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Carbon nanotubes (CNTs) and indium-gallium-zinc oxide (IGZO) have emerged as important materials for p-type and n-type thin-film transistors (TFTs), respectively, due to their high mobility, flexibility, and low fabrication temperature. However, fabricating sophisticated macroelectronic circuits operating in complementary mode is challenging using only a single material, because implementing n-type CNT TFTs and p-type IGZO TFTs is difficult. Therefore, hybrid complementary circuits integrated with p-type CNT TFTs and n-type IGZO TFTs have been demonstrated to combine the strength of each TFT. However, limited efforts have been devoted to optimizing the circuit performance by tuning the process conditions under which the percolated CNT network channel and IGZO channel are formed. In particular, the densities of CNTs in the network channel and the amount of oxygen vacancies in the IGZO channel can be simply adjusted, which are important in determining the electrical properties of each TFT. In this work, we systematically investigated the device and circuit performance by varying such conditions; hence, we confirmed the design features of each TFT that can be optimized to enhance the hybrid complementary circuits.

In this study, we investigated how the structure and oxygen flow rate (OFR) during the sputter-deposition affects the photo-responses of amorphous indium-gallium-zinc-oxide (a-IGZO)-based photodetector devices. As the result of comparing three types of device structures with one another, which are a global Schottky diode, local Schottky diode, and thin-film transistor (TFT), the IGZO TFT with the gate pulse technique suppressing the persistent photoconductivity (PPC) is the most promising photodetector in terms of a high photo-sensitivity and uniform sensing characteristic. In order to analyze the IGZO TFT-based photodetectors more quantitatively, the time-evolution of sub-gap density-of-states (DOS) was directly observed under photo-illumination and consecutively during the PPC-compensating period with applying the gate pulse. It shows that the increased ionized oxygen vacancy (VO2+) defects under photo-illumination was fully recovered by the positive gate pulse and even overcompensated by additional electron trapping. Based on experimentally extracted sub-gap DOS, the origin on PPC was successfully decomposed into the hole trapping and the VO ionization. Although the VO ionization is enhanced in lower OFR (O-poor) device, the PPC becomes more severe in high OFR (O-rich) device because the hole trapping dominates the PPC in IGZO TFT under photo-illumination rather than the VO ionization and more abundant holes are trapped into gate insulator and/or interface in O-rich TFTs. Similarly, the electron trapping during the PPC-compensating period with applying the positive gate pulse becomes more prominent in O-rich TFTs. It is attributed to more hole/electron traps in the gate insulator and/or interface, which is associated with oxygen interstitials, or originates from the ion bombardment-related lower quality gate oxide in O-rich devices.

We report the effect of the oxygen content of the LaAlO3 layer on the synaptic behavior in the Pt/LaAlO3/Nb-doped SrTiO3 memristor for neuromorphic applications. As the oxygen-content decreases, the current becomes larger and the spike time-dependent plasticity (STDP) becomes less sensitive to the time difference between pre- and post-synaptic spike voltage. In addition, the conduction mechanism, which was found to be a combination of thermionic and Poole-Frenkel emissions, and the effect of oxygen content are explained in association with the oxygen vacancy in the LaAlO3 layer. The trade-off between large current and efficient STDP can be controlled by the oxygen content. Furthermore, the results of extracting the synaptic strength-based model parameters indicate that the Pt/LaAlO3/Nb-doped SrTiO3 shows the efficient STDP characteristics in comparison to previously reported memristor materials.

