Jimmy Iskandar

Chamber matching across multiple dimensions utilizing Predictive Maintenance, Equipment Health Monitoring, Virtual Metrology and Run-To-Run control

Matching tools running identical processes is particularly critical for users migrating to more advanced nodes. Sustaining a fleet of tools to a matched state can reduce yield losses and yield variability, allow for greater routing flexibility in the fab, identify and control process inefficiencies, and reduce time for root cause analysis of yield issues. The matching process is multi-dimensional, covering hardware, software, tool sensors, process, metrology, maintenance and end of line electrical test and yield. The current state-of-the-art of chamber matching during production is chamber variance detection and reporting. A solution that provides a true active matching capability has been developed as part of a multi-dimensional chamber matching approach. It leverages a number of Advanced Process Control (APC) techniques collectively. Specifically Equipment Health Monitoring (EHM) is used for health monitoring during processing and for fingerprinting during maintenance recovery, Predictive Maintenance (PdM) is used to predict a consistent downtime state, and Virtual Metrology (VM) along with Run-to-Run (R2R) control is used to expedite maintenance recovery.

Predictive Maintenance in semiconductor manufacturing

Over the past two years the Predictive Maintenance (PdM) capability in semiconductor manufacturing has migrated from Proof-of-Concept (PoC) and univariate Fault Detection (FD) extrapolation mechanisms to fab-wide solutions that are (1) robust to typical process and equipment disturbances, (2) extensible so as to provide solution approaches that are portable across instances of a tool type and across tool types, and (3) maintainable so as to provide solutions that are useful for long periods of time. A number of advancements have facilitated this advancement including solutions for porting modeling components across process and equipment types, mechanisms for incorporating process and equipment knowledge into models, mechanisms for determining model context (e.g., recipe) dependency, methods for model optimization to fab financials, and methods for rejecting run-time disturbances in PdM modeling. As a result of these and other innovations, the landscape of PdM in semiconductor manufacturing has rapidly advanced to the point that, from a technical perspective, solutions are now available for fab-wide PdM realization.

Maintenance of virtual metrology models

Virtual metrology (VM) predicts on-wafer properties, such as thickness and uniformity, using equipment data (e.g., sensors, constants) and potentially on-wafer properties or predictions from previous steps. While much literature is devoted to developing VM models, maintaining them (e.g., so they can be used for months in production) presents a different set of challenges. Behavior of equipment can change over time, causing degradation of VM models. Maintenance events can abruptly reset sensors and process conditions, invalidating VM models. In addition, sensor noise, sensor imperfection and aging parts can disturb performance of VM models. While some of these issues can be addressed during modeling by incorporating certain variables that capture the dynamics associated with these issues, others are harder to foresee. Resolving these issues may require tuning the models as they age or manually rebuilding the models after they become ineffective. To mitigate these problems, techniques including monitoring residual error and tool degradation, compensating for sensor reset and residual error, and auto-retraining models can significantly help VM models maintain their performance during deployment.

Deploying an Equipment Health monitoring dashboard and assessing predictive maintenance

Predictive maintenance (PdM) is cited by the ITRS as a critical technology to incorporate into production over the next five years to reduce unscheduled downtime and cycle time, maintain high quality, and reduce cost. Equipment Health monitoring (EHM) is a companion to PdM that provides a tracking indication of equipment health. The industry needs to deploy and assess PdM and EHM capabilities to determine best practices for the industry and the potential for cost reduction through deployment of these technologies. Applied Materials is working with both Micron Technology and Intel Corporation on EHM and PdM development and assessment projects, partially funded by ISMI. As a result of these projects a portable EHM solution has been designed and demonstrated that can be deployed “out-of-the-box” to track equipment health, but also updated as more information is ascertained on specific smart health indicators. Also, preliminary PdM results in both projects reveals an ability to predict key downtime event including particle monitor, throttle valve and liquid flow failures. Results were achieved on both CVD and etch tool types.