Two-dimensional materials have garnered interest from the perspectives of physics, materials, and applied electronics owing to their outstanding physical and chemical properties. Advances in exfoliation and synthesis technologies have enabled preparation and electrical characterization of various atomically thin films of semiconductor transition metal dichalcogenides (TMDs). Their two-dimensional structures and electromagnetic spectra coupled to bandgaps in the visible region indicate their suitability for digital electronics and optoelectronics. To further expand the potential applications of these two-dimensional semiconductor materials, technologies capable of precisely controlling the electrical properties of the material are essential. Doping has been traditionally used to effectively change the electrical and electronic properties of materials through relatively simple processes. To change the electrical properties, substances that can donate or remove electrons are added. Doping of atomically thin two-dimensional semiconductor materials is similar to that used for silicon but has a slightly different mechanism. Three main methods with different characteristics and slightly different principles are generally used. This review presents an overview of various advanced doping techniques based on the substitutional, chemical, and charge transfer molecular doping strategies of graphene and TMDs, which are the representative 2D semiconductor materials.

The recent reports of various photodetectors based on molybdenum disulfide (MoS₂) field effect transistors showed that it was difficult to obtain optoelectronic performances in the broad detection range [visible–infrared (IR)] applicable to various fields. Here, by forming a mono-/multi-layer nano-bridge multi-heterojunction structure (more than > 300 junctions with 25 nm intervals) through the selective layer control of multi-layer MoS₂, a photodetector with ultrasensitive optoelectronic performances in a broad spectral range (photoresponsivity of 2.67 × 10⁶ A/W at λ = 520 nm and 1.65 × 10⁴ A/W at λ = 1064 nm) superior to the previously reported MoS₂-based photodetectors could be successfully fabricated. The nano-bridge multi-heterojunction is believed to be an important device technology that can be applied to broadband light sensing, highly sensitive fluorescence imaging, ultrasensitive biomedical diagnostics, and ultrafast optoelectronic integrated circuits through the formation of a nanoscale serial multi-heterojunction, just by adding a selective layer control process.

Nanocomposites are gaining high demand for the development of next-generation energy storage devices because of their eco-friendly and cost-effective natures. However, their short-term energy retainability and marginal stability are regarded as hindrances to overcome. In this work, we demonstrate a high-performance supercapacitor fabricated by biocarbon-based MoS₂ (Bio-C/MoS₂) nanoparticles synthesized by a facile hydrothermal approach using date fruits. Here, we report the high specific capacitance for a carbon-based nanocomposite employing the pyrolysis technique of converting agricultural biowaste into a highly affordable energy resource. The biocompatible Bio-C/MoS₂ nanospheres exhibited a high capacitance of 945 F g^-1 at a current density of 0.5 A g^-1 and an excellent reproducing stability of 92% after 10000 charge/discharge cycles. In addition, the Bio-C/MoS₂ NS showed an exceptional power density of 3800-8000 W kg^-1 and an energy density of 74.9-157 Wh kg^-1. The results would pave a new strategy for design of eco-friendly materials toward the high-performance energy storage technology.

Here, various synaptic functions and neural network simulation based pattern-recognition using novel, solution-processed organic memtransistors (memTs) with an unconventional redox-gating mechanism are demonstrated. Our synaptic memT device using conjugated polymer thin-film and redox-active solid electrolyte as the gate dielectric can be routinely operated at gate voltages (V_GS) below − 1.5 V, subthreshold-swings (S) smaller than 120 mV/dec, and ON/OFF current ratio larger than 10⁸. Large hysteresis in transfer curves depicts the signature of non-volatile resistive switching (RS) property with ON/OFF ratio as high as 10⁵. In addition, our memT device also shows many synaptic functions, including the availability of many conducting-states (> 500) that are used for efficient pattern recognition using the simplest neural network simulation model with training and test accuracy higher than 90%. Overall, the presented approach opens a new and promising way to fabricate high-performance artificial synapses and their arrays for the implementation of hardware-oriented neural network.

Neuromorphic computing offers parallel data processing and low energy consumption and can be useful to replace conventional von Neumann computing. Memristors are two-terminal devices with varying conductance that can be used as synaptic arrays in hardware-based neuromorphic devices. In this research, we extensively investigate the analog symmetric multi-level switching characteristics of zinc tin oxide (ZTO)-based memristor devices for neuromorphic systems. A ZTO semiconductor layer is introduced between a complementary metal-oxide-semiconductor (CMOS) compatible Ni top electrode and a highly doped poly-Si bottom electrode. A variety of bio-realistic synaptic features are demonstrated, including long-term potentiation (LTP), long-term depression (LTD), and spike timing-dependent plasticity (STDP). The Ni/ZTO/Si device in which the adjustment of the number of states in conductance is realized by applying different pulse schemes is highly suitable for hardware-based neuromorphic applications. We evaluate the pattern recognition accuracy by implementing a system-level neural network simulation with ZTO-based memristor synapses. The density of states (DOS) and charge density plots reveal that oxygen vacancies in ZTO assist in generating resistive switching in the Ni/ZTO/Si device. The proposed ZTO-based memristor composed of metal-insulator-semiconductor (MIS) structure is expected to contribute to future neuromorphic applications through further studies.

In this work, we fabricated a dual gate positive feedback field-effect transistor (FBFET) integrated with CMOS. We investigated the DC and transient characteristics of the FBFET. The fabricated FBFET has an extremely low sub-threshold slope of less than 2.3 mV/dec and low off-current. We also propose an analog integrated-and-fire neuron circuit incorporating a FBFET, which significantly reduces the power dissipation of hardware neural networks. In a conventional neuron circuit using a membrane capacitor to integrate input pulses, most of the energy is consumed by the first inverter stage connected to the capacitor. Since the membrane capacitor is charged slowly compared to digital logic, a large amount of short-circuit current flows between V_dd and ground in the first inverter during this period. In the proposed neuron circuit, the short-circuit current is significantly suppressed by adopting a FBFET in the inverter. Through TCAD mixed mode simulation of the device and the circuit, we compare the energy consumption of a conventional and the proposed neuron circuits. In a single neuron circuit with microsecond duration pulses, 58% of the energy consumption is reduced by incorporating a FBFET. We performed SPICE compact modeling of FBFET, and its parameters were fitted to match the measurement results of the fabricated FBFET. Then, we conducted a circuit simulation to verify the operating neural networks. We implemented a single layer spiking neural network (SNN) that had resistive synaptic devices. In the SNN simulation, approximately 94% of the average power consumption of all output neurons was reduced.