铜线表面钯层特征是影响芯片封装过程中烧球和键合质量的重要因素。采用无卤直接涂镀工艺,制备了不同涂镀速度和涂镀温度下的镀钯铜线,研究了涂镀速度对镀层形貌和无空气焊球(free air ball,FAB)特征的影响。结果表明:随着涂镀速度增加...铜线表面钯层特征是影响芯片封装过程中烧球和键合质量的重要因素。采用无卤直接涂镀工艺,制备了不同涂镀速度和涂镀温度下的镀钯铜线,研究了涂镀速度对镀层形貌和无空气焊球(free air ball,FAB)特征的影响。结果表明:随着涂镀速度增加,涂镀时间减少,钯颗粒在铜线表面分布的均匀性变差,局部分布不均匀的钯颗粒团聚引起钯颗粒浓度较高,镀层表面钯颗粒团聚区域增加。在涂镀速度50 m/min下,镀层表面钯分布较为均匀。随着涂镀速度的增加,FAB球直径逐渐减小;镀钯铜线表面Pd颗粒团聚区域和未团聚区域的钯含量差增大,FAB球尺寸的一致性逐渐下降。在较低涂镀速度50 m/min下,FAB球表面钯分布比较均匀;在较高的涂镀速度100 m/min下,镀层表面大量团聚的钯颗粒重熔后在FAB球表面呈大面积连续的富钯区,钯再分布的均匀性较差。从FAB球尺寸的一致性和表面钯再分布的均匀性方面考虑,涂镀速度50 m/min和涂镀温度400℃为镀钯铜线涂镀较佳的工艺参数。展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