We propose a universal model for bias-stress (BS)-induced instability in the inkjet-printed carbon nanotube (CNT) networks used in field-effect transistors (FETs). By combining two experimental methods, i.e., a comparison between air and vacuum BS tests and interface trap extraction, BS instability is explained regardless of either the BS polarity or ambient condition, using a single platform constituted by four key factors: OH− adsorption/desorption followed by a change in carrier concentration, electron concentration in CNT channel corroborated with H2O/O2 molecules in ambient, charge trapping/detrapping, and interface trap generation. Under negative BS (NBS), the negative threshold voltage shift (ΔVT) is dominated by OH− desorption, which is followed by hole trapping in the interface and/or gate insulator. Under positive BS (PBS), the positive ΔVT is dominated by OH− adsorption, which is followed by electron trapping in the interface and/or gate insulator. This instability is compensated by interface trap extraction; PBS instability is slightly more complicated than NBS instability. Furthermore, our model is verified using device simulation, which gives insights on how much each mechanism contributes to BS instability. Our result is potentially useful for the design of highly stable CNT-based flexible circuits in the Internet of Things wearable healthcare era.

A physics-based compact model for phase-change random access memory (PcRAM) was proposed considering the ratio of vertical-to-lateral crystal growth rate (α), and it was incorporated into HSPICE via Verilog-A. The proposed model was verified using the experimental results taken from the 256 × 256 cross-point (X-point) PcRAM cell array with the Ge2Sb2Te5 20–22 nm ITRS technology node. The proposed compact model successfully reproduced the measured PcRAM cell resistance (RC) depending on the SET pulse width and amplitude after a background RESET, which is a challenging issue that holds back the X-point PcRAM as a promising candidate for a modern storage-class memory in perspective of the write latency and power consumption, without heavy computational burden while capturing the essence of physical meaning via the multidomain simulation which includes the threshold switching, electrical, thermal, and phase-change modules. The extracted α value was 1.55. Furthermore, it was found that the SET pulse-dependent abrupt/gradual change of RC is sensitive to α. This suggests that α should be carefully optimized for PCM-based neuromorphic applications

In this work, a SPICE compact model of indium-gallium-zinc oxide (IGZO) memristor in consideration of IGZO and electrode materials having non-quasi-statically updated Schottky barrier heights has been developed. In order for compact modeling of an analog memristor with higher accuracy, understanding of its switching characteristics and conduction behaviors needs to be preceded. It has been empirically revealed that they are dependent on metal species of the electrodes and processing approach. The switching characteristics are more weightedly determined by the interface between the switching layer and the metal with lower workfunction out of two electrode metals and interface status has been controlled by an Ar bombardment in this work. In order for identifying the conduction mechanism, a series of device simulations have been performed and the internal electric field distribution over the device structure has been closely investigated. It has been shown that the conduction behaviors are mainly determined by the thermionic emission taking place between Pd electrode and IGZO switching layer. For preparing the model parameters, along with the experimental results, transient measurement techniques have been cultivated at the same time, which has made possible to tell the difference between sets of model parameters obtained by theory and the techniques. In consequence, a highly reliable physics-based modeling for IGZO memristor has been developed through identification of switching and conduction mechanisms and extraction of the model parameters with the simultaneous help of Verilog-A equation build-up, which has demonstrated a plausible agreement with the measurement results.

In this study, we implement an artificial synapse and neuron in a single platform by combining a silicon nanowire (SiNW) ion-sensitive field-effect transistor (ISFET), an indium-gallium-zinc-oxide (IGZO) memristor, and a voltage-controlled oscillator (VCO). The chemical and electrical operations of the synapse are emulated using the pH sensor operation of the ISFET and long-term potentiation/short-term plasticity of the IGZO memristor, respectively. The concentration of hydrogen ions in an electrolyte is successfully transformed via a VCO-based neuron into modulation of synaptic strength, i.e., the current of the memristor. It mimics the strength of the synaptic connection modulated by the concentration of the neurotransmitter. Thus, the chemical-electrical signal conversion in chemical synapses is clearly demonstrated. Furthermore, the proposed artificial platform can discriminate the chemical synapse from the electrical synapse and the path of the neuro-signal propagation and that of memorization/update of synaptic strength. This can potentially provide a new insight into the principles of brain-inspired computing that can overcome the bottleneck of the state-of-the-art von-Neumann computing systems.