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Register a new userHigh-throughput fabrication and semi-automated characterization of oxide thin film transistors
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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.
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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 plastic substrate from a large-area carrier without damaging the electronics remains challenging. Here we present a large-area photonic lift-off (PLO) process to rapidly separate polymer films from rigid carriers. PLO uses a 150 microsecond pulse of broadband light from flashlamps to lift off functional thin films from a glass carrier substrate coated with a light-absorber layer (LAL). A 3D finite element model indicates that the polymer/LAL interface reaches 865 degrees C during PLO, but the top surface of the PI reaches only 118 degrees C. To demonstrate the feasibility of this process in the production of flexible electronics, an array of indium zinc oxide (IZO) thin-film transistors (TFTs) was fabricated on a polyimide substrate and then photonically lifted off from the glass carrier. The TFT mobility was 3.15 cm2V-1s-1 before and after PLO, indicating no significant change during PLO. The flexible TFTs were mechanically robust, with no reduction in mobility while bent. The PLO process can offer unmatched high-throughput solutions in large-area flexible electronics production.
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 computational algorithms aimed at enabling the design optimization and optical or magneto-optical (MO) spectral analysis of 1D magnetic photonic crystals (MPC) is reported, together with its Windows software implementation. Relevant material property datasets (e.g., the optical spectra of refractive index, absorption, and gyration) of several important optical and MO materials are included, enabling easy reproduction of the previously published results from the field of MPC-based Faraday rotator development, and an effective demonstration-quality introduction of future users to the multiple features of this package. We also report on the methods and algorithms used to obtain the absorption coefficient spectral dispersion datasets for new materials, for which the film thickness, transmission spectrum, and refractive index dispersion function are known.
Flexible and transparent electronics presents a new era of electronic technologies. Ubiquitous applications involve wearable electronics, biosensors, flexible transparent displays, radio-frequency identifications (RFIDs), etc.Zinc oxide (ZnO) and related materials are the most commonly used inorganic semiconductors in flexible and transparent devices, owing to their high electrical performance, together with low processing temperature and good optical transparency.In this paper, we review recent advances in flexible and transparent thin-film transistors (TFTs) based on ZnO and related materials.After a brief introduction, the main progresses on the preparation of each component (substrate, electrodes, channel and dielectrics) are summarized and discussed. Then, the effect of mechanical bending on electrical performance was highlighted. Finally, we suggest the challenges and opportunities in future investigations.
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 be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.
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 of a HIM is utilized to create nanopores with diameters down to 1.3 nm. It will be demonstrated that nanopores can be milled into silicon nitride, carbon nanomembranes (CNMs) and graphene with well-defined aspect ratio. To image and characterize the produced nanopores, helium ion microscopy and high resolution scanning transmission electron microscopy were used. The analysis of the nanopores growth behavior, allows inferring on the profile of the helium ion beam.
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