In this research project we investigate the feasibility of a class of sensors for semiconductor manufacturing applications. The variables that these sensors can measure include etch rate, temperature, and plasma induced potentials.
The common theme shared by this class of sensors is that they are based on electrical impedance tomography (EIT). EIT involves injecting currents into an object while measuring the induced potentials on the surface of the object. The internal conductivity distribution can be approximately deduced from these measurements. This estimation problem is in general non-linear and poorly conditioned. Simulations have been performed to assess the potential performance of EIT based sensors in semiconductor manufacturing.
In a semiconductor manufacturing context, chemical and physical effects can induce conductivity changes in the interior of the wafer being processed. By placing electrodes at the wafer periphery and measuring potentials across these electrodes, we can infer conductivity changes. This can, in turn, be related to physical and chemical effects through process models. We have built a prototype etch-rate sensor based on this technology and it is being tested in the UC Berkeley Microfabrication Laboratory. The figure below shows the estimated change in thickness after wet etch using EIT (left) and the optically measured change in thickness (right).
Figure 1: Estimated change in thickness using EIT (left) and optically measured change in thickness (right)