Multibarrier Collaborative Modulation Devices with Ultra-High Logic Operation Density.

The demands for highly miniaturized and multifunctional electronics are rapidly increasing. As scaling-down processes of transistors are restricted by physical limits, reconfigurable electronics with switchable operation functions for different tasks are developed for higher function integration based on split- or vertical-dual-gate structures. To promote the present reconfigurable electronics and exceed the function integration limit, the critical issue is to integrate complex operations into simple circuit forms by establishing more control dimensions. This work proposes a multibarrier collaborative (MBC) modulation architecture to increase the control dimension by multiple forms of potential barriers and achieves combinational and reconfigurable logic operations by a single MBC device. The MBC architecture exhibits ultrahigh logic operation density, including 58.8% area reduction for multiplexer operations and 71.4% area reduction for 4-logic reconfigurable operations. Besides, a hardware security module composed of 4 MBC devices implementing 8 types of logic operations is demonstrated. This work reveals an effective design of function integration for next-generation electronics.

Two-dimensional fully ferroelectric-gated hybrid computing-in-memory hardware for high-precision and energy-efficient dynamic tracking.

Computing in memory (CIM) breaks the conventional von Neumann bottleneck through in situ processing. Monolithic integration of digital and analog CIM hardware, ensuring both high precision and energy efficiency, provides a sustainable paradigm for increasingly sophisticated artificial intelligence (AI) applications but remains challenging. Here, we propose a complementary metal-oxide semiconductor-compatible ferroelectric hybrid CIM platform that consists of Boolean logic and triggers for digital processing and multistage cell arrays for analog computation. The basic ferroelectric-gated units are assembled with solution-processable two-dimensional (2D) molybdenum disulfide atomic-thin channels at a wafer-scale yield of 96.36%, delivering high on/off ratios (>107), high endurance (>1012), long retention time (>10 years), and ultralow cycle-to-cycle/device-to-device variations (~0.3%/~0.5%). Last, we customize a highly compact 2D hybrid CIM system for dynamic tracking, achieving a high accuracy of 99.8% and a 263-fold improvement in power efficiency compared to graphics processing units. These results demonstrate the potential of 2D fully ferroelectric-gated hybrid hardware for developing versatile CIM blocks for AI tasks.

Recent Progress in Biosensors for Depression Monitoring-Advancing Personalized Treatment.

Depression is currently a major contributor to unnatural deaths and the healthcare burden globally, and a patient's battle with depression is often a long one. Because the causes, symptoms, and effects of medications are complex and highly individualized, early identification and personalized treatment of depression are key to improving treatment outcomes. The development of wearable electronics, machine learning, and other technologies in recent years has provided more possibilities for the realization of this goal. Conducting regular monitoring through biosensing technology allows for a more comprehensive and objective analysis than previous self-evaluations. This includes identifying depressive episodes, distinguishing somatization symptoms, analyzing etiology, and evaluating the effectiveness of treatment programs. This review summarizes recent research on biosensing technologies for depression. Special attention is given to technologies that can be portable or wearable, with the potential to enable patient use outside of the hospital, for long periods.

Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition.

Gesture sensors are essential to collect human movements for human-computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core-shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

Reconfigurable aJ-Level Ferroelectric Transistor-Based Boolean Logic for Logic-in-Memory.

Logic-in-memory (LIM) architecture holds great potential to break the von Neumann bottleneck. Despite the extensive research on novel devices, challenges persist in developing suitable engineering building blocks for such designs. Herein, we propose a reconfigurable strategy for efficient implementation of Boolean logics based on a hafnium oxide-based ferroelectric field effect transistor (HfO2-based FeFET). The logic results are stored within the device itself (in situ) during the computation process, featuring the key characteristics of LIM. The fast switching speed and low power consumption of a HfO2-based FeFET enable the execution of Boolean logics with an ultralow energy of lower than 8 attojoule (aJ). This represents a significant milestone in achieving aJ-level computing energy consumption. Furthermore, the system demonstrates exceptional reliability with computing endurance exceeding 108 cycles and retention properties exceeding 1000 s. These results highlight the remarkable potential of a FeFET for the realization of high performance beyond the von Neumann LIM computing architectures.

Performance Limits and Advancements in Single 2D Transition Metal Dichalcogenide Transistor.

