Advanced Electronic Materials最新文献

GeSn-on-Si avalanche photodiodes (APDs) are emerging as a promising solution for low-light detection in the short-wave infrared (SWIR) spectral range, including applications in imaging and telecommunications. In this work, key challenges such as high dark current and limited responsivity are addressed by demonstrating devices, which combine low noise with high signal amplification, while remaining compatible with silicon-based technology. GeSn-on-Si APDs with various Sn concentrations up to 1.9% are fabricated and characterized. The GeSn layers are grown pseudomorphically on Ge virtual substrates on Si wafers using molecular beam epitaxy. The devices comprise a double-mesa structure and exhibit a dark current dominated by a perimeter leakage path, independent of the Sn content. A dark current below 1 µA is maintained up to the onset of avalanche breakdown, marking a significant improvement compared to prior work. A record-high responsivity of 14.7 A W⁻¹ is achieved at 1550 nm for the APD with 1.9% Sn. Through impulse response measurements, the 3-dB bandwidth is determined to 1.2 GHz on devices with an 80 µm diameter, resulting in a responsivity-bandwidth-product of 17.6 A W⁻¹ GHz⁻¹. These results highlight the potential of GeSn-on-Si APDs for high-performance, low-light applications in the SWIR range.

Charge carrier transport in disordered semiconductors is critically influenced by the shape of the band tail in the density of states (DOS). To minimize energetic disorder and suppress band tails, deposition processes and post-treatment methods of semiconducting thin films must be carefully optimized. While capacitance–voltage (CV) measurements are routinely employed to extract doping densities and flatband voltages, no standardized procedure currently exists to quantitatively determine the DOS from such measurements. In this work, we address this gap by introducing a novel method to extract quantitative DOS information from CV data. Our approach relies on an analytical solution for charge accumulation in an exponential DOS distribution. We apply the method to Indium Gallium Zinc Oxide (IGZO) thin-film transistors and systematically investigate how measurement frequency and channel geometry affect the results. Comparison with alternative optical and electrical techniques confirms that CV measurements can provide reliable and straightforward access to DOS parameters, provided that the transistor channel dimensions exceed L × W = 20 µm × 100 µm. Additionally, CV measurements offer practical advantages, as they are fully compatible with standard transistor architectures, including encapsulation and light shielding commonly used in technological applications.

Neuromorphic computing systems require artificial synaptic devices capable of emulating complex biological neural functions. This study presents a dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene (DNTT)-based organic field-effect transistor that demonstrates synaptic plasticity under optical stimulation at 200 K. The device exhibits a dual-mechanism synaptic behavior through charge separation and trapping, where photogenerated holes provide rapid transport while electrons are preferentially captured in deep trap states, creating persistent field modulation. Excitatory postsynaptic current measurements reveal characteristic three-phase temporal dynamics with rapid activation, exponential decay, and sustained enhancement lasting tens of minutes. Paired-pulse facilitation demonstrates short-term plasticity with dual exponential decay constants of 140 and 610 ms, while multi-pulse stimulation produces remarkable persistent current level enhancement exceeding 10 000% of the initial baseline, reflecting sequential filling of continuous trap state distributions. The device simultaneously implements both short-term and long-term plasticity mechanisms in a single component, enabling simultaneous working memory and persistent information storage functions. Neuromorphic functionality is demonstrated through simulated XOR logic operations, showing non-linearly separable computation capabilities. The 200 K operating temperature aligns favorably with Mars surface conditions, requiring minimal heating compared to terrestrial cooling requirements, making the device particularly promising for space-based neuromorphic systems where radiation-hard organic semiconductors provide additional advantages.

In this work, a compact model for mushroom-type phase-change memory devices is introduced that incorporates the shape and size of the amorphous mark under different programming conditions, and is applicable to both projecting and non-projecting devices. The model includes analytical equations for the amorphous and crystalline regions and uniquely features a current leakage path that injects current at the outer edge of the electrodes. The results demonstrate that accurately modeling the size and shape of the phase configurations is crucial for predicting the full-span of the RESET and SET programming, including the characteristics of threshold switching. Additionally, the model effectively captures read-out behaviors, including the dependence of resistance drift and bipolar current asymmetry behaviours on the phase configurations. The compact model is also provided in Verilog–A format, so it can be easily used in standard circuit-level simulation tools.

