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Intelligent perception with closed-loop information acquisition, processing, and feedback is critical for humanoid robots and embodied intelligence systems. Ionochromic transistors hold great potential for on-site signal processing and visual feedback. Here, we report a bioinspired ionochromic neuromorphic device with integrated signal-processing capabilities for intelligent perception and display. The transistor unit consisting of poly(3-hexylthiophene) (P3HT) and [EMIM][TFSI] (1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide) ion gel, achieves synchronous conductance modulation and reversible color change via voltage-controlled ion doping effects, mimicking biological synaptic response, and color regulation in chameleons. This dual-functional behavior originates from the generation of polarons/bipolarons that reconfigure the P3HT energy levels and modify optical transitions. The device exhibits a high-contrast electrochromic transition, with absorbance at 520 nm decreasing from 36.3% to 18.9%. Furthermore, a uniform electrolyte-gated transistors array enables tactile signal visualization, verified by a visual Morse code system and robotic hand integration, realizing in situ tactile perception and accurate object recognition. This work provides an alternative solution for integrated intelligent perception, advancing the development of next-generation human-machine interaction (HMI) platforms.

Neuromorphic computing seeks to replicate the computational efficiency and parallelism of the human brain by emulating its neural architecture. In this work, we present the Array of Cointegrated Transistor-Based Artificial Neurons and Synapses (ACTANS), a simplified hardware architecture that unifies transistors as both artificial neurons and synapses within a fully compatible complementary metal-oxide-semiconductor (CMOS) fabrication process. The artificial neurons and synapses are homotypic transistors that are structurally identical but functionally distinct. The proposed ACTANS consists of one neuron and sixteen connected synapses. In contrast to circuit-based neuromorphic systems that are limited by excessive area requirements and high-power consumption, and to non-CMOS systems that struggle to integrate neurons, synapses, and peripheral circuits owing to incompatibility with CMOS processes, the ACTANS architecture provides a compact device footprint and enables seamless neuron-synapse-circuit cointegration. The system is capable of performing cognitive tasks such as letter and pattern recognition.

Organic electrochemical transistors are commonly benchmarked using volumetric capacitance (C*); however, this metric does not distinguish between Faradaic and non-Faradaic contributions and therefore does not directly quantify the density of electronically active charge carriers that governs device-relevant doping capacity. Here we introduce effective volumetric capacitance (Ceff* = C* · η), where η denotes doping efficiency, as a metric for electronically effective volumetric doping. Using this metric, we show ionophilic side chains promote high ionic uptake but also sequester ions away from the conjugated backbone, lowering doping efficiency. By contrast, side-chain removal increases ion access to electrochemically addressable backbone sites, enabling doping efficiencies approaching unity. The resulting materials exhibited both enhanced volumetric charge density and improved charge transport, yielding transistors with transconductance among the highest reported. These results establish a practical design rule for organic mixed conductors by showing that electronically effective doping, rather than ionic uptake alone, governs transistor operation.

Understanding the transconductance (gm) at cryogenic temperature remains a major challenge in the physics-based modeling of nanoscale field-effect transistors. Although short-channel drain current (ID)-gate voltage characteristics can often be described accurately within transport-based formalisms, the corresponding gm commonly exhibits pronounced low-temperature anomalies that remain unexplained. Here, we investigate a 65 nm Si n-channel transistor measured from 250 K down to 12 K and show that a Landauer-consistent model provides an accurate quasi-ballistic transport baseline for the ID across the full temperature range, but fails to reproduce the shoulder-like gm feature that emerges at deep cryogenic temperature. We show that this residual discrepancy originates not from transport nonidealities but from quantum capacitance (Cq)-induced electrostatic renormalization of gate-induced charge modulation. By decomposing the gm into transport and charge-response contributions, and introducing a gate-control factor governed primarily by the Cq, we derive a compact framework in which the corrected gm consists of renormalized baseline and differential electrostatic terms. A two-crossover parameterization of Cq in the effective gate-overdrive domain enables simultaneous and self-consistent reproduction of both the ID and gm, including the cryogenic shoulder. These results identify anomalous cryogenic gm as a Cq-induced signature of rapidly varying gate-to-charge coupling in quasi-ballistic transistors.

