The ability to develop ferroelectric materials using binary oxides is critical to enable novel low-power, high-density non-volatile memory and fast switching logic. The discovery of ferroelectricity in hafnia-based thin films, has focused the hopes of the community on this class of materials to overcome the existing problems of perovskite-based integrated ferroelectrics. However, both the control of ferroelectricity in doped-HfO₂ and the direct characterization at the nanoscale of ferroelectric phenomena, are increasingly difficult to achieve. The main limitations are imposed by the inherent intertwining of ferroelectric and dielectric properties, the role of strain, interfaces and electric field-mediated phase, and polarization changes. In this work, using Si-doped HfO₂ as a material system, we performed a correlative study with four scanning probe techniques for the local sensing of intrinsic ferroelectricity on the oxide surface. Putting each technique in perspective, we demonstrated that different origins of spatially resolved contrast can be obtained, thus highlighting possible crosstalk not originated by a genuine ferroelectric response. By leveraging the strength of each method, we showed how intrinsic processes in ultrathin dielectrics, i.e., electronic leakage, existence and generation of energy states, charge trapping (de-trapping) phenomena, and electrochemical effects, can influence the sensed response. We then proceeded to initiate hysteresis loops by means of tip-induced spectroscopic cycling (i.e., “wake-up”), thus observing the onset of oxide degradation processes associated with this step. Finally, direct piezoelectric effects were studied using the high pressure resulting from the probe’s confinement, noticing the absence of a net time-invariant piezo-generated charge. Our results are critical in providing a general framework of interpretation for multiple nanoscale processes impacting ferroelectricity in doped-hafnia and strategies for sensing it.

OrbiSIMS is a secondary ion mass spectrometry method with dual mass analyzers: a time-of-flight (ToF) mass spectrometer for high-speed imaging and an Orbitrap™ for high mass resolving power and mass accuracy. Originally developed for biological imaging, there is now growing interest in the application to semiconductor materials to resolve peak interferences that obfuscate analysis in traditional SIMS depth profiling experiments. We use a new method to calibrate the Orbitrap intensity scale to true counts, which allows comparison of the useful yield and duty cycle with a magnetic sector instrument and a time-of-flight instrument using an Sb implant in the silicon sample. The useful yield of the Orbitrap and magnetic sector instruments (for one detected peak) are similar. However, since the magnetic sector instrument has serial mass detection, its useful yield reduces as more peaks are analyzed. While the ToF instrument has parallel detection, it has a low duty cycle and the useful yield is two orders of magnitude lower for 1000 eV Cs+ sputtering. The depth resolution was also compared from the measurement of the downslope from depth profiles of an Sb delta multilayer. For 1000 eV Cs+ sputtering, the downslopes are 3.9, 2.3, and 2.7 nm/decade for Orbitrap, magnetic sector, and ToF instruments, respectively. Ion trajectory simulation shows that the poorer depth resolution of the OrbiSIMS is in part due to ion beam distortion at low energy.

The complementary metal-oxide-semiconductor (CMOS) industry continues to push the boundaries of what is possible. Along with this, secondary ion mass spectrometry (SIMS) depth profiling continues to support CMOS R&D and high volume manufacturing (due to its unparalleled sensitivity and detection limits over predefined volumes and within acceptable time periods). New developments in SIMS are also being realized to support the move of the CMOS industry from planar structures to three-dimensional (3D) structures. This article presents a perspective of existing SIMS research areas for beyond one-dimensional structural analysis along with potential future SIMS implementation and data processing scenarios. The topics covered include: (1) existing SIMS depth profiling approaches for 3D structures, (2) in-fab SIMS deployment, (3) data analysis of in-fab SIMS derived depth profiles from 3D structures via pattern recognition, and (4) hybrid characterization approaches. With SIMS depth profiling of optimized (for SIMS) 3D structures already demonstrated, the remaining topics may serve to extend these and other capabilities and open new application areas for SIMS.