A system-on-a-chip for rapid-throughput critical dimension microscopy

Many lithographically created optical components, such as photonic crystals, require the creation of periodically repeated structures [1]. The optical properties depend critically on the consistency of the shape and periodicity of the repeated structure. At the same time, the structure and its period may be similar to, or substantially below that of the optical diffraction limit, making inspection with optical microscopy difficult.
Inspection tools must be able to scan an entire wafer (300 mm diameter), and identify wafers that fail to meet specifications rapidly. However, high resolution, and high throughput are often difficult to achieve simultaneously, and a compromise must be made.
TeraNova is developing an optical inspection tool that can rapidly image features on wafers. Their product relies on (a) knowledge of what the features should be, and (b) a detailed and accurate model of light diffraction from the wafer surface. This combination allows deviations from features to be identified by modifying the model of the surface features until the calculated diffraction pattern matches the observed pattern.
This form of microscopy—known as Fourier microscopy—has the potential to be very rapid and highly accurate. However, the solver, which calculates the wafer features from the diffraction pattern, must be very rapid and precise. To achieve this, a hardware solver will be implemented. The hardware solver must be combined with mechatronic tracking of the absolute wafer position, requiring the automatic identification of fiduciary markers.
Finally, the problem of computer obsolescence in instrumentation (resulting in security weaknesses) will also be addressed by combining the digital hardware and software into a system-on-a-chip (SoC) to provide a powerful, yet secure operating environment for the microscope software.

Eindrapportage

Teranova has developed a scatterometer that allows manufacturers to rapidly characterise structures tiny structures that are otherwise difficult to measure. This sort of measurement is very important for, for instance, ensuring the quality of the optics used to project images in AR/VR glasses. Aside from the device—a type of microscope—a rapid and computationally intensive calculation must be performed repeatedly before characterisation is complete. This calculation was a bottleneck in the through put of the scatterometer.

We took a two-pronged approach to speeding the calculations: we developed a machine learning model that uses the raw measurement data to predict the results of the characterisation. We showed that for the set of structures we investigated, the machine learning model predictions reduced the required computation time significantly. However, the training data set was not extensive enough to provide accurate predictions for all types of structures.

We also investigated using a hybrid computational approach: using a field programmable gate array (FPGA) to perform computations that are very expensive on a CPU (matrix inversion, for instance). One barrier to implementation on an FPGA is the transition from floating point numbers to block floating point numbers, which are more memory efficient. The transition to block floating point numbers and the implementation of basic matrix operations were able to be demonstrated in models, but implementation on hardware has proven to be complex.

In summary: the number of computations that the scatterometer computer needs to be performed has been reduced by a machine learning model, while the speed of computation has not yet been improved by the implementation of digital hardware