Biological visual systems perceive information by processing light signals through the regulation of synaptic weights in the nervous system. Consequently, optoelectronic synaptic devices that directly respond to light stimuli and mimic synaptic plasticity hold immense potential for constructing highly efficient neuromorphic computing systems. This paper reports an optoelectronic synaptic transistor utilizing amorphous indium gallium zinc oxide (IGZO) to serve as the active channel layer of the device. Owing to its wide bandgap, the transistor shows a strong positive photoresponse under ultraviolet (UV) light, leading to substantial photocurrent enhancement. Simultaneously, applying electrical pulses suppressed the current response of the device, achieving negative modulation of synaptic weights, which successfully simulates the dynamic balance mechanism between excitatory and inhibitory effects in biological synapses. Based on this phenomenon, this work defines optoelectronic co-modulation as an operation mode that achieves bidirectional dynamic regulation of channel conductance via ultraviolet light-induced carrier excitation and electrical pulse-induced charge trapping. Leveraging the photoelectric synergistic properties of the device, we successfully simulated key biological synaptic functions including postsynaptic current (PSC), paired-pulse depression (PPD), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). Based on this, we achieved fundamental "AND" and "OR" logic gate functions. Furthermore, when the device was applied to handwritten digit recognition tasks, the neural network achieved an accuracy of 90%.

Gaussian distribution functions underpin a wide range of probabilistic computing models, yet their faithful and tunable implementation at the hardware level remains a fundamental challenge. Conventional approaches based on anti-ambipolar transistors rely on heterojunctions formed from dissimilar semiconducting materials, introducing intrinsic asymmetries in carrier mobility, interface quality, and band alignment that prevent accurate mirroring of symmetric Gaussian curves. Here we report a single-material, single-channel split-gate Gaussian-mirroring transistor (SC-GMT) that generates symmetric, Gaussian-shaped transfer curves through reversal voltage biasing. By independently modulating carrier concentrations via split-gate control, the device achieves tunable amplitude, mean, and standard deviation with >99.99% coefficient of determination to ideal Gaussian distributions. To demonstrate practical utility, we integrate the SC-GMT into a custom-built printed circuit board with digital-to-analog control and real-time current sensing. Using this platform, we implement a hardware Gaussian Naive Bayes (GNB) classifier capable of distinguishing deepfake and authentic voices with 82% accuracy. Moreover, the transistor's drain current scales with the product of two gate voltages, enabling quadratic-order analog multiplication critical for probabilistic models and attention-based architectures.

Oral diseases represent a major global health burden, underscoring the need for sensitive, accessible, and noninvasive diagnostic technologies. Electrochemical biosensing offers a powerful route for point-of-care oral health monitoring by translating biomolecular interactions in saliva, gingival crevicular fluid, and exhaled breath condensate into quantifiable electrical signals. This review systematically discusses electrochemical biosensing strategies for oral disease diagnosis, beginning with the structure and operating mechanisms of electrochemical sensors and then summarizing their applications in detecting disease-related nucleic acids, proteins, pathogens, and other molecules. We further examine feasible strategies for early diagnosis, including signal amplification methods based on nanomaterials, enzyme catalysis, nucleic acid amplification, chemical deposition, and cascade integration, as well as antifouling interfaces designed to maintain stable sensing performance in complex oral biofluids. Particular attention is given to advanced transistor architectures, especially organic electrochemical transistors (OECTs), which offer intrinsic signal amplification and high-gain readout for low-abundance biomarkers. Finally, we outline current challenges, future directions, and translational opportunities for electrochemical biosensing technologies, providing a roadmap toward precision dentistry and modern oral health management.

Porphyromonas gingivalis (P. gingivalis) as a keystone pathogen of periodontal diseases has been increasingly associated with periodontitis and systemic comorbidities. The rapid and sensitive detection of P. gingivalis in saliva remains challenging because of the complex salivary matrix and insufficient sample preparation in conventional assays. To address these limitations, we developed an integrated saliva-compatible biosensing platform by combining polymer-free viscoelastic microfluidic pretreatment with an electrolyte-gated graphene field-effect transistor (GFET) biosensor. The microfluidic module exploits saliva's intrinsic viscoelasticity to enable additive-free particle focusing by co-flowing viscoelastic saliva with Newtonian PBS. This self-conditioning generates synergistic inertial and elastic lift forces that laterally separate P. gingivalis from the salivary matrix, achieving 93.8% bacterial recovery and 82.1% protein removal. To enhance measurement reliability on the antibody-functionalized Au side-gate, a reference-gate-based differential calibration strategy was implemented to suppress common-mode interference and environmental drift. With this calibration, the integrated GFET biosensor achieved a limit of detection of 291 CFU/mL over a linear range of 1 × 103-1 × 108 CFU/mL, and the clinical validation demonstrated robust discrimination between periodontitis and healthy controls with a high sensitivity of 94.1%. This integrated biosensor provides a compact, low-consumption, and automation-friendly route for saliva-compatible transistor biosensing and advances practical point-of-care periodontal diagnostics.