Two-dimensional (2D) transition metal dichalcogenides (TMDs) allow for atomic-scale manipulation, challenging the conventional limitations of semiconductor materials. This capability may overcome the short-channel effect, sparking significant advancements in electronic devices that utilize 2D TMDs. Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance. This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor. It delves into the impacts of miniaturization, including the reduction of channel length, gate length, source/drain contact length, and dielectric thickness on transistor operation and performance. In addition, this review provides a detailed analysis of performance parameters such as source/drain contact resistance, subthreshold swing, hysteresis loop, carrier mobility, on/off ratio, and the development of p-type and single logic transistors. This review details the two logical expressions of the single 2D-TMD logic transistor, including current and voltage. It also emphasizes the role of 2D TMD-based transistors as memory devices, focusing on enhancing memory operation speed, endurance, data retention, and extinction ratio, as well as reducing energy consumption in memory devices functioning as artificial synapses. This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices. This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications. It underscores the anticipated challenges, opportunities, and potential solutions in navigating the dimension and performance boundaries of 2D transistors.

Designs and Applications for the Multimodal Flexible Hybrid Epidermal Electronic Systems.

Research on the flexible hybrid epidermal electronic system (FHEES) has attracted considerable attention due to its potential applications in human-machine interaction and healthcare. Through material and structural innovations, FHEES combines the advantages of traditional stiff electronic devices and flexible electronic technology, enabling it to be worn conformally on the skin while retaining complex system functionality. FHEESs use multimodal sensing to enhance the identification accuracy of the wearer's motion modes, intentions, or health status, thus realizing more comprehensive physiological signal acquisition. However, the heterogeneous integration of soft and stiff components makes balancing comfort and performance in designing and implementing multimodal FHEESs challenging. Herein, multimodal FHEESs are first introduced in 2 types based on their different system structure: all-in-one and assembled, reflecting totally different heterogeneous integration strategies. Characteristics and the key design issues (such as interconnect design, interface strategy, substrate selection, etc.) of the 2 multimodal FHEESs are emphasized. Besides, the applications and advantages of the 2 multimodal FHEESs in recent research have been presented, with a focus on the control and medical fields. Finally, the prospects and challenges of the multimodal FHEES are discussed.

All-Solution-Processed Electronics with Sub-Microscale Resolution and Nanoscale Fidelity Fabricated Via a Humidity-Controlled, Surface Energy-Directed Assembly Process.

Solution-based processes have received considerable attention in the fabrication of electronics and sensors owing to their merits of being low-cost, vacuum-free, and simple in equipment. However, the current solution-based processes either lack patterning capability or have low resolution (tens of micrometers) and low pattern fidelity in terms of line edge roughness (LER, several micrometers). Here, we present a surface energy-directed assembly (SEDA) process to fabricate metal oxide patterns with up to 2 orders of magnitude improvement in resolution (800 nm) and LER (16 nm). Experiment results show that high pattern fidelity can be achieved only at low relative humidities of below 30%. The reason for this phenomenon lies in negligible water condensation on the solution droplet. Employing the SEDA process, all-solution-processed metal oxide thin film transistors (TFTs) are fabricated by using indium oxide as channel layers, indium tin oxide as source/drain electrodes and gate electrodes, and aluminum oxide as gate dielectrics. TFT-based logic gate circuits, including NOT, NOR, NAND, and AND are fabricated as well, demonstrating the applicability of the SEDA process in fabricating large area functional electronics.

A Large-Scale and Low-Cost Thermoacoustic Loudspeaker Based on Three-Dimensional Graphene Foam.

Graphene is a promising material for thermoacoustic sources due to its extremely low heat capacity per unit area and high thermal conductivity. However, current graphene thermoacoustic devices have limited device area and relatively high cost, which limit their applications of daily use. Here, we adopt a dip-coating method to fabricate a large-scale and cost-effective graphene sound source. This sound source has the three-dimensional (3D) porous structure that can increase the contact area between graphene and air, thus assisting heat to release into the air. In this method, polyurethane (PU) is used as a support, and graphene nanoplates are attached onto the PU skeleton so that a highly flexible graphene foam (GrF) device is obtained. At a measuring distance of 1 mm, it can emit sound at up to 70 dB under the normalized input power of 1 W. Considering its unique porous structure, we establish a thermoacoustic analysis model to simulate the acoustic performance of GrF. Furthermore, the obtained GrF can be made up to 44 in. (100 cm × 50 cm) in size, and it has good flexibility and processability, which broadens the application fields of GrF loudspeakers. It can be attached to the surfaces of objects with different shapes, making it suitable to be used as a large-area speaker in automobiles, houses, and other application scenarios, such as neck mounted speaker. In addition, it can also be widely used as a fully flexible in-ear earphone.

