Semiconductor Materials Analysis and Fabrication Process Control
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This information is provided to communicate a high level overview of APC, with classic examples to communicate potential and the infrastructure to nurture APC. APC system refers to the use of advanced statistical and analytical techniques to manipulate process control parameters and inputs on process tools to improve output quality. It is algorithm-based, and indicates the adjustments necessary from one lot to the next to reduce systematic variation. APC system adjustments focus on 1 Feedforward: Data from preceding operations is utilized to adjust processing at subsequent operations.
In a generic APC system, 1 an inspection tool measures predefined inspection points on a processed wafer and feeds the results a database. The APC engine is the workhorse of the system, analyzing output parameters, and recommending input parameter changes to the recipe, when it detects actionable signals from the inspection results. Also, there is a database associated with the system to store the large amount of data generated by the inspection tools, and for the storage of data models, versioning, etc. Today, an APC framework is essential for rapid implementation and scaling ability to install many APC applications across multiple tools in the factory.
A framework allows for a common data format, scripting language, standardized helpful algorithms, and a database that is accessible to factory-wide controllers and tools. Some companies choose to develop a custom APC framework. But, there are many APC framework solutions available in the market these days.
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Frameworks have assisted in the proliferation of APC systems, coupled with rapid application development and implementation with lower costs and faster realization of an expected benefit, which is to maximize revenue from every wafer processed. EDA is very important in the Photolithography and Plasma Etch modules that traditionally generate the most volume of data needed for APC system implementations.
Terminology: Like other disciplines Process Control has its own terminology, and the entire scope of advanced process control in manufacturing usually involves all the aspects below. Run-to-Run Control R2R describes the scenario, where in-between processing of two lots or two wafers of the same lot, an advanced process control system can implement changes in recipe parameters.
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The system makes continuous adjustments if necessary at the end of each processing and measurement cycle, before repeating the next cycle, and thereby minimizing drift. Fault Detection and Classification FDC describes the scenario where the factory monitors data on the manufacturing tool, while simultaneously using analytical techniques to detect tool faults. And once a fault is detected, then it is classified to determine an assignable cause. This cause is then corrected before the tool is released for further processing. Fault detection and classification has another branch that is called Fault Prediction and this involves using the same data to identify parameters that can be monitored to flag the need for tool maintenance activity ahead of when the fault manifests itself.
Models: Tool processing is described in a model that is specific to its activity. The model describes all the controllable input parameters and measurable output parameters that are statistically linked in mathematical terms that reflects a desired run. Hence, when specific measured output parameter deviates from the desired values, the related input parameters are changed to bring the process back under control.
Advanced process control relies on mathematics, statistics, and machine learning. Commonality analysis, multivariate analysis, linear programming, time-series, and machine learning, are used extensively in advanced process control.
Leveraging APC Technology: This requires the manufacturing facility to implement an infrastructure that encourages the generation of APC project ideas, and facilitates evaluation and final implementation. This will ensure that feasibility is ascertained, and RoI is scrutinized before starting implementation.
APC Health Monitoring: In a dynamic manufacturing environment, tool upgrades and changes are constantly done both physically and at a software level. Just as SPC health monitoring is done regularly, APC health monitoring is also required by the identification and monitoring of appropriate parameters.
The computer program product may include code that performs the step of storing a time of attaining the estimated temperature. The semiconductor manufacturing system may include a plurality of process modules that provide data for storage in the data structure. The semiconductor manufacturing system may include a robotic semiconductor wafer handler that provides data for storage in the data structure.
The computer program product may include code that performs the step of associating a time with the data in one of the one or more fields of the data structure. The computer program product may include code that performs the step of storing an attribute of the data in one of the one or more fields of the data structure. The computer program product may include code that performs the step of retrieving data from at least one of the one or more fields of the data structure and using the retrieved data to control processing of the workpiece.
In one aspect, a system disclosed herein includes a semiconductor manufacturing system; at least one program to control operation of the semiconductor manufacturing system to process a plurality of wafers, and to receive data from the semiconductor manufacturing system relating to one of the plurality of wafers; a database that maintains a data structure for each one of the plurality of wafers and stores the received data in the data structure corresponding to the related one of the plurality of wafers.
The at least one program may include software to optimize throughput of the semiconductor manufacturing system according to wafer-specific data in the database. In one aspect, a computer readable medium disclosed herein has stored thereon a data structure, the data structure may include: a first field uniquely identifying a wafer; a second field containing a measured value for the wafer during a fabrication process; a third field containing a calculated value for the wafer; and a forth field containing information about at least one process step to which the wafer has been exposed.
