Advanced Electronic Materials最新文献

Oxide-Based Electrolyte-Gated Transistors

ZnO-based transistors have been fabricated using an innovative configuration that combines vertical architecture with electrolyte usage (see article number 2300562 by Douglas Henrique Vieira, Neri Alves, and co-workers). The diode counterpart unveils a current–voltage relationship arising from space-charge limited current, which undergoes continuous shift due to the field-effect promoted by the charge accumulation in the source's perforation, driven by its capacitor counterpart. Beyond the transport mechanism, the findings showcase excellence in current switching based on irradiance – a phenomenon analogous to the field-effect.

A study on a bias voltage-dependent weight update characteristics in a sub-threshold region of a low power synaptic pass-transistor (SPT) is presented with a Hf-doped zinc oxide active layer. The SPT is a synaptic thin-film transistor (TFT) in series with a load TFT which is used as a resistive load (R_L) to be scaled by a bias voltage (V_B). Here, when the V_B of the load TFT is modulated, the R_L can be changed. With the changed R_L, it is expected that the weight update characteristics (i.e., dynamic ratio) and electrical characteristics (i.e., power consumption) of the SPT are varied, respectively, suggesting a trade-off relation between the dynamic ratio and power consumption. To check these, the pulsed characteristics of the fabricated SPT is monitored for different V_B, respectively. From experimental results, as increasing V_B, it is found that the decreased R_L leads to the increase of the power consumption while enhancing the dynamic ratio because a full depression (FD) can be relatively easy. On the other hand, when the V_B is reduced, the R_L is increased resulting in the decrease of both the power dissipation and the dynamic ratio due to a difficulty of FD.

In the last 15 years memristors have been investigated as devices for high-density, low-power, non-volatile, resistive random access memory (ReRAM) beyond Moore's law. They also show potential in neuromorphic logic architectures to overcome the Von–Neumann bottleneck of classical circuitry facilitating better hardware for artificial intelligence (AI) and artificial neural network (ANN) systems. Molybdenum disulfide (MoS₂) has emerged as a promising material for memristor devices of monolayer thickness due to its direct bandgap, high carrier mobility and environmental stability. In this review, recent progress in the development of MoS₂ memristors the current understanding of the mechanisms behind their function are examined. The remaining obstacles to a commercially viable device principle and how these may be surmounted in light of the rapid progress that has already been made are also discussed.

When implementing optoelectronic devices through the stacking of heterogeneous materials, considering the bandgap offset is crucial for achieving efficient carrier dynamics. In this study, the bandgap offset characteristics are investigated when n-type gallium nitride nanowires (n-GaN NWs) are used as electron transport layers in methylammonium lead iodide (MAPbI₃)-based optoelectronic devices. n-GaN NWs are grown on indium-tin-oxide (ITO)-coated glass via the plasma-assisted molecular beam epitaxy (PA-MBE) process to form the “GaN NWs-on-glass” platform. A MAPbI₃ thin film is then spin-coated on the GaN NWs-on-glass. X-ray photoelectron spectroscopy (XPS) shows that the valence and conduction band offsets in the MAPbI₃/n-GaN heterostructure are 2.19 and 0.40 eV, respectively, indicating a type-II band alignment ideal for optoelectronic applications. Prototype photovoltaic devices stacking perovskite on GaN NWs-on-glass show excellent interfacial charge-transfer ability, photon recycling, and carrier extraction efficiency. As a pioneering step in exploiting the diverse potential of the GaN-on-glass, it is demonstrated that the junction characteristics of MAPbI₃/n-GaN NW heterostructures can lead to a variety of optoelectronic device applications.