Laser-Induced Skin-like Flexible Pressure Sensor for Artificial Intelligence Speech Recognition.

Skin-like flexible pressure sensors with good sensing performance have great application potential, but their development is limited owing to the need for multistep, high-cost, and low-efficiency preparation processes. Herein, a simple, low-cost, and efficient laser-induced forming process is proposed for the first time to prepare a skin-like flexible piezoresistive sensor. In the laser-induced forming process, based on the photothermal effect of graphene and the foaming effect of glucose, a skin-like polydimethylsiloxanes (PDMS) film with porous structures and surface protrusions is obtained by using infrared laser irradiation of the glucose/graphene/PDMS prepolymer film. Further, based on the skin-like PDMS film with a graphene conductive layer, a new skin-like flexible piezoresistive sensor is obtained. Due to the stress concentration caused by the surface protrusions and the low stiffness caused by the porous structures, the flexible piezoresistive sensor realizes an ultrahigh sensitivity of 1348 kPa-1 at 0-2 kPa, a wide range of 200 kPa, a fast response/recovery time of 52 ms/35 ms, and good stability over 5000 cycles. The application of the sensor to the detection of human pulses and robot clamping force indicates its potential for health monitoring and soft robots. Furthermore, in combination with the neural network (CNN) algorithm in artificial intelligence technology, the sensor achieves 95% accuracy in speech recognition, which demonstrates its great potential for intelligent wearable electronics. Especially, the laser-induced forming process is expected to facilitate the efficient, large-scale preparation of flexible devices with multilevel structures.

The Roadmap of 2D Materials and Devices Toward Chips.

Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

Programmable Ferroelectricity in Hf0.5Zr0.5O2 Enabled by Oxygen Defect Engineering.

Ferroelectricity, especially the Si-compatible type recently observed in hafnia-based materials, is technologically useful for modern memory and logic applications, but it is challenging to differentiate intrinsic ferroelectric polarization from the polar phase and oxygen vacancy. Here, we report electrically controllable ferroelectricity in a Hf0.5Zr0.5O2-based heterostructure with Sr-doped LaMnO3, a mixed ionic-electronic conductor, as an electrode. Electrically reversible extraction and insertion of an oxygen vacancy into Hf0.5Zr0.5O2 are macroscopically characterized and atomically imaged in situ. Utilizing this reversible process, we achieved multilevel polarization states modulated by the electric field. Our study demonstrates the usefulness of the mixed conductor to repair, create, manipulate, and utilize advanced ferroelectric functionality. Furthermore, the programmed ferroelectric heterostructures with Si-compatible doped hafnia are desirable for the development of future ferroelectric electronics.

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing.

Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today's era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT's highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT's ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

Graphene-based Two-Stage Enhancement Pressure Sensor for Subtle Mechanical Force Monitoring.

The development of pressure sensors with high sensitivity and a low detection limit for subtle mechanical force monitoring and the understanding of the sensing mechanism behind subtle mechanical force monitoring are of great significance for intelligent technology. Here, we proposed a graphene-based two-stage enhancement pressure sensor (GTEPS), and we analyzed the difference between subtle mechanical force monitoring and conventional mechanical force monitoring. The GTEPS exhibited a high sensitivity of 62.2 kPa-1 and a low detection limit of 0.1 Pa. Leveraging its excellent performance, the GTEPS was successfully applied in various subtle mechanical force monitoring applications, including acoustic wave detection, voice-print recognition, and pulse wave monitoring. In acoustic wave detection, the GTEPS achieved a 100% recognition accuracy for six words. In voiceprint recognition, the sensor exhibited accurate identification of distinct voiceprints among individuals. Furthermore, in pulse wave monitoring, GTEPS demonstrated effective detection of pulse waves. By combination of the pulse wave signals with electrocardiogram (ECG) signals, it enabled the assessment of blood pressure. These results demonstrate the excellent performance of GTEPS and highlight its great potential for subtle mechanical force monitoring and its various applications. The current results indicate that GTEPS shows great potential for applications in subtle mechanical force monitoring.

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing.

Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the "Boltzmann tyranny", which restricts SS to 60 mV decade-1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade-1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from -7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.

Landauer-QFLPS Model for Mixed Schottky-Ohmic Contact Two-Dimensional Transistors.

Two-dimensional material-based field-effect transistors (2DM-FETs) are playing a revolutionary role in electronic devices. However, before electronic design automation (EDA) for 2DM-FETs can be achieved, it remains necessary to determine how to incorporate contact transports into model. Reported methods compromise between physical intelligibility and model compactness due to the heterojunction nature. To address this, quasi-Fermi-level phase space theory (QFLPS) is generalized to incorporate contact transports using the Landauer formula. It turns out that the Landauer-QFLPS model effectively overcomes the issue of concern. The proposed new formula can describe 2DM-FETs with Schottky or Ohmic contacts with superior accuracy and efficiency over previous methods, especially when describing non-monotonic drain conductance characteristics. A three-bit threshold inverter quantizer (TIQ) circuit is fabricated using ambipolar black phosphorus and it is demonstrated that the model accurately predicts circuit performance. The model could be very effective and valuable in the development of 2DM-FET-based integrated circuits.

Macroscopic negative differential thermal resistance in the overlapping graphene homojunction structure.

As one of the most potential ways to manipulate heat, thermal functional devices have achieved several breakthroughs in recent years, but are still limited to theoretical simulations. One of its theoretical bases is the existence of the negative differential thermal resistance (NDTR). However, most of the existing systems where the phenomenon of NDTR is found are atomic-level systems. In order to realize the macroscopic NDTR and provide effective theoretical guidance and support for the practical realization of thermal functional devices, we construct the overlapping graphene homojunction model, using the negative thermal expansion property of graphene to modify the overlapping area, and thus regulating the heat flow. The COMSOL-MATLAB co-simulation is used to perform calculations through negative feedback loops. It is found that the NDTR phenomenon exists under certain parameter conditions, which can provide new ideas and bring more opportunities for the experimental realization of nonlinear thermal functional devices.

Straight Manipulation Annealing in a Solvent Atmosphere for Quality-Improved Cs2AgBiBr6 Perovskites.

As a new-generation photoelectric material, perovskites have attracted researchers' attention due to their excellent optoelectronic properties. However, the existence of defects inevitably causes structural degradation and restricts their performance, which need to be further improved by post-treatment. At present, post-treatments mostly focus on non-contact treatments, which may constrain the effect since the influence on the perovskites caused by the direct contact is much more straightly. Therefore, we proposed an annealing strategy of straight manipulation in a solvent atmosphere with the assistance of polyimide (PI) tape for the perovskite post-treatment, due to the high heat resistance and less glue residual of this tape. It casts an influence on the perovskite directly, proving the possibility of the straight manipulation by operators, promoting the recrystallization of the perovskite grains and removing the impurity substance. The optimized Pb-free perovskite film exhibits a better X-ray sensitivity of 7.5 × 104 µC Gyair-1 cm-2 and a great detection limit of 47 nGyair s-1, which is comparable to advanced Pb-based perovskite X-ray detectors and all commercial ones. The new annealing strategy provides a facile, effective, and simple method to improve the perovskite quality, exhibiting the potential and harmlessness of the direct contact post-treatment, which paves the way for a broader application of perovskites.

Polarized Tunneling Transistor for Ultrafast Memory.

In today's information age, high performance nonvolatile memory devices have become extremely important. Despite their potential, existing devices suffer from limitations, such as low operation speed, low memory capacity, short retention time, and a complex preparation process. To overcome these limitations, advanced memory designs are required to improve speed, memory capacity, and retention time and reduce the number of preparation steps. Here, we present a nonvolatile floating-gate-like memory device based on a transistor that uses the polarization effect of ferroelectric material PZT (Pb[Zr0.2Ti0.8]O3) for regulating tunneling electrons for charging and discharging the MoS2 channel layer. The transistor is defined as a polarized tunneling transistor (PTT) and does not require a tunnel layer or a floating-gate layer. The PTT demonstrates an ultrafast programming/erasing speed of 25/20 ns and a response time of 120/105 ns, which is comparable to the ultrafast flash memories based on van der Waals heterostructures. Additionally, the PTT has a high extinction ratio of 104, a long retention time of 10 years, and a simple fabrication process. Our research provides future guidelines for the development of the next generation of ultrafast nonvolatile memory devices.