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.

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.

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.

Multi-receptor skin with highly sensitive tele-perception somatosensory.

The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured doping of inorganic nanoparticles to enhance the local electric field, coupled with advanced deep learning algorithms, achieving a ΔV/Δd sensitivity of 14.2, surpassing benchmarks. This enables precise remote control of surveillance systems and robotic manipulators. Our long short-term memory-based adaptive pulse identification achieves 99.56% accuracy in material identification with accelerated processing speeds. In addition, we demonstrate the feasibility of using a two-dimensional (2D) sensor matrix to integrate real object scan data into a convolutional neural network to accurately discriminate the shape and material of 3D objects. This promises transformative advances in human-computer interaction and neuromorphic computing.

Simulation of Novel Nano Low-Dimensional FETs at the Scaling Limit.

The scaling of bulk Si-based transistors has reached its limits, while novel architectures such as FinFETs and GAAFETs face challenges in sub-10 nm nodes due to complex fabrication processes and severe drain-induced barrier lowering (DIBL) effects. An effective strategy to avoid short-channel effects (SCEs) is the integration of low-dimensional materials into novel device architectures, leveraging the coupling between multiple gates to achieve efficient electrostatic control of the channel. We employed TCAD simulations to model multi-gate FETs based on various dimensional systems and comprehensively investigated electric fields, potentials, current densities, and electron densities within the devices. Through continuous parameter scaling and extracting the sub-threshold swing (SS) and DIBL from the electrical outputs, we offered optimal MoS2 layer numbers and single-walled carbon nanotube (SWCNT) diameters, as well as designed structures for multi-gate FETs based on monolayer MoS2, identifying dual-gate transistors as suitable for high-speed switching applications. Comparing the switching performance of two device types at the same node revealed CNT's advantages as a channel material in mitigating SCEs at sub-3 nm nodes. We validated the performance enhancement of 2D materials in the novel device architecture and reduced the complexity of the related experimental processes. Consequently, our research provides crucial insights for designing next-generation high-performance transistors based on low-dimensional materials at the scaling limit.

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.

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.