proposed to simplify the optimized mask pattern.Results illustrate that the proposed SMO method can significantly reduce the lithography patterning error,and maintain high computational efficiency.展开更多
The power and transportation systems are urban interdependent critical infrastructures(CIs).During the post-disaster restoration process,transportation mobility and power restoration process are interdependent,and the...The power and transportation systems are urban interdependent critical infrastructures(CIs).During the post-disaster restoration process,transportation mobility and power restoration process are interdependent,and their functionalities significantly affect the well-beings of other urban CIs.Therefore,to enhance the resilience of urban CIs,successful recovery strategies should promote CI function cooperatively and synergistically to distribute goods and services efficiently.This paper develops an integrative framework that addresses the challenges of enhancing the recovery efficiency of urban power and transportation systems in short-term recovery period.Specifically,the post-storm recovery process is considered as a scheduling problem with the constraints representingcrew dispatch,equipment and fuel limit.We propose a new framework for co-optimizing the recovery scheduling of power and transportation systems,respecting precedency requirement and network constraints.The advantages and benefits of co-optimized recovery scheduling are validated in a testing system.展开更多
Energy efficiency is one of the most important issues for High Performance Computing（HPC） today.Heterogeneous HPC platform with some energy-efficient customizable cores（as application-specific accelerators）is beli...Energy efficiency is one of the most important issues for High Performance Computing（HPC） today.Heterogeneous HPC platform with some energy-efficient customizable cores（as application-specific accelerators）is believed as one of the promising solutions to meet ever-increasing computing needs and to overcome power density limitations. In this paper, we focus on using customizable processor cores to optimize the typical stencil computations—— the kernel of many high-performance applications. We develop a series of effective software/hardware co-optimization strategies to exploit the instruction-level and memory-computation parallelism,as well as to decrease the energy consumption. These optimizations include loop tiling, prefetching, cache customization, Single Instruction Multiple Data（SIMD）, and Direct Memory Access（DMA）, as well as necessary ISA extensions. Detailed tests of power-efficiency are given to evaluate the effect of all these optimizations comprehensively. The results are impressive： the combination of these optimizations has improved the application performance by 341% while the energy consumption has been decreased by 35%; a preliminary comparison with X86, GPU, and FPGA platforms also showed that the design could achieve an order of magnitude higher performance efficiency. We believe this work can help understand sources of inefficiency in general-purpose chips and can be used as a beginning to customize an energy efficient CMP for further improvement.展开更多
Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal,spatial,and cross-domain factors.A novel framewo...Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal,spatial,and cross-domain factors.A novel framework is proposed,grounded in the cyber-physical-social system in energy(CPSSE)and whole-reductionism thinking(WRT),incorporating a tailored mathematical model and optimization method to formalize the co-optimization of carbon reduction and carbon sequestration in the power sector.Using the carbon peaking and carbon neutrality transition of China as a case study,clustering method is employed to construct a diverse set of strategically distinct carbon trajectories.For each trajectory,the evolution of the generation mix and the deployment pathways of carbon capture and storage(CCS)technologies are analyzed,identifying the optimal transition pathway based on the criterion of minimizing cumulative economic costs.Further,by comparing non-fossil energy substitution and CCS retrofitting in thermal power,the analysis highlights the potential for co-optimization of carbon reduction and carbon sequestration.The results demonstrate that leveraging the spatiotemporal complementarities between the two can substantially lower the economic cost of achieving carbon neutrality,providing insights for integrated decarbonization strategies in power system planning.展开更多
