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具有稳定阈值开关特性的全印刷IGZO忆阻器阵列用于人工伤害感受器 被引量:1
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作者 彭文鸿 刘常飞 +5 位作者 许晨辉 覃琮尧 秦宁浦 陈惠鹏 郭太良 胡文平 《Science China Materials》 SCIE EI CAS CSCD 2024年第8期2661-2670,共10页
人工感知系统的大规模制备与图案化对实现仿生系统至关重要.传统工艺的图案化受限于掩膜版,难以大规模制备.喷墨打印技术的无掩膜图案化制备能力十分适配目前大规模制备的需求.然而,在器件的制造上,打印技术往往局限于制造简单的器件或... 人工感知系统的大规模制备与图案化对实现仿生系统至关重要.传统工艺的图案化受限于掩膜版,难以大规模制备.喷墨打印技术的无掩膜图案化制备能力十分适配目前大规模制备的需求.然而,在器件的制造上,打印技术往往局限于制造简单的器件或在复杂器件中制备图案化的有源层.在此,我们实现全喷墨打印IGZO忆阻器阵列用于模拟伤害感受器.通过在全金属氧化物的ITO/IGZO/ITO忆阻器中加入一层Ag,实现了稳定的阈值切换特性,大的电流开关比(10~5),10~4次循环扫描的出色开关耐久性和良好的空间均匀性.我们分析了器件基于Ag导电丝的传导方式和LIF特性,如积累爆发、自泄露等,证明了忆阻器作为人工神经元的潜力.最后,我们利用该器件稳定的阈值切换特性,成功实现了人工伤害感受器,包括阈值、松弛、无适应和敏化.我们的工作证明了喷墨打印技术在实现仿生系统中拥有着巨大的应用潜力. 展开更多
关键词 喷墨打印技术 有源层 忆阻器 人工神经元 图案化 金属氧化物 循环扫描 感知系统
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Memristor-based in-situ convolutional strategy for accurate braille recognition
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作者 Xianghong Zhang congyao qin +6 位作者 Wenhong Peng Ningpu qin Enping Cheng Jianxin Wu Yuyang Fan Qian Yang Huipeng Chen 《Science China Materials》 SCIE EI CAS CSCD 2024年第12期3986-3993,共8页
Signal processing has entered the era of big data,and improving processing efficiency becomes crucial.Traditional computing architectures face computational efficiency limitations due to the separation of storage and ... Signal processing has entered the era of big data,and improving processing efficiency becomes crucial.Traditional computing architectures face computational efficiency limitations due to the separation of storage and computation.Array circuits based on multi-conductor devices enable full hardware convolutional neural networks(CNNs),which hold great potential to improve computational efficiency.However,when processing large-scale convolutional computations,there is still a significant amount of device redundancy,resulting in low computational power consumption and high computational costs.Here,we innovatively propose a memristor-based in-situ convolutional strategy,which uses the dynamic changes in the conductive wire,doping area,and polarization area of memristors as the process of convolutional operations,and uses the time required for conductance switching of a single device as the computation result,embodying convolutional computation through the unique spiked digital signal of the memristor.Our strategy reasonably encodes complex analog signals into simple digital signals through a memristor,completing the convolutional computation at the device level,which is essential for complex signal processing and computational efficiency improvement.Based on the implementation of device-level convolutional computing,we have achieved feature recognition and noise filtering for braille signals.We believe that our successful implementation of convolutional computing at the device level will promote the construction of complex CNNs with large-scale convolutional computing capabilities,bringing innovation and development to the field of neuromorphic computing. 展开更多
关键词 convolutional computing multi-conductor MEMRISTOR conductive filaments
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