The heterostructure of two-dimensional transition metal dichalcogenide (TMDC) has garnered extensive attention, for the junction is the building block of a semiconductor device. However, the controllable synthesis of TMDC heterostructures of different transition metals and different chalcogen elements is still challenging because of the etching by atom substitution during the chemical vapor deposition (CVD) process. Here, a Mo─O transition state with lower energy is introduced to the edge of an as-grown MoSe₂ by using ultraviolet ozone treatment, to prevent the fast atom substitution of S for Se, and enable a stable growth of WS₂/MoSe₂ lateral heterostructure. A polymer-free transfer method is developed based on capillary interaction, and atomic structure characterization confirms the high-quality WS₂/MoSe₂ lateral heterostructure. The WS₂/MoSe₂ lateral heterostructure photodetector exhibits superior photoresponse compared to WS₂ and MoSe₂ devices, with a responsivity of 21.87 A W⁻¹ and a detectivity of 4.2 × 10¹² Jones at 350 nm. Kelvin probe force microscopy result reveals that the built-in electric field within the heterojunction facilitates the effective separation of photogenerated electron-hole pairs. This study carries profound implications for the CVD growth and polymer-free transfer of TMDC heterostructures in photodetector applications.

Memristor, with the ability of analog computing, is widely investigated for improving the computing efficiency of deep neural networks (DNNs) deployment. However, how to fully take advantage of the analog computing ability of memristive computing system (MCS) for DNN deployment is still an open question. Here, a new neural network models deployment scheme, that is, an information dimension matching (IDM) scheme, is proposed to fully take advantage of the analog computing ability of MCS. Furthermore, the spatial and temporal DNN, that is convolutional neural network (CNN) and recurrent neural network (RNN) is used to verify the proposed deployment scheme, respectively. The experimental results indicate that, compared to the traditional deployment schemes, the proposed deployment scheme shows obvious inference accuracy and energy efficiency improvement (>4 × in four-layer DNNs deployment), and the energy efficiency improvement increases dramatically with the layers increment of DNNs. This work paves the path for developing high computing efficiency analog MCS.

This study presents a binarized neural network (BNN) comprising quasi-nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec⁻¹) and a high on/off ratio (≥ 10⁷). A pair of QNVM devices are used for a single synaptic cell in a cell array, in which its memory state represents the synaptic weight, and the voltages applied to the pair act as input in a complementary fashion. The array of synaptic cells performs matrix multiply-accumulate (MAC) operations between the weight matrix and input vector using XNOR and current summation. All the results of the MAC operations and vector-matrix multiplications are equivalent. Moreover, the BNN features a high accuracy of 93.32% in the MNIST image recognition simulation owing to high device uniformity (1.35%), which demonstrates the feasibility of compact and high-performance neuromorphic computing.

Memristors are a candidate device for artificial neural systems due to their excellent conductance-regulation ability and potential to simulate the characteristics of biological synapses. This study fabricated a Pt/TaO_x/TiO_y/Ti analog artificial synapse memristor that exhibits excellent multilevel storage property with a large on/off ratio of ≈660 times. The dynamic resistive switching mechanism is well expounded and validated by the reset stopping voltage dependent Schottky fitting results. Moreover, the essential biological synaptic characteristics such as long-term potentiation/depression (LTP/D) and paired-pulse facilitation (PPF) are successfully mimicked with a low pulse energy consumption of 12.69 nJ. A neuromorphic network constructed on the enhanced symmetry and linearity of conductance for this Pt/TaO_x/TiO_y/Ti memristive device can achieve 92.45% accuracy in recognizing handwritten pattern. These results demonstrate a significant potential for application Pt/TaO_x/TiO_y/Ti memristor in non-volatile memory and bioinspired neuromorphic systems.

The popularity of metal halide perovskites is in part the result of their versatility in numerous applications. To date, perovskites are used in their intrinsic, undoped form, as the doping of these materials is not yet adequately mastered. Herein, the recently reported electronic doping of CH₃NH₃PbI₃ is employed to fabricate perovskite solar cells in which the interfacial electron transport layer (ETL) is replaced by n-doping of one side of the perovskite film. The doping involves the incorporation of metastable Sm²⁺ ions that undergo an in situ oxidation to Sm³⁺, releasing electrons to the conduction band to render the perovskite n-type. In spite of the lack of an ETL, these solar cells have the same efficiency as the samples with the ETL. The open circuit voltage of the doped solar cells increases proportionally to the doping concentration due to the narrowing of the depletion layer thickness at the interface of the perovskite and the top electrode, reaching the value of ≈1 V for the doped ETL-free device, the same as for the reference sample. These proof-of-concept results represent the first step toward perovskite-based devices incorporating a p-n homojunction.