The data structure may include a fifth field containing at least one prospective processing step for the wafer. In one aspect, a system disclosed herein includes a semiconductor handling system including at least one robot and a plurality of process chambers; a software controller that controls operation of the handling system to process one or more workpieces; and an electronic interface that may include a shared medium that couples the at least one robot, the plurality of process chambers, and the controller in a communicating relationship.
The shared medium may include a daisy chain in which the at least one robot, the plurality of process chambers, and the controller share at least one wire. The shared medium may include a wireless network. The operation of the handling system may include controlling one or more slot valves. The operation of the handling system may include moving the one or more workpieces among the process chambers with the at least one robot.
The operation of the handling system may include receiving sensor data from at least one of the plurality of process chambers. In one aspect, a system disclosed herein includes a semiconductor handling system that may include at least one robot and a plurality of process chambers; at least one workpiece within the semiconductor handling system; a database that stores data for the at least one workpiece indexed according to a unique identifier for the at least one workpiece; a controller that controls operation of the semiconductor handling system, the controller may employ a neural network and a finite state machine to schedule handling of the at least one workpiece; and a graphical user interface that may display a real time three-dimensional view of the semiconductor handling system and the at least one workpiece.
The foregoing and other objects and advantages of the invention will be appreciated more fully from the following further description thereof, with reference to the accompanying drawings wherein:. The systems and methods described herein relate to software for operating a semiconductor manufacturing system. The system for processing a wafer may include a plurality of valves , a plurality of process tools , handling hardware , control software , and a load lock In general operation, the system operates to receive a wafer through the load lock , to move the wafer among the process tools with the handling hardware so that the wafer may be processed, and to remove the processed wafer through the load lock The wafer may be any wafer or other workpiece processed by the system It will be understood that, while the following description is applicable to wafers, and refers specifically to wafers in a number of illustrative embodiments, a variety of other objects may be handled within a semiconductor facility including a production wafer, a test wafer, a cleaning wafer, a calibration wafer, or the like, as well as other substrates such as for reticles, magnetic heads, flat panels, and the like , including substrates having various shapes such as square or rectangular substrates.
In addition, a particular wafer-related operation may relate to a batch of wafers, which may be arranged horizontally within a plane, vertically stacked, or otherwise positioned for group handling, processing, and so forth. The valves may include slot valves or any other isolation valves or other hardware for isolating the environment of a process tool from a shared vacuum environment of the system Each valve may be operable to selectively isolate one or more interior chambers.
The process tools may include any tools or modules suitable for processing semiconductor wafers. The process tools may also, or instead, include cluster tools with a number of different process tools arranged about a common wafer handler. The handling hardware may include one or more robotic arms, transport carts, elevators, transfer stations and the like, as well as combinations of these.
While in certain instances, the handling hardware may include a single robotic arm or transport cart, more complex combinations may be usefully employed, such as a number of robotic arms that hand off wafers along a line of process tools either directly or via a transfer station , a number of robots that service a cart such as a magnetically levitated cart or a cart on rails for relatively long distance transport, and so forth.
All such combinations that might be usefully employed to manipulate wafers and transfer wafers among process tools are intended to fall within the scope of the handling hardware described herein. The control software performs a variety of tasks associated with processing wafers within the system By way of example and not limitation, the control software may control operation of the valves , process tools , handling hardware , and load lock Each of these hardware items may have a proprietary or open programming interface, and the control software may also, or instead, manage communications with these hardware items, such as by interpreting data from the hardware or providing control signals to the hardware.
At a more abstract level, the control software may coordinate the various components of the system to schedule processing of one or more wafers , such as by coordinating and controlling operations of the load lock and handling system to move a wafer into the system and into one of the process tools The control software may also provide an external programmatic interface for controlling the entire system , and may also, or instead, provide information to a fabrication-wide computer infrastructure, such as event logs, status information, and the like.
As will be described in greater detail below, the control software may employ a neural network to calculate weights for a finite state machine that controls process scheduling. As will also be described in greater detail below, the control software may use data from or provide data to a wafer-centric database. The control software may also provide a graphical user interface for user interaction with the system and related process data.
The load lock may include any device or combination of devices that operate to control access to a vacuum or other controlled interior environment maintained for the handling hardware and process tools It will be appreciated that, while a single load lock is depicted, the system may include multiple load locks , such as an exit load lock opposite the first load lock i. The load lock may include vacuum pumps, vents, gas supplies, slot valves, and any other hardware useful for handling wafers in this context.
