This study presents dual-mode memory transistor that accommodates memory and synaptic operations utilizing photoinduced charge trapping at the interface between poly(1,4-butanediol diacrylate)(pBDDA)and Parylene diele...This study presents dual-mode memory transistor that accommodates memory and synaptic operations utilizing photoinduced charge trapping at the interface between poly(1,4-butanediol diacrylate)(pBDDA)and Parylene dielectric layer.Memory characteristics were implemented based on the photoresponsivity of dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene(DNTT),enabling instantaneous electron storage under combined optical and electrical inputs,with retention times up to 10,000 s.Meanwhile,synaptic characteristics were induced by gradual charge trapping via optical pulse stimulation.Synaptic plasticity was confirmed via the potentiation-depression curve,emulating key features of biological nervous system,namely short-term memory(STM)and long-term memory(LTM).Furthermore,the fingerprint recognition tasks highlighted identification and authentication abilities by incorporating our synaptic function into an artificial neural network(ANN).The dual-mode memory transistor,fabricated on a business card,showed excellent compatibility with flexible optoelectronics,maintaining stable memory and synaptic performance over 500 bending cycles with minimal changes in memory window,memory ratio,and potentiation-depression behavior.展开更多
With the advancement of deep learning and neural networks,the computational demands for applications in wearable devices have grown exponentially.However,wearable devices also have strict requirements for long battery...With the advancement of deep learning and neural networks,the computational demands for applications in wearable devices have grown exponentially.However,wearable devices also have strict requirements for long battery life,low power consumption,and compact size.In this work,we propose a scalable optoelectronic computing system based on an integrated optical convolution acceleration core.This system enables high-precision computation at the speed of light,achieving 7-bit accuracy while maintaining extremely low power consumption.It also demonstrates peak throughput of 3.2 TOPS(tera operations per second)in parallel processing.We have successfully demonstrated image convolution and the typical application of an interactive first-person perspective gesture recognition application based on depth information.The system achieves a comparable recognition accuracy to traditional electronic computation in all blind tests.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIT)(RS-2023-00210194,RS-2024-00438999,RS-2024-00442020,RS-2024-00454508)supported by Institute of Information&communications Technology Planning&Evaluation(IITP)under the artificial intelligence semiconductor support program to nurture the best talents(IITP-(2025)-RS-2023-00253914)grant funded by the Korea government(MSIT)and the research fund of Hanyang University(HY-2024-2696).
文摘This study presents dual-mode memory transistor that accommodates memory and synaptic operations utilizing photoinduced charge trapping at the interface between poly(1,4-butanediol diacrylate)(pBDDA)and Parylene dielectric layer.Memory characteristics were implemented based on the photoresponsivity of dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene(DNTT),enabling instantaneous electron storage under combined optical and electrical inputs,with retention times up to 10,000 s.Meanwhile,synaptic characteristics were induced by gradual charge trapping via optical pulse stimulation.Synaptic plasticity was confirmed via the potentiation-depression curve,emulating key features of biological nervous system,namely short-term memory(STM)and long-term memory(LTM).Furthermore,the fingerprint recognition tasks highlighted identification and authentication abilities by incorporating our synaptic function into an artificial neural network(ANN).The dual-mode memory transistor,fabricated on a business card,showed excellent compatibility with flexible optoelectronics,maintaining stable memory and synaptic performance over 500 bending cycles with minimal changes in memory window,memory ratio,and potentiation-depression behavior.
基金supported by the National Natural Science Foundation of China (U21A20511)the Innovation Project of Optics Valley Laboratory (OVL2021BG001).
文摘With the advancement of deep learning and neural networks,the computational demands for applications in wearable devices have grown exponentially.However,wearable devices also have strict requirements for long battery life,low power consumption,and compact size.In this work,we propose a scalable optoelectronic computing system based on an integrated optical convolution acceleration core.This system enables high-precision computation at the speed of light,achieving 7-bit accuracy while maintaining extremely low power consumption.It also demonstrates peak throughput of 3.2 TOPS(tera operations per second)in parallel processing.We have successfully demonstrated image convolution and the typical application of an interactive first-person perspective gesture recognition application based on depth information.The system achieves a comparable recognition accuracy to traditional electronic computation in all blind tests.
