A high power buck-boost switch-mode LED driver delivering a constant 350 mA with a power efficient current sensing scheme is presented in this paper. The LED current is extracted by differentiating the output capacito...A high power buck-boost switch-mode LED driver delivering a constant 350 mA with a power efficient current sensing scheme is presented in this paper. The LED current is extracted by differentiating the output capacitor voltage and maintained by a feedback. The circuit has been fabricated in a standard 0.35 μm AMS CMOS process. Measurement results demonstrated a power-conversion efficiency over 90% with a line regulation of 8%/V for input voltage of 3.3 V and current output between 200 mA and 350 mA.展开更多
Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic computing.Recently,memristor-based neu...Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic computing.Recently,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological counterparts.However,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging.Here,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions.The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights.Finally,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this network.This work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.展开更多
The linear cofactor difference operator（LCDO） method,a direct parameter extraction method for general diodes,is presented.With the developed LCDO method,the extreme spectral characteristic of the diode voltage-curre...The linear cofactor difference operator（LCDO） method,a direct parameter extraction method for general diodes,is presented.With the developed LCDO method,the extreme spectral characteristic of the diode voltage-current curves is revealed,and its extreme positions are related to the diode characteristic parameters directly.The method is applied to diodes with different sizes and temperatures,and the related characteristic parameters,such as reverse saturation current,series resistance and non-ideality factor,are extracted directly.The extraction result shows good agreement with the experimental data.展开更多
文摘A high power buck-boost switch-mode LED driver delivering a constant 350 mA with a power efficient current sensing scheme is presented in this paper. The LED current is extracted by differentiating the output capacitor voltage and maintained by a feedback. The circuit has been fabricated in a standard 0.35 μm AMS CMOS process. Measurement results demonstrated a power-conversion efficiency over 90% with a line regulation of 8%/V for input voltage of 3.3 V and current output between 200 mA and 350 mA.
基金The authors thank the National High Technology Research Development Program(2017YFB0405600 and 2018YFA0701500)the National Key R&D Program(2019FYB2205101)+4 种基金the National Natural Science Foundation of China(61825404,61732020,61821091,61851402,61751401,and 61804171)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB44000000)the China Postdoctoral Science Foundation(2020 M681167)the Major Scientific Research Project of Zhejiang Lab(2019KC0AD02)CASCroucher Funding(CAS18EG01 and 172511KYSB20180135).
文摘Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic computing.Recently,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological counterparts.However,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging.Here,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions.The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights.Finally,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this network.This work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.
基金Project supported by the State Key Development Program for Basic Research of China and the National Natural Science Foundation of China(Nos.60936005,60976066)
文摘The linear cofactor difference operator（LCDO） method,a direct parameter extraction method for general diodes,is presented.With the developed LCDO method,the extreme spectral characteristic of the diode voltage-current curves is revealed,and its extreme positions are related to the diode characteristic parameters directly.The method is applied to diodes with different sizes and temperatures,and the related characteristic parameters,such as reverse saturation current,series resistance and non-ideality factor,are extracted directly.The extraction result shows good agreement with the experimental data.