As the IC manufacturing enter sub 20nm tech nodes,DFM become more and more important to make sure more stable yield and lower cost.However,by introducing newly designed hardware(1980i etc.)process chemical(NTD)and Con...As the IC manufacturing enter sub 20nm tech nodes,DFM become more and more important to make sure more stable yield and lower cost.However,by introducing newly designed hardware(1980i etc.)process chemical(NTD)and Control Algorithm(Focus APC)into the mature tech nodes such as 14nm/12nm,more process window and less process variations are expected for latecomer wafer fabs(Tier-2/3 companies)who just started the competition with Tier-1 companies.With improved weapons,latecomer companies are able to review their DFM strategy one more time to see whether the benefit from hardware/process/control algorithm improvement can be shared with designers.In this paper,we use OPC simulation tools from different EDA suppliers to see the feasibility of transferring the benefits of hardware/process/control algorithm improvement to more relaxed design limitation through source mask optimization(SMO):1)Better hardware:scanner(better focus/exposure variation),CMP(intrafield topo),Mask CD variation(relaxed MEEF spec),etc.2) New process:from positive tone development to negative tone development.3)Better control schemes:holistic focus feedback,feedback/forward overlay control,high order CD uniformity improvement.Simulations show all those gains in hardware and process can be transferred into more relaxed design such as sub design rule structure process window include forbidden pitches(1D)and smaller E2E gaps(2D weak points).展开更多
Although power grids have become safer with increased situational awareness,major extreme events still pose reliability and resilience challenges,primarily at the distribution level,due to increased vulnerabilities an...Although power grids have become safer with increased situational awareness,major extreme events still pose reliability and resilience challenges,primarily at the distribution level,due to increased vulnerabilities and limited recovery resources.Information and communication technologies(ICTs)have introduced new vulnerabilities that have been widely investigated in previous studies.These vulnerabilities include remote device failures,communication channel disturbances,and cyberattacks.However,only few studies have explored the opportunity offered by communications to improve the resilience of power grids and eliminate the notion that power-telecom interdependencies always pose a threat.This paper proposes a communication-aware restoration approach of smart distribution grids,which leverages power-telecom interdependencies to determine the optimal restoration strategies.The states of grid-energized telecom points are tracked to provide the best restoration actions,which are enabled through the resilience resources of repair,manual switching,remote reconfiguration,and distributed generators.As the telecom network coordinates the allocation of these resilience resources based on their coupling tendencies,different telecom architectures have been introduced to investigate the contribution of private and public ICTs to grid management and restoration operations.System restoration uses the configuration that follows a remote fast response as the input to formulate the problem as mixed-integer linear programming.Results from numerical simulations reveal an enhanced restoration process derived from telecom-aware recovery and the co-optimization of resilience resources.The existing disparity between overhead and underground power line configurations is also quantified.展开更多
Advancements in the semiconductor industry introduce novel channel materials,device structures,and integration methods,leading to intricate physics challenges when characterizing devices at circuit level.Nevertheless,...Advancements in the semiconductor industry introduce novel channel materials,device structures,and integration methods,leading to intricate physics challenges when characterizing devices at circuit level.Nevertheless,accurate models for emerging devices are crucial for physics-driven TCAD-to-SPICE flows to enable the increasingly vital design technology co-optimization(DTCO).Particularly for ultra-scaled devices where quantum effects become significant,this led to the introduction of empirical model parameters and a disconnection to manufacturing processes.To catch up with these developments,an alternative to the traditional white-box modeling methods has attracted much attention:machine learning-assisted compact modeling(MLCM).These black-box methods target towards general-purpose modeling of complex mathematics and physics through training of neural networks on experimental and simulated data,generating an accurate closed-form mapping between output characteristics and input parameters for fabrication process and device operation.To address this new trend,this work provides a comprehensive overview of emerging device model methodologies,spanning from device physics to machine learning engines.By analyzing,structuring,and extending distributed efforts on this topic,it is shown how MLCM can overcome limitations of traditional compact modeling and contribute to effective DTCO to further advance semiconductor technologies.展开更多
The photonic Ising machine, a promising non-von Neumann computational paradigm, offers a feasible way to address combinatorial optimization problems. We develop a digital noise injection method for spatial photonic Is...The photonic Ising machine, a promising non-von Neumann computational paradigm, offers a feasible way to address combinatorial optimization problems. We develop a digital noise injection method for spatial photonic Ising machines based on smoothed analysis, where noise level acts as a parameter that quantifies the smoothness degree. Through experiments with 20736-node Max-Cut problems, we establish a stable performance within a smoothness degree of 0.04 to 0.07. Digital noise injection results in a 24% performance enhancement, showing a 73% improvement over heuristic Sahni–Gonzales (SG) algorithms. Furthermore, to address noise-induced instability concerns, we propose an optoelectronic co-optimization method for a more streamlined smoothing method with strong stability.展开更多
Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system(DS)in extreme weather and with equipment faults.Previous studies have separately considered network re...Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system(DS)in extreme weather and with equipment faults.Previous studies have separately considered network reconfigu-ration(NR)and dispatching mobile power sources(MPS)to restore the outage load.However,NR cannot deal with the scenario of an electrical island,while dispatching MPS results in a long power outage.In this paper,a resilient outage recovery method based on co-optimizing MPS and NR is proposed,where the DS and traffic network(TN)are considered simultaneously.In the DS,the switch action cost and power losses are minimized,and the access points of MPSs are changed by carrying out the NR process.In the TN,an MPS dispatching model with the objective of mini-mizing power outage time,routing and power generation cost is developed to optimize the MPSs’schedule.A solu-tion algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy.Finally,numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method.It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS.The results also show that the system operation cost can be reduced by considering power losses in the objective function.展开更多
Low temperature complementary metal oxide semiconductor(CMOS)or cryogenic CMOS is a promising avenue for the continuation of Moore’s law while serving the needs of high performance computing.With temperature as a con...Low temperature complementary metal oxide semiconductor(CMOS)or cryogenic CMOS is a promising avenue for the continuation of Moore’s law while serving the needs of high performance computing.With temperature as a control“knob”to steepen the subthreshold slope behavior of CMOS devices,the supply voltage of operation can be reduced with no impact on operating speed.With the optimal threshold voltage engineering,the device ON current can be further enhanced,translating to higher performance.In this article,the experimentally calibrated data was adopted to tune the threshold voltage and investigated the power performance area of cryogenic CMOS at device,circuit and system level.We also presented results from measurement and analysis of functional memory chips fabricated in 28 nm bulk CMOS and 22 nm fully depleted silicon on insulator(FDSOI)operating at cryogenic temperature.Finally,the challenges and opportunities in the further development and deployment of such systems were discussed.展开更多
As a significant clean energy source, natural gas plays an important role in modern energy context. The growing utilization of natural gas brings uncertainties into the power system, which requires an integrated way t...As a significant clean energy source, natural gas plays an important role in modern energy context. The growing utilization of natural gas brings uncertainties into the power system, which requires an integrated way to plan natural gas and power systems. In this paper, the co-planning process is formulated as a mixed integer nonlinear programming problem to address emerging challenges,such as system reliability evaluation, market time line mismatch, market uncertainties, demand response effect,etc. An innovative expansion co-planning(ECP) framework is established in this paper to find the best augmentation plan which comes with the minimum cost.Specifically, to cope with uncertainties in market share,decision analysis is introduced. Meanwhile, the energy conversion efficiency between gas and electricity in the coupled load center is considered in the ECP constraints.Comprehensive case studies are applied to validate the performance of proposed approach.展开更多
Electromagnetic wave-absorbing(EMA)materials at high temperatures are limited by poor conduction loss(L_(c)).However,adding conductors simultaneously increases the conduction loss and interfacial polarization loss,lea...Electromagnetic wave-absorbing(EMA)materials at high temperatures are limited by poor conduction loss(L_(c)).However,adding conductors simultaneously increases the conduction loss and interfacial polarization loss,leading to a conflict between impedance matching(Z_(in)/Z_(0))and electromagnetic wave loss.This will prevent electromagnetic waves from entering the EMA materials,finally reducing overall absorbing performance.Here,the effective electrical conductivity(σ)is enhanced by synchronizing particle size and grain number of Ti_(3)AlC_(2) to increase the conduction loss and avoid the conflict between the impedance matching and the electromagnetic wave loss.As a result,the best-absorbing performance with an effective absorption bandwidth(EAB)of 4.8 GHz(10.6–15.4 GHz)at a thickness of only 1.5 mm is realized,which is the best combination of wide absorption bandwidth and small thickness,and the minimum reflection loss(RL_(min))reaches−45.6 dB at 4.1 GHz.In short,this work explores the regulating mechanism of the EMA materials of effective electrical conductivity by simulated calculations using the Vienna ab-initio Simulation Package(VASP)and COMSOL as well as a series of experiments,which provide new insight into a rational design of materials with anisotropic electrical conductivity.展开更多
