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Photonic curing is a large‐area, high‐throughput thermal processing technique that uses high‐intensity pulsed light to selectively cure thin films on thermally sensitive substrates. This study employs 3‐dimensional (3D) simulation to show, for the first time, that gate geometry significantly impacts peak curing temperature during photonic curing. The simulation results are experimentally validated by photonically curing solution‐processed indium zinc oxide for thin‐film transistors with different bottom gate geometries and comparing their performance to thermally annealed control devices. Under the same photonic curing pulse, for a fixed aspect ratio, peak photonic curing temperature increases with larger gate area, while for a fixed area, peak photonic curing temperature decreases with increasing aspect ratio. For different gate areas and aspect ratios, the simulated peak photonic curing temperature varies from ≈200 to 450 °C, which strongly impacts metal‐hydroxide to metal‐oxide conversion in sol–gels. Thus, the subsequent transistor performance is strongly influenced by the gate geometry. For example, for increasing gate area with fixed aspect ratio of 1, the average mobility increases from 1.61 to 12.52 cm2 V−1 s−1, while the threshold voltage decreases from 2.14 to −5.68 V. Thus, this study provides valuable insights for adopting 3D simulation to design transistors for complex large‐area electronics using photonic curing.
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Multi-objective Bayesian optimization with human-in-the-loop for flexible neuromorphic electronics fabrication
Neuromorphic computing hardware enables edge computing and can be implemented in flexible electronics for novel applications. Metal oxide materials are promising candidates for fabricating flexible neuromorphic electronics, but suffer from processing con- straints due to the incompatibilities between oxides and polymer substrates. In this work, we use photonic curing to fabricate flexible metal–insulator–metal capacitors with solution-processible alumi- num oxide dielectric tailored for neuromorphic applications. Because photonic curing outcomes depend on many input para- meters, identifying an optimal processing condition through a traditional grid-search approach is unfeasible. Here, we apply multi-objective Bayesian optimization (MOBO) to determine photo- nic curing conditions that optimize the trade-off between desired electrical properties of large capacitance–frequency dispersion and low leakage current. Furthermore, we develop a human-in-the- loop (HITL) framework for incorporating failed experiments into the MOBO machine learning workflow, demonstrating that this frame- work accelerates optimization by reducing the number of experi- mental rounds required. Once optimization is concluded, we analyze different Pareto-optimal conditions to tune the dielectric’s properties and provide insight into the importance of different inputs through Shapley Additive exPlanations analysis. The demon- strated framework of combining MOBO with HITL feedback can be adapted to a wide range of multi-objective experimental problems that have interconnected inputs and high experimental failure rates to generate usable results for machine learning models.
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- Award ID(s):
- 2135203
- PAR ID:
- 10663688
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Journal of Materials Chemistry C
- ISSN:
- 2050-7526
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1039/D5TC03660G
