鈣鈦礦材料因其優異的性質，例如高吸收係數、可調控的能隙以及優異的載子遷移率，而在光電應用領域（如發光二極體、光電探測器、雷射與太陽能電池）中備受矚目。其中，錫基鈣鈦礦因具備環保特性，已成為取代含鉛鈣鈦礦的有力候選材料。在本研究中，我們設計並製作了一種基於 ITO/ZnO/CsSnBr₃/NiO/Ag 異質結構的錫基鈣鈦礦突觸元件。此全無機 CsSnBr₃ 薄膜是透過結合三區爐的化學氣相沉積法（CVD）所製備。我們系統性地研究了這些錫基鈣鈦礦薄膜的形貌、結晶結構及光學特性，並進一步探討 ITO/ZnO/CsSnBr₃/NiO/Ag 結構於模擬神經突觸行為中的應用表現。結果顯示，CsSnBr₃ 薄膜對不同波長光具有良好吸收能力，並能在光刺激下展現出短期與長期可塑性（STP 與 LTP），模擬出類似生物神經突觸的記憶與學習行為。此外，CsSnBr₃ 薄膜展現出高結晶品質、缺陷與晶界較少以及緊密的晶格排列，有助於提升其操作穩定性與重複性。此外，實驗結果亦顯示元件對相同波段光源具有多階阻態的 RRAM 特性，可作為類神經元的集成元件，實現視覺感知與記憶模擬。透過 MNIST 數字資料庫進行訓練與測試，可對不同顏色光源下的三類元件分別賦予權重並進行權重更新，進而展現於圖像辨識任務中具有明顯優勢的識別結果。

Perovskite materials have attracted significant attention in optoelectronic applications—such as light-emitting diodes, photodetectors, lasers, and solar cells—due to their exceptional properties, including high absorption coefficients, tunable bandgaps, and excellent carrier mobility. Among them, tin-based perovskites have emerged as promising candidates to replace lead-based perovskites owing to their environmental friendliness. In this study, we designed and fabricated a tin-based perovskite synaptic device with an ITO/ZnO/CsSnBr₃/NiO/Ag heterostructure. The all-inorganic CsSnBr₃ thin films were synthesized using a three-zone tube furnace via chemical vapor deposition (CVD). We systematically investigated the morphology, crystal structure, and optical properties of the CsSnBr₃ thin films, and further evaluated the synaptic behavior of the ITO/ZnO/CsSnBr₃/NiO/Ag configuration in neuromorphic applications. The results demonstrated that the CsSnBr₃ films exhibited strong light absorption across different wavelengths and showed both short-term and long-term plasticity (STP and LTP) under optical stimulation, effectively mimicking biological synaptic memory and learning behaviors. Moreover, the CsSnBr₃ films displayed high crystallinity with minimal defects and grain boundaries, contributing to enhanced operational stability and repeatability. Additionally, the device showed multilevel resistive switching characteristics under illumination of the same wavelength, enabling it to function as an integrated neuromorphic component for visual perception and memory simulation. By training and testing with the MNIST dataset, the devices under three different color lights were individually assigned weights, which were subsequently updated, achieving superior recognition performance in image classification tasks.

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