In-sensor reservoir computing offers a promising paradigm for signal analysis by embedding sensing and computation within a single platform. However, it remains challenging to realize both dynamic temporal processing and long-term memory using a single device. Here, we report a multi-modal and reconfigurable oxide-based memristive device that enables both volatile and nonvolatile switching modes in a unified architecture. By precisely tuning the crystallinity of the TiO₂ layer and adjusting the compliance current, we modulate the conductive filament dynamics to switch between volatile and nonvolatile behavior, and multi-modal switching is verified based on nucleation theory. The volatile mode enables fading memory and nonlinearity required for high-dimensional temporal encoding, while the nonvolatile mode provides robust analog weight storage with 5-bit resolution and retention exceeding 10⁵ s. These dual functions are integrated into a neuromorphic in-sensor reservoir computing system. The system accurately reconstructs ECG waveforms (NRMSE = 0.010) and achieves multi-step prediction of pH time-series (accuracy = 98.2%), while reducing energy consumption by over five-fold compared to conventional echo state networks. We demonstrate a scalable and energy-efficient approach toward intelligent biochemical sensing, highlighting how material-level configurability in memristive devices can unlock new directions for on-sensor neuromorphic hardware.

Ferroelectric Memcapacitors

In their Research Article (10.1002/aelm.202500421), Woo Young Choi and co-workers propose and demonstrate a novel capacitive time-domain (TD) content-addressable memory (CAM) based on a single ambipolar ferroelectric memcapacitor (1C) exhibiting band-reject-filter-shaped capacitance-voltage characteristics. The proposed 1C TD CAM performs linear Hamming distance computation through propagation delay modulation, enabling high-density associative memory and highly reliable in-memory nearest-neighbor search for one-shot learning.

Wearable triboelectric pressure sensors hold tremendous promise in sign language recognition. Nevertheless, the poor stability and low output are unfavorable to acquire accurate and stable electrical signals for wearable sensors. Herein, we have prepared a waterproof and stable MoBT_x/PDMS-based triboelectric pressure sensor with a controllable dielectric constant. This pressure sensor illustrates high output performance (144 V, 2800 nA, and 46 nC), high stability (5000 cycles stretching, 5000 cycles bending, and 5000 cycles after washing), and long-term robustness (60 days). The sensor arrays with 7 channels consisting of step-type and stripe-type pressure sensors, can generate distinctive responses corresponding to specific hand gestures, contributing to a higher resolution of sign language letters. Integrated with a microcontroller, this system achieves precise gesture recognition and effective conversion of sign language movements into an audio signal for real-time communication. This work offers a robust strategy toward reliable, high-resolution, and real-time sign language communication, advancing assistive technologies for individuals with disabilities.

Organic neuromorphic circuits offer new opportunities for low-power, flexible electronics capable of real-time inference at the edge. In this work, we present a neuromorphic system based on organic thin-film transistors (OTFTs) that performs biosignal classification in a Bayesian framework. The spiking behavior of artificial OTFT neuron circuits are first characterized, showing how their dynamics can be tuned through circuit parameters. We then demonstrate how the circuits can infer information from real-world electroencephalography (EEG) data. When applied to epilepsy-related signal patterns, the system achieves excellent classification performance, while maintaining ultra-low power operation. These results highlight the potential of OTFT-based neuromorphic architectures for embedded medical diagnostics.

AlN has the largest bandgap in the wurtzite III-nitride semiconductor family, making it an ideal barrier for a thin GaN channel to achieve strong carrier confinement in field-effect transistors, analogous to silicon-on-insulator technology. Unlike $�{\mathrm{SiO}}_2$/Si/$�{\mathrm{SiO}}_2$, AlN/GaN/AlN can be grown fully epitaxially, enabling high carrier mobilities suitable for high-frequency applications. However, developing these heterostructures and related devices has been hindered by challenges in strain management, polarization effects, defect control, and charge trapping. Here, the AlN single-crystal high electron mobility transistor (XHEMT) is introduced, a new nitride transistor technology designed to address these issues. The XHEMT structure features a pseudomorphic GaN channel sandwiched between AlN layers, grown on single-crystal AlN substrates. XHEMTs demonstrate RF performance on par with the state-of-the-art GaN HEMTs, achieving 5.92 W/mm output power and 65% peak power-added efficiency at 10 GHz under 17 V drain bias. These devices overcome several limitations present in conventional GaN HEMTs, which are grown on lattice-mismatched foreign substrates that introduce undesirable dislocations and exacerbated thermal resistance. With the recent availability of 100-mm AlN substrates and AlN's high thermal conductivity (340 W/$��m�·�K�$), XHEMTs show strong potential for next-generation RF electronics.