The standard design technology co-optimization(DTCO)involves frequent interactions between circuit design and process manufacturing,which requires several months.To assist designers in establishing a bridge between de...The standard design technology co-optimization(DTCO)involves frequent interactions between circuit design and process manufacturing,which requires several months.To assist designers in establishing a bridge between device parameters and circuit metrics efficiently,and provide guidance for parameter optimization in the early stages of circuit design.In this paper,we propose an efficient machine learning(ML)-enhanced DTCO framework.This framework achieves the co-optimization of device parameters and circuit metrics.We select the gate metal work function(WF)as the parameter to validate the effectiveness of our framework.And the ridge regression approach is used to bypass TCAD simulation,compact model extraction and cell library characterization.We reduces time consumption by at least 92%compared to traditional DTCO framework,while ensuring that errors of delay,internal power consumption and leakage power below 4 ps,0.035mJ,and 0.4μW,respectively.By adjusting the WF,we achieved a better balance between circuit delay and power consumption.This work contributes to designers exploring a broader design space and achieving a efficient DTCO flow.展开更多
基金Project supported by the Key-Tech Program of Zhejiang Province,China (No. 021101559), and the Fok Ying Tong Education Founda-tion (No. 94031), China
文摘In order to improve the efficiency of embedded software running on processor core, this paper proposes a hard-ware/software co-optimization approach for embedded software from the system point of view. The proposed stepwise methods aim at exploiting the structure and the resources of the processor as much as possible for software algorithm optimization. To achieve low memory usage and low frequency need for the same performance, this co-optimization approach was used to optimize embedded software of MP3 decoder based on a 16-bit fixed-point DSP core. After the optimization, the results of decoding 128 kbps, 44.1 kHz stereo MP3 on DSP evaluation platform need 45.9 MIPS and 20.4 kbytes memory space. The optimization rate achieves 65.6% for memory and 49.6% for frequency respectively compared with the results by compiler using floating-point computation. The experimental result indicates the availability of the hardware/software co-optimization approach depending on the algorithm and architecture.
基金financially supported by National Natural Science Foundation of China (No. 62274181,62204257 and 62374016)Chinese Ministry of Science and Technology (No. 2019YFB2205005)+4 种基金Guangdong Province Research and Development Program in Key Fields (No. 2021B0101280002)the support from Youth Innovation Promotion Association Chinese Academy of Sciences (No. 2021115)Beijing Institute of ElectronicsBeijing Association for Science and Technology as well,the support from University of Chinese Academy of Sciences (No. 118900M032)China Fundamental Research Funds for the Central Universities (No. E2ET3801)
文摘Extreme ultraviolet(EUV)lithography with high numerical aperture(NA)is a future technology to manufacture the integrated circuit in sub-nanometer dimension.Meanwhile,source mask co-optimization(SMO)is an extensively used approach for advanced lithography process beyond 28 nm technology node.This work proposes a novel SMO method to improve the image fidelity of high-NA EUV lithography system.A fast high-NA EUV lithography imaging model is established first,which includes the effects of mask three-dimensional structure and anamorphic magnification.Then,this paper develops an efficient SMO method that combines the gradient-based mask optimization algorithm and the compressivesensing-based source optimization algorithm.A mask rule check(MRC)process is further proposed to simplify the optimized mask pattern.Results illustrate that the proposed SMO method can significantly reduce the lithography patterning error,and maintain high computational efficiency.
基金supported by the U.S.National Science Foundation Project(No.ECCS-171121)CARRER Award(No.CMMI-1554559)CSUFRD-IoT Award.
文摘The power and transportation systems are urban interdependent critical infrastructures(CIs).During the post-disaster restoration process,transportation mobility and power restoration process are interdependent,and their functionalities significantly affect the well-beings of other urban CIs.Therefore,to enhance the resilience of urban CIs,successful recovery strategies should promote CI function cooperatively and synergistically to distribute goods and services efficiently.This paper develops an integrative framework that addresses the challenges of enhancing the recovery efficiency of urban power and transportation systems in short-term recovery period.Specifically,the post-storm recovery process is considered as a scheduling problem with the constraints representingcrew dispatch,equipment and fuel limit.We propose a new framework for co-optimizing the recovery scheduling of power and transportation systems,respecting precedency requirement and network constraints.The advantages and benefits of co-optimized recovery scheduling are validated in a testing system.