It will be further appreciated that the load lock may expose the interior environment directly to an external environment such as a clean room, or may be coupled to an equipment front end module, unified pod handler, or the like to transition single wafers or groups of wafers between the interior environment and other areas of a fabrication facility. It will be appreciated that the description of the system is purposefully generic. A semiconductor processing system may include a wide array of hardware, sensors and the like, all of which may be controlled or used by the control software to achieve desired wafer processing.
For example, although four process tools are depicted, it will be understood that fewer or more tools may be employed, and each tool may be a single tool, a process module, a cluster tool, stacked process modules, batch processing modules, and so forth. Further, while a linear arrangement is depicted, any suitable layout of tools and handling hardware may be usefully employed according to a particular process design.
Further, tools such as aligners, robots, carts, tracks, elevators, and the like may be employed, along with sensors such as pressure sensors, optical sensors, contamination sensors, etc. All such variations are intended to fall within the scope of the system described herein. In more complex processing scenarios, the system may process multiple wafers concurrently. Thus a first wafer may be introduced and moved by the handling hardware to a process tool and, while the first wafer is being processed, receive a second wafer which may be moved to a different process tool It will be appreciated that the foregoing software components and the arrangement thereof as depicted in FIG.
The process tool interfaces may be programming interfaces resident on process tools, process modules, cluster tools, or the like. Interfaces to other hardware may include physical interfaces e. This may include, for example, interfaces to robots, carts, elevators, aligners, slot valves, pumps, vents, heaters, coolers, electrically controlled grippers, and so forth. This may also include sensor interfaces such as outputs from thermometers, optical sensors, pressure sensors, gas detectors, voltmeters, ohmmeters, robotic drive encoders, and so forth.
The controller may be an integrated controller for a processing system, such as the system described above. The controller may be embodied on a computer or workstation located physically near the system, or may be embodied in a remote computer located in a control room or other computer facility, or may be integrated into a fabrication-wide software system.
The hardware interface may provide an consistent internal interface for the controller to exercise programmatic control over inputs and outputs for the hardware described above. Within the controller , the user interface component may provide a user interface for human control and monitoring of operation of the system The user interface, which is described below in greater detail, may include graphics, animation, simulations, manual control, recipe selection, performance statistics, and any other inputs or outputs useful for human control of the system It will be appreciated that a wide variety of interface techniques are known and may be usefully employed to provide a graphical user interface as described below.
This includes network-oriented interface technologies such as web server technologies, as well as application-oriented interface technologies.
Unless otherwise specified or clear from the context, all such technologies may be suitably employed with the systems and methods described herein. The scheduling component processes recipes for wafers within the system This may include scheduling of movements among process tools, as well as processing within particular process tools.
The scheduling component may receive recipes in any suitable machine readable form, and may create corresponding control signals for the system During execution, the control signals may be communicated to system components through the hardware interface Recipes and other system control instructions may be received from a remote location such as a central fabrication control system through the fabrication facility interface , or may be entered locally at a computer device that operates the controller Scheduling may be controlled in a number of different ways.
For example, the scheduling component may employ state machines and neural networks as described in greater detail below. However, numerous other scheduling techniques are known in the art for minimizing or reducing processing time and cost, many of which may be usefully employed with the systems and methods described herein. For example, the system may employ rule-based scheduling, route-based scheduling, state-based scheduling, neural network-based scheduling, and so forth. In one embodiment, the scheduling component may permit selection of one or more of these various scheduling models to control operation of the system This may be presented, for example, as a user-selectable option in a user interface such as the interface described below.
Once a scheduling technique is selected, a user may be prompted for any inputs such as rules, process steps, time constraints, and so forth. In embodiments, the scheduling component may employ multiple scheduling techniques concurrently. While one example of this is the neural-network-weighted state machine described below, it will be appreciated that numerous other combinations may be usefully employed.
For example, the scheduling component may use a state machine to control robotics, while rule-based scheduling is employed to control and optimize use of process tools. All such variations are intended to fall within the scope of this disclosure.
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The diagnostics component may monitor operation of the system This may include tracking scheduled maintenance as well as monitoring operation of the system to identify hardware failures and to determine, where possible, when failures are becoming more likely based upon current operation. One useful hardware diagnostics system is described, for example, in U. Other components may include any other useful modules, executable files, routines, processes, or other software components useful in operating the controller , and more generally, in controlling operation of a semiconductor manufacturing system as described herein.