Characterization and Real Time Defect Mitigation in Chemical/Mechanical Polishing of Microelectronic Wafers Using Decision Theory and MultiSensor Fusion

使用决策理论和多传感器融合对微电子晶圆化学/机械抛光进行表征和实时缺陷缓解

• 批准号: 1000978
• 负责人: Satish Bukkapatnam
• 金额: $ 40万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1000978&HistoricalAwards=false
• 关键词: Characterization; Real; Time; Defect; Mitigation

The objective of this research is to apply the principles of multi-sensor fusion and decision theory for deriving quantitative relationships connecting signal features from various sensors, including, force, temperature, vibration, and others, gathered during chemical mechanical planarization of microelectronics wafers, with specific defect evolutions, towards real-time surface defect mitigation and process control. The approach will be to first induce certain basic defect patterns on wafers, such as indentation, scratching using nanoindentation and nanoscratching techniques. These wafers then will be polished on a chemical mechanical planarization machine instrumented with multiple wireless microelectromechanical systems sensors. It is anticipated that an archive of defect-sensitive sensor features for different defects generated in chemical mechanical planarization will be developed. Decision theory will be used to predict and control the defects. The control action (for example, specific adjustment of down force and platen speed) at each time-epoch will be estimated by maximizing a utility function, so that the control actions are robust to noise. The chemical mechanical planarization platform will be equipped with multiple wired- and wireless-microelectromechanical systems-based sensors, where possible, to monitor temperature, vibrations, chemistry (pH), and forces for defect detection and mitigation.If successful, this research will facilitate industry adoption of micro-electromechanical systems sensor-based approaches to address defects and such critical impediments to wafer yield. The sensor-networked chemical mechanical planarization platform can be used as a test bed for instruction and training of students and industry personnel. The Principal Investigators have a track record for attracting students from diverse backgrounds, and plan to work with local minority institutions to recruit qualified students into this research project. The students will be exposed to fundamental multi-disciplinary research and industry practices in advanced manufacturing processes through mutual visits between university and industry. The highlights of the research will be made available on the website of the Principal Investigators and the results will be published in various journals, trade magazines, and presentations at national and international conferences.

本研究的目的是应用多传感器融合和决策理论的原则，从不同的传感器，包括力，温度，振动，和其他的，收集在微电子晶片的化学机械平坦化过程中，与特定的缺陷演变，对实时表面缺陷缓解和过程控制的信号特征，推导出定量关系。该方法将首先在晶片上诱导某些基本缺陷图案，例如压痕、使用纳米压痕和纳米划痕技术的划痕。然后，这些晶片将在装有多个无线微机电系统传感器的化学机械平面化机器上抛光。预计将开发用于化学机械平坦化中产生的不同缺陷的缺陷敏感传感器特征的档案。运用决策理论对缺陷进行预测和控制。将通过最大化效用函数来估计每个时间段的控制动作（例如，下压力和压板速度的特定调节），使得控制动作对噪声是鲁棒的。化学机械平面化平台将配备多个有线和无线微机电系统为基础的传感器，在可能的情况下，监测温度，振动，化学（pH值），和力的缺陷检测和mitigation.If成功，这项研究将促进工业采用微机电系统传感器为基础的方法来解决缺陷和晶圆产量等关键障碍。传感器网络化学机械平面化平台可用作学生和行业人员教学和培训的测试台。首席研究员在吸引来自不同背景的学生方面有着良好的记录，并计划与当地少数民族机构合作，招募合格的学生参加本研究项目。学生将通过大学和工业之间的互访接触到先进制造过程中的基础多学科研究和行业实践。 研究的重点将在主要研究者的网站上公布，结果将发表在各种期刊，行业杂志以及国家和国际会议上的演讲中。