Alzheimer's disease (AD) is a common neurodegenerative disorder. Compared to the limited specificity of single-biomarker detection, the combined detection of multiple biomarkers can significantly improve the accuracy of AD diagnosis. In this study, a field-effect transistor (FET) biosensor array integrating four independently functionalized regions was fabricated by modifying carbon nanotube (CNT) surfaces with amyloid bovine serum albumin (AL-BSA) nanofilm as an antifouling layer, enabling the simultaneous detection of multiple tau proteins. The proposed FET biosensor exhibited excellent anti-fouling performance while maintaining high sensitivity, with detection limits as low as 3.3 fg/mL, 0.17 fg/mL, 0.02 fg/mL, and 2.5 fg/mL for p-tau181, p-tau217, p-tau231, and t-tau, respectively. Evaluation of 35 clinical serum samples indicated that p-tau181 outperformed the other three tau proteins as an AD diagnostic biomarker. By further incorporating a support vector machine (SVM)-based machine learning algorithm and employing multi-biomarker co-detection, the accuracy of AD diagnosis can be improved to nearly 100%. This study provides a novel strategy for array FET biosensors with high clinical application potential for the precise detection of AD.

Triboelectric nanogenerators provide a direct electrical interface to mechanical stimuli, yet most triboelectric-neuromorphic integrations rely on volatile conductance modulation or indirect capacitive coupling, limiting memory-assisted tactile perception. Here, we demonstrate direct triboelectric programming of a MoS2/HfO2/PbZr0.3Ti0.7O3 ferroelectric transistor, where rectified triboelectric pulses are coupled to ferroelectric switching to realize cumulative conductance modulation. The spike-like outputs generated by mechanical contact-separation are translated into history-dependent conductance evolution through polarization-controlled electrostatic modulation in PbZr0.3Ti0.7O3 assisted by charge trapping in HfO2. Systematic correlation between triboelectric pulse amplitude, repetition frequency, and channel conductance reveals that repetitive mechanical inputs progressively accumulate polarization-mediated states without external signal amplification or continuous waveform processing. The device exhibits fundamental synaptic characteristics under electrical stimulation, while triboelectrically generated inputs enable cumulative tactile programming. By constructing a force-frequency-dependent conductance map and defining a reference current window, tactile conditions are encoded into stable state regimes, allowing threshold-based discrimination based on accumulated device states rather than instantaneous signals. Furthermore, the experimentally parameterized encoding framework is extended to electrocardiogram signal classification, demonstrating its capability to transform temporal bio-signals into device-relevant conductance features for neuromorphic signal processing. These results establish a physics-grounded route for analog memory-assisted tactile state encoding in neuromorphic sensory systems.

DNA methylation is an important epigenetic biomarker for early disease screening and prognosis evaluation, but its reliable detection remains challenging because methylated DNA is often present at low abundance in complex biological backgrounds. Here, we report a methylation-sensitive bioelectronic sensing platform that integrates AciI-assisted target discrimination, CRISPR/Cas12a-mediated trans-cleavage, and vertical organic electrochemical transistors (vOECTs) amplification for highly sensitive methylated DNA detection. In this strategy, unmethylated DNA is selectively digested by AciI, while intact methylated DNA activates the crRNA-guided Cas12a system, triggering collateral cleavage of ssDNA reporters immobilized on the Au gate electrode. The resulting interfacial changes are efficiently amplified by the vOECTs through coupled electric-double-layer gating. The platform achieved quantitative methylated DNA detection from 100 fM to 100 pM with a sensitivity of 267.6 μA/dec and a detection limit of 100 fM. The sensor also exhibited good operational stability, reproducibility, and reliable recovery performance in artificial serum samples. This work demonstrates the potential of CRISPR/vOECTs bioelectronics for sensitive epigenetic analysis and presents a promising proof-of-concept for future non-invasive screening strategies.

