Developing devices with a wide-temperature range persistent photoconductivity (PPC) and ultra-low power consumption remains a significant challenge for optical synaptic devices used in neuromorphic computing. By harnessing the PPC properties in materials, it can achieve optical storage and neuromorphic computing, surpassing the von Neuman architecture-based systems. However, previous research implemented PPC required additional gate voltages and low temperatures, which need additional energy consumption and PPC cannot be achieved across a wide temperature range. Here, we fabricated a simple heterojunctions using zinc(II)-meso-tetraphenyl porphyrin (ZnTPP) and single-walled carbon nanotubes (SWCNTs). By leveraging the strong binding energy at the heterojunction interface and the unique band structure, the heterojunction achieved PPC over an exceptionally wide temperature range (77 K-400 K). Remarkably, it demonstrated nonvolatile storage for up to 2×10⁴s, without additional gate voltage. The minimum energy consumption for each synaptic event is as low as 6.5 aJ. Furthermore, we successfully demonstrate the feasibility to manufacture a flexible wafer-scale array utilizing this heterojunction. We applied it to autonomous driving under extreme temperatures and achieved as a high impressive accuracy rate as 94.5%. This tunable and stable wide-temperature PPC capability holds promise for ultra-low-power neuromorphic computing.

开发具有宽温度范围持久光电导（PPC）和超低功耗的设备仍然是用于神经形态计算的光学突触设备的重大挑战。通过利用材料中的PPC特性，它可以实现光存储和神经形态计算，超越基于冯诺依曼架构的系统。然而，之前研究实现的PPC需要额外的栅极电压和较低的温度，这需要额外的能耗，并且无法在较宽的温度范围内实现PPC。在这里，我们使用锌（II）-内消旋四苯基卟啉（ZnTPP）和单壁碳纳米管（SWCNT）制造了一个简单的异质结。通过利用异质结界面处的强结合能和独特的能带结构，异质结在极宽的温度范围（77 K-400 K）内实现了PPC。值得注意的是，它展示了长达 2×10 ⁴ s 的非易失性存储，无需额外的栅极电压。每个突触事件的最低能量消耗低至 6.5 aJ。此外，我们成功证明了利用这种异质结制造柔性晶圆级阵列的可行性。我们将其应用于极端温度下的自动驾驶，并取得了令人印象深刻的高达 94.5% 的准确率。这种可调且稳定的宽温 PPC 功能为超低功耗神经形态计算带来了希望。