Sensor Technology Drivers in Semiconductor Manufacturing
June 6, 2012 Leave a comment
As in many industries, the degree of automation in semiconductor manufacturing is increasing. The reasons for this are the same as in any industry striving to automate: increase throughput, reduce labor, and improve quality.
However, semiconductor manufacturing presents some unique technical challenges that differentiate it from conventional manufacturing in other industries. Some of the factors driving sensor technology in automated semiconductor manufacturing include:
- Small size. The clean room environment, necessary for semiconductor processing, is very expensive per square foot. There is constant pressure to reduce the size of the machines, and the sensors that go into them.
- In fact, the high cost of clean room space is another motivator for reducing the role of humans in the process. Not only do humans take up a lot of physical space, they represent about 75% of the particle contaminant sources in the clean room.
- Advanced Process Control (APC). APC is a method for shortening the time frame between collection of SPC (Statistical Process Control) data and the application of process corrections. This means that rather than time-consuming external metrology, there is a drive for so-called “in-situ” metrology. There is a need to measure process variables in real-time or near-real time in order to close the APC loop in a shorter time frame.
- Extreme environmental conditions. Semiconductor manufacturing can often involve special ambient conditions, for example:
- Liquid or gaseous chemistries that can attack various standard sensor housing materials
- Vacuum chambers, where outgassing from the sensor must be controlled and the sensor must not destructively expand due to gasses trapped inside it
- High temperatures
- High pressures, e.g. sensors operating inside valves
In many cases, sensor applications in semiconductor manufacturing involve a very specific set of mechanical, environmental, and sensing performance requirements that ultimately require a custom sensor solution. Since the unit volume for such custom sensors is low, the cost per sensor can end up correspondingly high. Higher sensor costs tend to mitigate the cost benefits of space reduction and increased automation, so it comes down to a process of optimization. Ultimately, the goal is to generate a positive return on investment by ensuring that the cost savings resulting from automation easily exceed the cost of automating.