Relaxation dynamics of supercooled liquids approaching glassy arrest remain a central challenge in integrated electronic architectures, where conventional rheometry becomes incompatible. Here, we demonstrate that an ambipolar PdSe_{2} field-effect transistor functions as an electrical probe capable of resolving ion-specific relaxation dynamics in fragile ionic glass formers and semiquantitatively inferring rheological parameters within an operating device environment. Temperature evolution of the transfer curve hysteresis and time-resolved current transients under ionic-gate pulse reveal a non-Arrhenius fragile slowdown. We track the continuous reduction of dynamically equilibrated liquid regions approaching the glass transition through an electrically accessible quantity p_{eq}(T), quantifying the fraction of the mobile ions able to relax within the experimental timescale. Upon cooling, p_{eq} collapses sharply as mobile regions fragment into percolating fractal clusters, consistent with a reduction of configurational entropy predicted for fragile glass formers. This approach enables temperature-dependent scaling of viscosity and extraction of characteristic temperatures marking the ergodic-to-nonergodic crossover, within a solid-state device architecture where conventional rheological characterization is inapplicable. Further, polymer confinement of the ionic liquid shifts these characteristic temperatures upward, demonstrating the sensitivity of this method to structural constraints imposed by the polymer matrix.

Flexible graphene field-effect transistors (FGr-FETs) possess ultra-high detection sensitivity, excellent mechanical flexibility, and extensive interface signal transduction mechanisms, driving the innovative development of flexible electronics. However, the accurate extraction of signals in complex physiological scenarios remains a key factor limiting the advancement of FGr-FET. This review systematically examines the mechanisms underlying the sensing and transduction of electrophysiological, mechanical, and biochemical signals in FGr-FET. We summarize strategies for enhancing FGr-FET sensing performance, including material modification, structural optimization, and the amplification of optical and magnetic signals. This paper highlights the latest advances in multimodal sensing and precision medicine applications and analyzes key challenges related to Debye screening, device reliability, inter-device variability, long-term operational stability, and clinical translation. Finally, we discuss the potential application of artificial intelligence algorithms in the precise extraction of physiological signals from FGr-FETs. These reviews provide a theoretical foundation for the development of next-generation precision medicine platforms.

Organic electrochemical transistors (OECTs) can be optimized by tuning the gate-channel capacitance ratio via geometry or materials. However, electrolyte properties also critically influence performance, which is a phenomenon unexplained by capacitive models alone. This study demonstrates that varying electrolyte concentration modulates the on-state current (Ion), the off-state current (Ioff), the on-off ratio (Ion/Ioff), and the threshold voltage (VT). We propose a new impedance-based model incorporating both capacitive coupling and resistive voltage division across the electrolyte to explain these effects. This model successfully predicts and verifies a frequency-dependent VT shift under high-frequency signals. Leveraging this insight, we demonstrate that appropriately increasing the gate DC bias compensates for this shift, thereby enhancing transconductance in high-frequency applications.

Molecular orientation transition is a key determinant of charge transport efficiency in thin-film transistors (TFTs). Conventional strategies to induce such transitions often result in increased molecular disorder, which leads to device performance degradation. Herein, an organic-inorganic hybrid network was proposed to modulate the structure-performance relationship via the incorporation of specific inorganic metal oxides (e.g., In2O3), leading to a marked enhancement in device and sensing performance by nearly 20-fold. This phenomenon may be associated with a triple-condition mechanistic framework involving strong interfacial adsorption, thermodynamic driving force, and abundant oxygen vacancies. The proposed triple-condition mechanistic framework not only transforms molecular orientation from observation into a predictable rule for selecting inorganic additives in organic-inorganic hybrid networks, but also establishes a practical route for improving biosensing performance through molecular orientation in life and health monitoring.

