Full Information Acquisition


Figure 5 : G-Mode voltage spectroscopy (G-VS): a technique for ultrafast imaging of polarization switching in ferroelectric materials. (a) Raw G-VS data fi ltered in the frequency domain using adaptive noise thresholding and a band-pass fi lter to reveal multiple material bias-induced strain loops that are characteristic of the hysteretic response shown by ferroelectric materials. (b) Spatial maps of single frequency PFM (S-PFM) amplitude and phase signals, area of the piezoresponse loop from band excitation polarization switching (BEPS), the current state-of-art method for spectroscopic studies on ferroelectric materials, area of the mean strain loops in G-VS. S-PFM and G-VS show high-resolution maps with 256 × 256 spatial pixels acquired in 8.5 and 17 minutes respectively, while BEPS acquired a 40 × 40 pixel image in 77 minutes illustrating that G-VS is 3–4 orders of magnitude faster than BEPS. Reprinted with permission from S Somnath et al., Nature Communications 7 (2016) 13290.


Furthermore, G-Mode overcomes the requirement for application of a DC bias [ 63 ], which can be problematic for voltage-sensitive materials [ 67 ] or operation in liquid [ 68 – 70 ].


It is important to note that despite the popularity of KPFM measurements, the level of information available (that is, CPD) is not suffi cient for systems such as electroactive materials, devices, or solid-liquid interfaces, involving nonlinear lossy dielectrics. In such cases it is not enough to know the bias dependence of the electrostatic force (or to be more precise, apex of the parabola); it is imperative that the time dependence of the electrostatic force is also known [ 70 ]. In G-Mode KPFM, for every period of AC voltage, the parabolic bias dependence of the electrostatic force, and hence the electronic properties, can be determined [ 64 ]. Since the probe raster motion is much slower than the electrical excitation, several 10s–100s of readouts can be performed at each pixel. In this way, G-Mode KPFM provides a measure of the transient changes in electronic properties of the sample at each spatial location, where the temporal resolution of the measurement is on the order of the AC voltage period. T is dynamic aspect will be particularly useful for probing surface photovoltage in photovoltaics [ 71 ], ion transport in materials and devices [ 72 ], and even screening processes at the solid-liquid interface [ 64 ]. G-Mode KPFM can potentially enable multi-frequency open-loop KPFM measure- ments, allow reliable measurements in liquid to probe nonlinear interactions, and improve the measurement rate of KPFM by 1,000 times via direct force-voltage curve reconstructions. Piezoresponse force microscopy . T e PFM technique is used for probing electromechanical activity at the nanoscale and provides insight into localized functionality of ferroelectric and multiferroic materials [ 6 , 73 – 80 ]. In PFM, an electric bias is applied


40


to a conductive AFM tip in contact with the sample. T e bias results in surface deformation because of the converse piezoelectric eff ect and/or strain-coupled electrochemical phenomena, as well as an electrostatic force at the tip-surface junction. T ese surface deformations induce vibrations in the cantilever, which are measured at the AFM photodetector. In spectroscopic modes of PFM, the ferroelectric properties of a material are explored via local hysteresis loop measurements, where the electromechanical response is measured as a function of applied DC bias. In classical, single-frequency PFM (S-PFM), the cantilever is excited with a sinusoidal bias (typically 10–500 kHz), and the resultant cantilever response is measured using a lock-in amplifi er. Consequently, PFM images only contain phase and amplitude information at the excitation frequency at each spatial pixel. In G-Mode PFM (G-PFM) [ 54 ] the complete cantilever response at each pixel is stored for later analysis for the same sinusoidal excitation. Similar to KPFM, G-PFM data can be analyzed either by applying digital lock-ins at any frequency or through multivariate statistical analysis methods such as principal component analysis (PCA). Figure 4 compares information from S-PFM, digital lock-in, and PCA applied to an example G-PFM dataset acquired on a polycrystalline Pb(Zr 0.2 Ti 0.8 )O 3 (PZT) ceramic. Here, the loading maps of the fi rst two PCA components show strong contrast between oppositely oriented domains and are similar to the amplitude and phase images from traditional PFM images. Correspondingly, the eigenvectors of the fi rst two components show almost identical harmonic content and correspond to the phase-shiſt ed (by 90 degrees) periodic components of the response. Note that unlike lock-in detection, each PCA component contains multiple harmonics. T e third eigenvector is dominated by the intrinsic cantilever resonance,


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