基金supported by the National HighTech Research and Development (863) Program of China (No. 2013AA01A215)the Brain Inspired Computing Research of Tsinghua University (No. 20141080934)
文摘Energy efficiency is one of the most important issues for High Performance Computing（HPC） today.Heterogeneous HPC platform with some energy-efficient customizable cores（as application-specific accelerators）is believed as one of the promising solutions to meet ever-increasing computing needs and to overcome power density limitations. In this paper, we focus on using customizable processor cores to optimize the typical stencil computations—— the kernel of many high-performance applications. We develop a series of effective software/hardware co-optimization strategies to exploit the instruction-level and memory-computation parallelism,as well as to decrease the energy consumption. These optimizations include loop tiling, prefetching, cache customization, Single Instruction Multiple Data（SIMD）, and Direct Memory Access（DMA）, as well as necessary ISA extensions. Detailed tests of power-efficiency are given to evaluate the effect of all these optimizations comprehensively. The results are impressive： the combination of these optimizations has improved the application performance by 341% while the energy consumption has been decreased by 35%; a preliminary comparison with X86, GPU, and FPGA platforms also showed that the design could achieve an order of magnitude higher performance efficiency. We believe this work can help understand sources of inefficiency in general-purpose chips and can be used as a beginning to customize an energy efficient CMP for further improvement.
基金supported in part by the State Grid Corporation of China(SGCC)“Research on the Sand-table Deduction and Risk Decision-making Technology of the Power System Actively Supporting the Energy Transition and Dual-carbon Revolution”(No.524608220268)。
文摘Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal,spatial,and cross-domain factors.A novel framework is proposed,grounded in the cyber-physical-social system in energy(CPSSE)and whole-reductionism thinking(WRT),incorporating a tailored mathematical model and optimization method to formalize the co-optimization of carbon reduction and carbon sequestration in the power sector.Using the carbon peaking and carbon neutrality transition of China as a case study,clustering method is employed to construct a diverse set of strategically distinct carbon trajectories.For each trajectory,the evolution of the generation mix and the deployment pathways of carbon capture and storage(CCS)technologies are analyzed,identifying the optimal transition pathway based on the criterion of minimizing cumulative economic costs.Further,by comparing non-fossil energy substitution and CCS retrofitting in thermal power,the analysis highlights the potential for co-optimization of carbon reduction and carbon sequestration.The results demonstrate that leveraging the spatiotemporal complementarities between the two can substantially lower the economic cost of achieving carbon neutrality,providing insights for integrated decarbonization strategies in power system planning.
文摘As the IC manufacturing enter sub 20nm tech nodes,DFM become more and more important to make sure more stable yield and lower cost.However,by introducing newly designed hardware(1980i etc.)process chemical(NTD)and Control Algorithm(Focus APC)into the mature tech nodes such as 14nm/12nm,more process window and less process variations are expected for latecomer wafer fabs(Tier-2/3 companies)who just started the competition with Tier-1 companies.With improved weapons,latecomer companies are able to review their DFM strategy one more time to see whether the benefit from hardware/process/control algorithm improvement can be shared with designers.In this paper,we use OPC simulation tools from different EDA suppliers to see the feasibility of transferring the benefits of hardware/process/control algorithm improvement to more relaxed design limitation through source mask optimization(SMO):1)Better hardware:scanner(better focus/exposure variation),CMP(intrafield topo),Mask CD variation(relaxed MEEF spec),etc.2) New process:from positive tone development to negative tone development.3)Better control schemes:holistic focus feedback,feedback/forward overlay control,high order CD uniformity improvement.Simulations show all those gains in hardware and process can be transferred into more relaxed design such as sub design rule structure process window include forbidden pitches(1D)and smaller E2E gaps(2D weak points).
基金supported by EDF/Orange/SNCF in the framework of the Chair on Risk and Resilience of Complex Systems(CentraleSupelec,EDF,Orange,SNCF).
