本文通过分析碱金属原子在原子气室中的自旋弛豫作用,得出了原子磁力仪灵敏度上限受气室尺寸影响的理论模型。计算了不同气室尺寸下,工作物质为87Rb、工作温度为383.15 K时缓冲气体Ar的最优压强,此压强值随气室尺寸减小而快速增大。在...本文通过分析碱金属原子在原子气室中的自旋弛豫作用,得出了原子磁力仪灵敏度上限受气室尺寸影响的理论模型。计算了不同气室尺寸下,工作物质为87Rb、工作温度为383.15 K时缓冲气体Ar的最优压强,此压强值随气室尺寸减小而快速增大。在此基础上,计算了不同气室尺寸下磁力仪灵敏度上限。结果表明,磁力仪灵敏度上限随原子气室尺寸减小而快速恶化,当气室直径由1 cm减小到0.1 cm时,磁力仪灵敏度上限由0.4 p T Hz-1/2恶化为15 p T Hz-1/2。展开更多
AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by ...AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.展开更多
文摘本文通过分析碱金属原子在原子气室中的自旋弛豫作用,得出了原子磁力仪灵敏度上限受气室尺寸影响的理论模型。计算了不同气室尺寸下,工作物质为87Rb、工作温度为383.15 K时缓冲气体Ar的最优压强,此压强值随气室尺寸减小而快速增大。在此基础上,计算了不同气室尺寸下磁力仪灵敏度上限。结果表明,磁力仪灵敏度上限随原子气室尺寸减小而快速恶化,当气室直径由1 cm减小到0.1 cm时,磁力仪灵敏度上限由0.4 p T Hz-1/2恶化为15 p T Hz-1/2。
基金Project supported in part by the National Key Research and Development Program of China(Grant No.2021YFA0716400)the National Natural Science Foundation of China(Grant Nos.62225405,62150027,61974080,61991443,61975093,61927811,61875104,62175126,and 62235011)+2 种基金the Ministry of Science and Technology of China(Grant Nos.2021ZD0109900 and 2021ZD0109903)the Collaborative Innovation Center of Solid-State Lighting and Energy-Saving ElectronicsTsinghua University Initiative Scientific Research Program.
文摘AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.