Oxide semiconductors are considered one of the promising candidates to replace silicon as the channel material in ferroelectric field-effect transistors (FeFETs), owing to their compatibility with low-temperature complementary metal-oxide-semiconductor (CMOS) processing. However, intrinsic hole deficiency in oxide semiconductors limits complete ferroelectric polarization switching, and practical channel engineering strategies to overcome this limitation have rarely been demonstrated. This study demonstrates a depletion charge boosting method via in situ Ar+ plasma treatment during atomic layer deposition of the indium-gallium oxide (IGO) channel. Under the optimized plasma treatment condition (300 W for 60 s), plasma-induced ionized oxygen vacancies in the IGO front channel function as depletion charges that compensate for the negative bound charges in the zirconium-doped HfO2 (HZO) layer. The engineered IGO/HZO FeFETs exhibit an improved memory window of 1.612 V, potentiation/depression cycling up to 5120 pulses, and stable multi-level retention of 100 s for 64 conductance states. Furthermore, the multilayer perception-based artificial neural network simulations yield a recognition accuracy of approximately 89% after 125 training epochs. This depletion charge boosting technology provides a potential pathway for improving oxide channel-based FeFETs toward neuromorphic applications.

High-density organic electrochemical transistor (OECT) arrays are essential for neuromorphic computing and bioelectronic interfaces, but progress has been limited by the low resolution of electrolyte patterning. Although conventional photolithography offers high feature resolution, it involves a fundamental trade-off among spatial resolution, ionic capacitance, and stability in the electrolyte. Here we report an ion compensation-assisted photolithography (ICAP) strategy that yields electrolyte micro-patterns combining high precision, high capacitance and high stability. A molecularly engineered electrolyte forms, under UV exposure, a physicochemical dual cross-linked network with strong solvent resistance and hydrophobicity, which suppresses swelling during both aqueous development and the subsequent ion-compensation step, preserving pattern fidelity. Ion compensation then restores and enhances the mobile-ion content, increasing areal capacitance. The resulting electrolytes achieve a record 2 μm resolution, 15.6 μF cm-2 capacitance, and strong thermal stability from - 50 to 200 °C. Integrated into OECTs, the ICAP-patterned electrolytes suppress crosstalk by 97.6% and boost on/off ratios by 325%, reducing parasitic coupling by more than 40 times compared to unpatterned arrays. The method is compatible with p-type and n-type organic semiconductors and inorganic oxides, providing a versatile route to scalable neuromorphic circuits and advanced bioelectronics.

Carcinoembryonic antigen (CEA) is widely used for tumor monitoring, yet electrical biosensor readouts are commonly based on endpoint or steady-state signals and therefore discard information contained in the early transient response. We developed a learning-assisted readout for an existing AlGaN/GaN high-electron-mobility transistor (HEMT) aptasensor by integrating transient drain-source current sequences with four steady-state I-V descriptors in a bidirectional long short-term memory-multilayer perceptron (LSTM-MLP) model. Bias-voltage comparison identified V_DS = 0.5 V as the preferred operating point on the basis of response amplitude, noise, and signal-to-noise ratio. Eighteen independent response curves spanning six experimentally tested CEA concentrations were evaluated by grouped three-fold cross-validation at the original-curve level, yielding R2 = 0.998, RMSE(log10) = 0.078, and MAE(log10) = 0.062. Curve-level low-concentration measurements analyzed with a fixed decision threshold and logistic detection-probability model produced a model-assisted C95 of 3.20 pg/mL (95% bootstrap confidence interval: 2.83-3.52 pg/mL). Sequence-length analysis showed that 120 s was the earliest interval meeting prespecified performance-retention criteria relative to the complete 1000 s record. In 1:10 diluted pooled human serum, baseline-corrected recoveries were 97.6%, 98.0%, and 95.0% at 5, 500, and 50,000 pg/mL, respectively; comparison with 1:20 serum further demonstrated dilution-dependent matrix effects. These findings show that transient-response learning can extract quantitative information not fully used by steady-state analysis, while broader device-batch and clinical validation remains necessary.

Organic field-effect transistor (OFET)-based chemical sensors integrated with microfluidic systems are emerging as attractive platforms for real-time chemical monitoring. In these systems, microfluidics provides rapid analyte transport, low sample consumption, continuous solution exchange, and temporal control of interfacial processes. In this Perspective, we focus on two sensor configurations: extended-gate-type and water-gate-type OFET systems, which are classified according to device architecture and sensing interface rather than directly compared by figures of merit. This architecture-based viewpoint should provide a useful framework for the design of next-generation OFET-integrated microfluidic chemical sensors.