文摘Although power grids have become safer with increased situational awareness,major extreme events still pose reliability and resilience challenges,primarily at the distribution level,due to increased vulnerabilities and limited recovery resources.Information and communication technologies(ICTs)have introduced new vulnerabilities that have been widely investigated in previous studies.These vulnerabilities include remote device failures,communication channel disturbances,and cyberattacks.However,only few studies have explored the opportunity offered by communications to improve the resilience of power grids and eliminate the notion that power-telecom interdependencies always pose a threat.This paper proposes a communication-aware restoration approach of smart distribution grids,which leverages power-telecom interdependencies to determine the optimal restoration strategies.The states of grid-energized telecom points are tracked to provide the best restoration actions,which are enabled through the resilience resources of repair,manual switching,remote reconfiguration,and distributed generators.As the telecom network coordinates the allocation of these resilience resources based on their coupling tendencies,different telecom architectures have been introduced to investigate the contribution of private and public ICTs to grid management and restoration operations.System restoration uses the configuration that follows a remote fast response as the input to formulate the problem as mixed-integer linear programming.Results from numerical simulations reveal an enhanced restoration process derived from telecom-aware recovery and the co-optimization of resilience resources.The existing disparity between overhead and underground power line configurations is also quantified.
基金supported in part by the Strategic Priority Research Program of Chinese Academy of Sciences(XDA0330401)the National Natural Science Foundation of China(62274178,92264204)CAS Interdisciplinary Innovation Team(JCTD-2022-07).
文摘Advancements in the semiconductor industry introduce novel channel materials,device structures,and integration methods,leading to intricate physics challenges when characterizing devices at circuit level.Nevertheless,accurate models for emerging devices are crucial for physics-driven TCAD-to-SPICE flows to enable the increasingly vital design technology co-optimization(DTCO).Particularly for ultra-scaled devices where quantum effects become significant,this led to the introduction of empirical model parameters and a disconnection to manufacturing processes.To catch up with these developments,an alternative to the traditional white-box modeling methods has attracted much attention:machine learning-assisted compact modeling(MLCM).These black-box methods target towards general-purpose modeling of complex mathematics and physics through training of neural networks on experimental and simulated data,generating an accurate closed-form mapping between output characteristics and input parameters for fabrication process and device operation.To address this new trend,this work provides a comprehensive overview of emerging device model methodologies,spanning from device physics to machine learning engines.By analyzing,structuring,and extending distributed efforts on this topic,it is shown how MLCM can overcome limitations of traditional compact modeling and contribute to effective DTCO to further advance semiconductor technologies.
基金supported by the National Natural Science Foundation of China(Nos.62235011 and 62175146).
文摘The photonic Ising machine, a promising non-von Neumann computational paradigm, offers a feasible way to address combinatorial optimization problems. We develop a digital noise injection method for spatial photonic Ising machines based on smoothed analysis, where noise level acts as a parameter that quantifies the smoothness degree. Through experiments with 20736-node Max-Cut problems, we establish a stable performance within a smoothness degree of 0.04 to 0.07. Digital noise injection results in a 24% performance enhancement, showing a 73% improvement over heuristic Sahni–Gonzales (SG) algorithms. Furthermore, to address noise-induced instability concerns, we propose an optoelectronic co-optimization method for a more streamlined smoothing method with strong stability.
基金National Key R&D Program of China (2020YFF0305800)Science and Technology Project of SGCC (520201210025).
文摘Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system(DS)in extreme weather and with equipment faults.Previous studies have separately considered network reconfigu-ration(NR)and dispatching mobile power sources(MPS)to restore the outage load.However,NR cannot deal with the scenario of an electrical island,while dispatching MPS results in a long power outage.In this paper,a resilient outage recovery method based on co-optimizing MPS and NR is proposed,where the DS and traffic network(TN)are considered simultaneously.In the DS,the switch action cost and power losses are minimized,and the access points of MPSs are changed by carrying out the NR process.In the TN,an MPS dispatching model with the objective of mini-mizing power outage time,routing and power generation cost is developed to optimize the MPSs’schedule.A solu-tion algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy.Finally,numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method.It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS.The results also show that the system operation cost can be reduced by considering power losses in the objective function.
基金funded by the Defense Advanced Research Project Agency(DARPA)Low Temperature Logic Technology(LTLT)program.
