This paper reports a metal-semiconductor-metal (MSM) photodetector formed by the surface thermal oxidation of a GaN thin film on a silicon (Si) substrate. A high-temperature treatment of 4 h was used to synthesize a high-quality gallium oxide (Ga2O3) thin film on top of the GaN layer, which was followed by the deposition of titanium-gold (Ti/Au) interdigitated electrodes to complete the photodetector fabrication. A physics-driven optimization model based on a multiphysical function, F(W, S), was used to design the geometric topology of the photodetector. Theoretical simulations unveil a global performance peak at the symmetric topology of the electrode width W = 50 μm and interdigital spacing S = 50 μm. To validate this framework, eight sets of electrodes of varying geometric dimensions (D1–D8) were constructed on the empirical Ga2O3 surface. The experimental results are in excellent agreement with the trends predicted by the physical model, with Sample D4 yielding the apex performance indices (F-score = 4.5 and F-intrinsic = 29.6). Furthermore, multidimensional performance radar profiles reveal that Sample D4 possesses the largest optimized envelope area, establishing a synergistic balance of carrier transport, signal-to-noise ratio, and spatial photon collection. This work provides theoretical foundations and practical design guidelines for the topological integration of high-performance Ga2O3-based MSM photodetectors developed via annealing oxidation processes.
The physical non-idealities inherent in emerging spintronic devices, such as device-to-device variations, stochastic switching, and nonlinear conductance responses, pose fundamental challenges for their deployment in in-memory computing architectures. Here, we investigate the integration of canted spin–orbit torque magnetic tunnel junctions (SOT-MTJs) for deep learning applications and propose a hardware-aware deep learning framework that embeds into the training process experimentally extracted device characteristics, which are faithfully resolved via a dedicated three-terminal SOT testing scheme that effectively suppresses spin-transfer torque, halves the switching threshold voltage, and eliminates parasitic drifts to minimize measurement-induced fluctuations and reveal intrinsic 1010-cycle endurance, thereby directly facilitating robust hardware–software co-optimization. By tuning a normalized conductance factor gnorm, we achieve a median classification accuracy of 88.9% on the Wine dataset using a 13 × 13 SOT-MTJ array, surpassing baseline software-to-hardware weight mapping. Incorporating hardware-aware co-training further elevates the median accuracy to 94.4%, demonstrating substantial robustness against device imperfections. Overall, our results highlight that explicitly modeling device variability at both the physics and algorithm levels can enable spintronic hardware based on SOT-MTJ arrays to function as compact, energy-efficient neuromorphic platforms, allowing device stochasticity to be incorporated as a learnable constraint, ultimately recovering hardware inference accuracy to within a few percentage points of the ideal software baseline.
The ability to realize stable dielectric tunability in ferroelectric thin films requires control over strain, thickness, and polar domain structure, particularly in materials exhibiting diffuse ferroelectric behavior. Here, we demonstrate that film thickness provides a key control parameter for domain configuration, phase-transition character, and dielectric response in epitaxial Ba0 . 8Sr0 . 2TiO3 thin films. Using atomic-scale polarization mapping and dielectric spectroscopy, we show that increasing thickness drives a transition from coherently strained, tetragonal-dominated polarization at 60 nm to partially relaxed films at 150 nm with mixed orthorhombic and rhombohedral in-plane domains, with strain relaxation resolved by reciprocal space mapping between 100 and 150 nm. This domain reconfiguration produces a diffuse ferroelectric phase transition accompanied by a systematic enhancement of dielectric permittivity and large electric-field tunability reaching similar to 79-85%, stable from 10 kHz to 1 MHz and up to similar to 473 K on SrTiO3. The observed dielectric diffuseness evolves systematically with film thickness, together with corresponding changes in ferroelectric domain configuration, transition temperature, and dielectric tunability. Importantly, the same thickness-domain-tunability correlations persist after silicon integration, yielding similar to 70% tunability with comparable frequency and thermal stability up to similar to 453 K. These findings establish a direct experimental link between dimensionality, domain evolution, diffuse ferroelectricity, and dielectric functionality in silicon-compatible oxide thin films.
We demonstrate flexible, solution-processable electrostatic discharge (ESD) protective coatings whose conductive filler is derived from upcycled tire pyrolysis char (TPC), a hazardous residue of end-of-life tire pyrolysis. A three-stage protocol uses 6 M HCl, 4 M NaOH, and 6 M HNO3, each at 80 °C for 3 h, for demineralization, desilication, and surface oxidation, respectively. The contribution is integration of established leaching chemistry with purity tracking and device characterization for one carbon stream. Inductively coupled plasma optical emission spectroscopy showed that the sum of quantified elemental concentrations decreased from 4.92 to 0.076 wt %, approximately 98.5%. Ash decreased from 9.85 wt % on an as-received basis (10.9 wt % on a dry basis) to 0.15 wt %, with Zn, Fe, Ca, and Al below the detection limit (≤5 mg kg−1). The quantified elements other than S, expressed as oxides, account for 79% of the dry-basis ash. The purified char (PTPC) showed a lower Raman D/G ratio (0.27 to 0.22), a 38.5% larger Brunauer−Emmett−Teller surface area, and oxygen-containing surface groups. Surface oxidation and improved interparticle contacts are plausible contributors to performance; their individual contributions were not isolated experimentally. Doctor-blade-coated nitrocellulose-bound films on poly(ethylene terephthalate) (PET) gave approximately 1 kΩ sq−1 sheet resistance and dissipated 99% of a 1 kV contact-discharge pulse in approximately 368 μs. The ink was shear-thinning (power-law n = 0.35). After 1000 bending cycles at a 5 mm radius, resistance increased by approximately 8% on PET and 25% on cellulose paper; PET-supported coatings showed approximately 2% drift after 30 days of ambient aging. Preliminary all-in costs are USD 4.3–7.0 kg−1, including reagents at USD 2.1–3.1 kg−1 with acid-liquor reuse. These estimates and proposed Zn and silica recovery remain unvalidated at pilot scale. The protocol demonstrates a laboratory-scale route from waste char to conductive carbon for flexible ESD protection.
Abstract Chemical oxidation and viscoelastic deformation can cause substantial signal drift and time-dependent responses in soft wearable sensors, limiting their long-term reliability. Here, we present a machine-learning-assisted signal interpretation framework using a common polyvinyl alcohol (PVA)/tellurium (Te) nanowire/selenium (Se) nanoparticle composite configured into two sensor architectures: an unencapsulated thin-film humidity sensor and a porous piezoresistive pressure sensor. The unencapsulated humidity sensor exhibited severe oxidation-induced ambient aging over 115 days, including a substantial chemical baseline resistance shift and more than 96% loss in raw response. By applying fractional normalization and few-shot XGBoost calibration, functional classification of aged humidity-related signals was partially recovered, achieving 77.3% accuracy on degraded sensor data. In a separate porous pressure-sensing architecture, compression-induced current responses were obtained above an activation threshold of approximately 191 kPa, and LSTM analysis enabled classification of time-series mechanical signals with 98.7% accuracy. These results suggest that algorithmic calibration and time-series learning can extend the functional usability of degraded or mechanically compliant soft sensors, although future work is required to demonstrate fully integrated dual-modal operation and cross-sensitivity suppression.
