Thiel Research Group

Electron microscopy is well established as one of the most powerful tools for the study of microstructure. Low vacuum instruments represent the latest evolution of scanning electron microscopes, and can offer a much richer variety of information on specimen characteristics. These tools allow routine examination of dielectric and insulating materials while avoiding complications due to charging effects. However, the processes that give rise to secondary electron emissions are complex, and not well understood for these materials. Furthermore, the presence of a low pressure gas in these instruments allows for a wide variety of electron-gas interactions that potentially also can be used to gain new insights into the nature of specimen.

As the feature sizes in microelectronics devices continue to shrink, the specifications for manufacturing quality control become increasingly demanding. As a general rule, the size of a critical defect in a patterning step is half of the feature size of the technology node. Thus for a so-called 7 nm technology chip, the critical defect size is 3.5 nm. Scanning electron microscopy has long been a standard method for measuring critical dimensions on patterned wafers (the CD-SEM), but now there is increased interest in using electron beam technologies for pattern defect inspection. Our work uses a combination of simulations and experiments on reference standards to consider how the electron-optical performance of the SEM impact CD measurement of complex architectures and sensitivity to defects.

Charged particle beams can be used to initiate chemical reactions in precursor gases adsorbed onto substrates. Metallorganic precursors, for example, can be used to deposit metals. Other molecules can be used to etch and remove material. This approach therefore offers the possibility of direct-write fabrication of nanometer-scale structures, including three dimensional architectures. However, currently very little is understood about the relevant physics of the electron-precursor molecule interactions, nor are the kinetics of the processes well known. Our group examines the effects of deposition conditions on the structure, composition, and physical properties of the deposit, as well as considering the role of different precursor chemistries.

All of the technologies described here fundamentally derive from the interactions of energetic electrons with matter; that is, scattering processes, the creation and emission of secondary electrons, and processes by which the kinetic energy of an incident electron is absorbed into materials. These mechanisms are statistical processes that can be described by cross-sections. We use these cross-sections in a variety of Monte Carlo simulations to study the spatial distributions of energy deposition and stochastic emission processes in crystalline and amorphous “hard” materials, soft materials such as molecular, polymeric, and liquid systems, and gaseous species.

Electron beam technologies are both powerful and versatile for a wide range of nanotechnology applications. Their chief drawback, however, is speed for macroscopic applications. Inspecting an entire 300 mm silicon wafer for patterning defects in microelectronics manufacturing would take several months under typical conditions. Creating macroscopic structures using single beam EBID is even slower. One solution is the use of massively parallel arrays of electron beams working in unison. One means of producing a large array of beams is by splitting a single beam into many beamlets within a single electron-optical column. An alternative approach involves making large arrays of miniature columns. Both approaches have advantages and disadvantages depending on the specific application, but all applications and implementations depend on achieving a high degree of uniformity in the behavior of the beamlets in the

array. Our work in this area is in determining the relevant performance specifications needed for various applications as well as devising methodologies for assessing the variations. A specific example is the use of contrast transfer functions to determine the sensitivity to semiconductor patterning defects as a function of SEM operating parameters.