文摘Low temperature complementary metal oxide semiconductor(CMOS)or cryogenic CMOS is a promising avenue for the continuation of Moore’s law while serving the needs of high performance computing.With temperature as a control“knob”to steepen the subthreshold slope behavior of CMOS devices,the supply voltage of operation can be reduced with no impact on operating speed.With the optimal threshold voltage engineering,the device ON current can be further enhanced,translating to higher performance.In this article,the experimentally calibrated data was adopted to tune the threshold voltage and investigated the power performance area of cryogenic CMOS at device,circuit and system level.We also presented results from measurement and analysis of functional memory chips fabricated in 28 nm bulk CMOS and 22 nm fully depleted silicon on insulator(FDSOI)operating at cryogenic temperature.Finally,the challenges and opportunities in the further development and deployment of such systems were discussed.
基金supported in part by funding from the Faculty of Engineering&Information Technologies,The University of Sydney,under the Mid-career Researcher Development Schemein part by the ARC Discovery Grant(No.DP170103427)in part by the 2015 Science and Technology Project of China Southern Power Grid(No.WYKJ00000027)
文摘As a significant clean energy source, natural gas plays an important role in modern energy context. The growing utilization of natural gas brings uncertainties into the power system, which requires an integrated way to plan natural gas and power systems. In this paper, the co-planning process is formulated as a mixed integer nonlinear programming problem to address emerging challenges,such as system reliability evaluation, market time line mismatch, market uncertainties, demand response effect,etc. An innovative expansion co-planning(ECP) framework is established in this paper to find the best augmentation plan which comes with the minimum cost.Specifically, to cope with uncertainties in market share,decision analysis is introduced. Meanwhile, the energy conversion efficiency between gas and electricity in the coupled load center is considered in the ECP constraints.Comprehensive case studies are applied to validate the performance of proposed approach.
文摘Electromagnetic wave-absorbing(EMA)materials at high temperatures are limited by poor conduction loss(L_(c)).However,adding conductors simultaneously increases the conduction loss and interfacial polarization loss,leading to a conflict between impedance matching(Z_(in)/Z_(0))and electromagnetic wave loss.This will prevent electromagnetic waves from entering the EMA materials,finally reducing overall absorbing performance.Here,the effective electrical conductivity(σ)is enhanced by synchronizing particle size and grain number of Ti_(3)AlC_(2) to increase the conduction loss and avoid the conflict between the impedance matching and the electromagnetic wave loss.As a result,the best-absorbing performance with an effective absorption bandwidth(EAB)of 4.8 GHz(10.6–15.4 GHz)at a thickness of only 1.5 mm is realized,which is the best combination of wide absorption bandwidth and small thickness,and the minimum reflection loss(RL_(min))reaches−45.6 dB at 4.1 GHz.In short,this work explores the regulating mechanism of the EMA materials of effective electrical conductivity by simulated calculations using the Vienna ab-initio Simulation Package(VASP)and COMSOL as well as a series of experiments,which provide new insight into a rational design of materials with anisotropic electrical conductivity.
基金supported by the Cooperation Project between Xidian University and Shenzhen Fuxin Technology Company Ltd.(Electronic Design Automation Technology Innovation Center Project in Guangdong-Hong Kong Macao Greater Bay Area)well as by the Project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory(6142806230302).
文摘The standard design technology co-optimization(DTCO)involves frequent interactions between circuit design and process manufacturing,which requires several months.To assist designers in establishing a bridge between device parameters and circuit metrics efficiently,and provide guidance for parameter optimization in the early stages of circuit design.In this paper,we propose an efficient machine learning(ML)-enhanced DTCO framework.This framework achieves the co-optimization of device parameters and circuit metrics.We select the gate metal work function(WF)as the parameter to validate the effectiveness of our framework.And the ridge regression approach is used to bypass TCAD simulation,compact model extraction and cell library characterization.We reduces time consumption by at least 92%compared to traditional DTCO framework,while ensuring that errors of delay,internal power consumption and leakage power below 4 ps,0.035mJ,and 0.4μW,respectively.By adjusting the WF,we achieved a better balance between circuit delay and power consumption.This work contributes to designers exploring a broader design space and achieving a efficient DTCO flow.
