Recently,research on two-dimensional(2D)semiconductors has begun to translate from the fundamen-tal investigation into rudimentary functional circuits.In this work,we unveil the first functional MoS2 artificial neural...Recently,research on two-dimensional(2D)semiconductors has begun to translate from the fundamen-tal investigation into rudimentary functional circuits.In this work,we unveil the first functional MoS2 artificial neural network(ANN)chip,including multiply-and-accumulate(MAC),memory and activation function circuits.Such MoS2 ANN chip is realized through fabricating 818 field-effect transistors(FETs)on a wafer-scale and high-homogeneity MoS2 film,with a gate-last process to realize top gate structured FETs.A 62-level simulation program with integrated circuit emphasis(SPICE)model is utilized to design and optimize our analog ANN circuits.To demonstrate a practical application,a tactile digit sensing recognition was demonstrated based on our ANN circuits.After training,the digit recognition rate exceeds 97%.Our work not only demonstrates the protentional of 2D semiconductors in wafer-scale inte-grated circuits,but also paves the way for its future application in AI computation.展开更多
Two-dimensional materials with active sites are expected to replace platinum as large-scale hydrogen production catalysts.However,the rapid discovery of excellent two-dimensional hydrogen evolution reaction catalysts ...Two-dimensional materials with active sites are expected to replace platinum as large-scale hydrogen production catalysts.However,the rapid discovery of excellent two-dimensional hydrogen evolution reaction catalysts is seriously hindered due to the long experiment cycle and the huge cost of high-throughput calculations of adsorption energies.Considering that the traditional regression models cannot consider all the potential sites on the surface of catalysts,we use a deep learning method with crystal graph convolutional neural networks to accelerate the discovery of high-performance two-dimensional hydrogen evolution reaction catalysts from two-dimensional materials database,with the prediction accuracy as high as 95.2%.The proposed method considers all active sites,screens out 38 high performance catalysts from 6,531 two-dimensional materials,predicts their adsorption energies at different active sites,and determines the potential strongest adsorption sites.The prediction accuracy of the two-dimensional hydrogen evolution reaction catalysts screening strategy proposed in this work is at the density-functional-theory level,but the prediction speed is 10.19 years ahead of the high-throughput screening,demonstrating the capability of crystal graph convolutional neural networks-deep learning method for efficiently discovering high-performance new structures over a wide catalytic materials space.展开更多
Understanding charge transport mechanisms in thin-film transistors based on random networks of single-wall carbon nanotubes(SWCNT-TFTs)is essential for further advances to improve the potential for various nanoelectro...Understanding charge transport mechanisms in thin-film transistors based on random networks of single-wall carbon nanotubes(SWCNT-TFTs)is essential for further advances to improve the potential for various nanoelectronic applications.Herein,a comprehensive investigation of the two-dimensional(2D)charge transport mechanism in SWCNT-TFTs is reported by analyzing the temperature-dependent electrical characteristics determined from the direct-current and non-quasi-static transient measurements at 80-300 K.To elucidate the time-domain charge transport characteristics of the random networks in the SWCNTs,an empirical equation was derived from a theoretical trapping model,and a carrier velocity distribution was determined from the differentiation of the transient response.Furthermore,charge trapping and de-trapping in shallow-and deep-traps in SWCNT-TFTs were analyzed by investigating charge transport based on their trapping/de-trapping rate.The comprehensive analysis of this study provides fundamental insights into the 2D charge transport mechanism in TFTs based on random networks of nanomaterial channels.展开更多
基金the National Key Research and Development Program of China(2016YFA0203900,2018YFB2202500)Innovation Program of Shanghai Municipal Education Commission(2021-01-07-00-07-E00077)+3 种基金Shanghai Municipal Science and Technology Commission(18JC1410300,21DZ1100900)Research Grant Council of Hong Kong(15205619)the National Natural Science Foundation of China(61925402,61934008,and 6210030233)the Natural Science Foundation of Shanghai(21ZR1405700)。
文摘Recently,research on two-dimensional(2D)semiconductors has begun to translate from the fundamen-tal investigation into rudimentary functional circuits.In this work,we unveil the first functional MoS2 artificial neural network(ANN)chip,including multiply-and-accumulate(MAC),memory and activation function circuits.Such MoS2 ANN chip is realized through fabricating 818 field-effect transistors(FETs)on a wafer-scale and high-homogeneity MoS2 film,with a gate-last process to realize top gate structured FETs.A 62-level simulation program with integrated circuit emphasis(SPICE)model is utilized to design and optimize our analog ANN circuits.To demonstrate a practical application,a tactile digit sensing recognition was demonstrated based on our ANN circuits.After training,the digit recognition rate exceeds 97%.Our work not only demonstrates the protentional of 2D semiconductors in wafer-scale inte-grated circuits,but also paves the way for its future application in AI computation.
基金The authors are grateful for the financial support provided by the National Key Laboratory of Science and Technology on Micro/Nano Fabrication of China,the National Natural Science Foundation of China (No.21901157)the SJTU Global Strategic Partnership Fund (2020 SJTU-HUJI)the National Key R&D Program of China (2021YFC2100100).
文摘Two-dimensional materials with active sites are expected to replace platinum as large-scale hydrogen production catalysts.However,the rapid discovery of excellent two-dimensional hydrogen evolution reaction catalysts is seriously hindered due to the long experiment cycle and the huge cost of high-throughput calculations of adsorption energies.Considering that the traditional regression models cannot consider all the potential sites on the surface of catalysts,we use a deep learning method with crystal graph convolutional neural networks to accelerate the discovery of high-performance two-dimensional hydrogen evolution reaction catalysts from two-dimensional materials database,with the prediction accuracy as high as 95.2%.The proposed method considers all active sites,screens out 38 high performance catalysts from 6,531 two-dimensional materials,predicts their adsorption energies at different active sites,and determines the potential strongest adsorption sites.The prediction accuracy of the two-dimensional hydrogen evolution reaction catalysts screening strategy proposed in this work is at the density-functional-theory level,but the prediction speed is 10.19 years ahead of the high-throughput screening,demonstrating the capability of crystal graph convolutional neural networks-deep learning method for efficiently discovering high-performance new structures over a wide catalytic materials space.
基金supported by the National Research Foundation of Korea grant funded by the Korea government(MSIT)(NRF-2021R1A2C2012855).
文摘Understanding charge transport mechanisms in thin-film transistors based on random networks of single-wall carbon nanotubes(SWCNT-TFTs)is essential for further advances to improve the potential for various nanoelectronic applications.Herein,a comprehensive investigation of the two-dimensional(2D)charge transport mechanism in SWCNT-TFTs is reported by analyzing the temperature-dependent electrical characteristics determined from the direct-current and non-quasi-static transient measurements at 80-300 K.To elucidate the time-domain charge transport characteristics of the random networks in the SWCNTs,an empirical equation was derived from a theoretical trapping model,and a carrier velocity distribution was determined from the differentiation of the transient response.Furthermore,charge trapping and de-trapping in shallow-and deep-traps in SWCNT-TFTs were analyzed by investigating charge transport based on their trapping/de-trapping rate.The comprehensive analysis of this study provides fundamental insights into the 2D charge transport mechanism in TFTs based on random networks of nanomaterial channels.
