Cost-Effective Reliability Screening, Binning, and In-Field Adaptation

在半导体工业中，衡量可靠性的一种方法是产品的预期寿命。随着半导体制造技术的不断进步，确保可靠性已成为该行业的主要挑战之一。确保可靠性的能力对于产品线的成功以及行业的成功至关重要。该项目建议在产品发货给客户之前开发一种首先具有成本效益的解决方案来筛选不可靠的部件。该项目建议开发预测零件预期寿命的软件工具和方法。此外，该项目建议开发第二种具有成本效益的解决方案，用于在现场使用部件时持续监测和提高可靠性。将开发更多的软件工具和方法，以建立这种持续监测的模型。然后，这些监测模型用于根据零件当前的健康状况自动调整零件的速度和电压等操作参数，从而延长零件的使用寿命。该研究与教育活动相结合，以开发具有广泛影响的课程和教程材料，为教育提供最先进的实验室，以及吸引本科生和代表性不足的学生的研究计划。该研究致力于实现对最先进工业实践的全面理解，并完成多学科研究，将可靠性物理，半导体测试和数据挖掘的知识结合起来。通过这项研究发现的知识将为行业提供如何应对当前和未来可靠性挑战的明确方向。

In the semiconductor industry, one way of measuring reliability is the expected lifetime of a product. As semiconductor manufacturing technologies continue to advance, ensuring reliability has become one of the major challenges in the industry. The ability to ensure reliability is essential for the success of a product line as well as the success of the industry. This project proposes to develop a first cost-effective solution for screening unreliable parts before product shipment to the customer. The project proposes to develop software tools and methodologies for predicting the expected life time of a part. Further, the project proposes to develop a second cost-effective solution for continuously monitoring and improving reliability while a part is being used in the field. Additional software tools and methodologies will be developed for building models for such continuous monitoring. These monitoring models are then used for extending the life time of a part by automatic adjusting the operational parameters such as speed and voltage of the part based on its current health condition. The research is integrated with educational activities to develop course and tutorial materials for broad impact, a state-of-the-art laboratory for education, and a research program to attract undergraduate and underrepresented students. The research strives to achieve a comprehensive understanding of state-of-the-art industrial practices and to accomplish multidisciplinary studies merging knowledge from reliability physics, semiconductor testing, and data mining. Knowledge discovered through this research will provide the industry with a clear direction on how to cope with the present and future reliability challenges.