Brain‐inspired (neuromorphic) computing that offers lower energy consumption and parallelism (simultaneous processing and memorizing) compared to von Neumann computing provides excellent opportunities in many computational tasks ranging from image recognition to speech processing. To accomplish neuromorphic computing, highly efficient optoelectronic synapses, which can be the building blocks of optoelectronic neuromorphic computers, are necessary. Currently, carbon nanotubes (CNTs), an attractive candidate to develop circuit‐level photonic synapses, provide weak light responses. The inferior photoresponse of CNTs increases the energy consumption of neuromorphic optoelectronic devices. Herein, a method to grow organic–inorganic halide perovskite quantum dots (PQDs) directly on multiwall CNTs (MWCNTs) to increase the photosensitivity of optoelectronic synapses is demonstrated. The new hybrid material synchronizes the high photoresponse of PQDs and the excellent electrical properties of MWCNTs to provide photonic memory under very low light intensity (125 µW cm⁻²). However, neat MWCNTs do not show any detectable light response at the tested light intensity, as high as 25 mW cm⁻². Since the PQDs are grown directly on and in the MWCNTs, the hybrid PQD‐MWCNT provides a new direction for the future MWCNT‐based optoelectronic devices for neuromorphic computing with a potential to break the von Neumann bottleneck.

中文翻译：

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碳纳米管上钙钛矿量子点的增长，用于神经形态光电计算

与冯·诺依曼（von Neumann）计算相比，脑启发式（神经形态）计算具有更低的能耗和并行度（同时处理和存储），为从图像识别到语音处理的许多计算任务提供了绝佳的机会。要完成神经形态计算，高效的光电突触（可能是光电神经形态计算机的基础）是必要的。目前，碳纳米管（CNTs）是开发电路级光子突触的有吸引力的候选者，其光响应较弱。碳纳米管的较差的光响应会增加神经形态光电器件的能耗。在这里 证明了一种在多壁碳纳米管（MWCNT）上直接生长有机-无机卤化物钙钛矿量子点（PQD）以增加光电突触的光敏性的方法。新型混合材料可同步PQD的高光响应和MWCNT的出色电性能，从而在非常低的光强度（125 µW cm）下提供光子存储^-2）。然而，纯净的MWCNT在高达25mW cm ^-2的测试光强度下没有显示任何可检测到的光响应。由于PQD直接生长在MWCNT之上和之中，因此，混合PQD-MWCNT为未来基于MWCNT的神经形态计算光电器件提供了新的方向，有可能突破冯·诺依曼瓶颈。