隨著軟性電子應用對聚合物基板在低溫下機械強度與可靠性要求日益苛刻，現有低溫銲料常難以在高硬度、高強度與足夠延展性間取得平衡，且合金設計仍仰賴耗時的試錯實驗。Sn–In 合金因其低熔點特性備受矚目，卻普遍存在強度不足的問題。為解決此一瓶頸，本研究首先建立 Sn–In 二元合金的維氏硬度資料庫，並訓練高斯核脊回歸（Gaussian Kernel Ridge Regression）模型進行性能預測，接著應用基因演算法執行逆向設計，以快速篩選出 Sn–In–X（SIX）組成。所篩選出的SIX5合金經由 SEM–EDS、XRD、CALPHAD與 DSC 驗證；SEM–EDS 與 XRD 結果顯示 SIX5 由 β-In₃Sn、γ-InSn₄、Cu₆(In, Sn)₅ 及 BiIn₂ 四相構成，DSC 曲線於 110.25、107.75、90.83 與 57.40 °C 處出現四個吸熱峰，分別對應 γ-InSn₄、Cu₆(In, Sn)₅、β-In₃Sn 及 BiIn₂ 相的依序析出。性能測試證實，SIX5 的硬度達 21.28 HV（約為共晶 Sn–52In 之三倍），拉伸強度達 19.82 MPa（約為 Sn–52In 之兩倍）。此資料驅動設計流程可大幅減少實驗試錯並加速低溫銲料在先進封裝與軟性電子領域的優化。

As flexible electronics applications impose increasingly stringent requirements on the mechanical strength and reliability of polymer substrates at low temperatures, conventional low-temperature solders often struggle to balance high hardness, high strength, and sufficient ductility, and their alloy design still relies on time-consuming trial-and-error. To overcome this bottleneck, we first established a Vickers hardness database for binary Sn–In alloys and trained a Gaussian Kernel Ridge Regression model for property prediction, then applied a genetic algorithm for inverse design to rapidly screen Sn–In–X (SIX) compositions. The selected SIX5 alloys were validated by SEM–EDS, XRD, CALPHAD, and DSC; SEM–EDS and XRD results showed that SIX5 comprises four phases (β-In₃Sn, γ-InSn₄, Cu₆(In, Sn)₅, and BiIn₂), and DSC curves exhibited four endothermic peaks at 110.25, 107.75, 90.83, and 57.40 °C, corresponding sequentially to the precipitation of γ-InSn₄, Cu₆(In, Sn)₅, β-In₃Sn, and BiIn₂. Performance tests confirmed that SIX5 reaches a hardness of 21.28 HV—approximately three times that of eutectic Sn–52In—and a tensile strength of 19.82 MPa—approximately twice that of Sn–52In. This data-driven design workflow can significantly reduce experimental trial-and-error and accelerate the optimization of low-temperature solders for advanced packaging and flexible electronics.

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