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High throughput experimental methods are known to accelerate the rate of research, development, and deployment of electronic materials. For example, thin films with lateral gradients in composition, thickness, or other parameters have been used alongside spatially-resolved characterization to assess how various physical factors affect material properties under varying measurement conditions. Similarly, multi-layer electronic devices that contain such graded thin films as one or more of their layers can also be characterized spatially in order to optimize the performance. In this work, we apply these high throughput experimental methods to thin film transistors (TFTs), demonstrating combinatorial device fabrication and semi-automated characterization using sputtered Indium-Gallium-Zinc-Oxide (IGZO) TFTs as a case study. We show that both extrinsic and intrinsic types of device gradients can be generated in a TFT library, such as channel thickness and length, channel cation compositions, and oxygen atmosphere during deposition. We also present a semi-automated method to measure the 44 devices fabricated on a 50x50mm substrate that can help to identify properly functioning TFTs in the library and finish the measurement in a short time. Finally, we propose a fully automated characterization system for similar TFT libraries, which can be coupled with high throughput data analysis. These results demonstrate that high throughput methods can accelerate the investigation of TFTs and other electronic devices.
Fabricating high-performance and/or high-density flexible electronics on plastic substrates is often limited by the poor dimensional stability of polymer substrates. This can be mitigated by using glass carriers during fabrication, but removing the p
MPC (Magneto-Photonic Crystal) Optimisation is a feature-rich Windows software application designed to enable researchers to analyze the optical and magneto-optical spectral properties of multilayers containing gyrotropic constituents. A set of compu
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Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally
The Helium Ion Microscope (HIM) has the capability to image small features with a resolution down to 0.35 nm due to its highly focused gas field ionization source and its small beam-sample interaction volume. In this work, the focused helium ion beam