Asal Kiazadeh

In this work, fully patterned zinc tin oxide (ZTO) memristors are introduced using inkjet printing. By targeting a scalable, solution-based fabrication approach, highly stable devices with excellent reproducibility and minimal variability are achieved, using ZTO as the active layer, silver (Ag) as the top electrode, and molybdenum as the bottom electrode. The use of sustainable materials like ZTO enhances scalability and environmental compatibility, paving the way for next-generation, low-power neuromorphic computing. The devices successfully fulfill the fundamental criteria for in materia implementation of physical reservoir computing (PRC), including nonlinearity and fading memory property. The devices are successfully trained for classification tasks with MNIST handwritten dataset, achieving 89.4% accuracy and 86.5% by processing 4-bit and 5-bit input temporal sequences. The integration of printed memristors into hardware-based PRC architecture simplifies training complexity, making them particularly advantageous for energy-efficient, wearable AI systems.

For reconfigurable radios where the signals can be easily routed from one band to another band, new radio frequency switches (RF) are a fundament. The main factor driving the power consumption of the reconfigurable intelligent system (RIS) is the need for an intermediate device with static power consumption to maintain a certain surface configuration state. Since power usage scales quadratically with the RIS area, there is a relevant interest in mitigating this drawback so that this technology can be applied to everyday objects without needing such a high intrinsic power consumption. Current switch technologies such as PIN diodes, and field effect transistors (FETs) are volatile electronic devices, resulting in high static power. In addition, dynamic power dissipation related to switching event is also considerable. Regarding energy efficiency, non-volatile radio frequency resistive switch (RFRS) concept may be better alternative solution due to several advantages: smaller area, zero-hold voltage, lower actuation bias for operation, short switching time, scalability and capable to be fabricated in the backend-of-line of standard CMOS process.

Solution-based memristors deposited by inkjet printing technique have a strong technological potential based on their scalability, low cost, environmentally friendlier processing by being an efficient technique with minimal material waste. Indium-gallium-zinc oxide (IGZO), an oxide semiconductor material, shows promising resistive switching properties. In this work, a printed Ag/IGZO/ITO memristor has been fabricated. The IGZO thickness influences both memory window and switching voltage of the devices. The devices show both volatile counter8wise (c8w) and non-volatile 8wise (8w) switching at low operating voltage. The 8w switching has a SET and RESET voltage lower than 2 V and − 5 V, respectively, a retention up to 105 s and a memory window up to 100, whereas the c8w switching shows volatile characteristics with a low threshold voltage (Vth < − 0.65 V) and a characteristic time (τ) of 0.75 ± 0.12 ms when a single pulse of − 0.65 V with width of 0.1 ms is applied. The characteristic time alters depending on the number of pulses. These volatile characteristics allowed them to be tested on different 4-bit pulse sequences, as an initial proof of concept for temporal signal processing applications.

In this work, both analogue and digital depletion-mode single channel transistor circuits are presented and are simulated using an n-channel IGZO technology with VTH = -0.87V. A logic family is introduced, suppressing the need for an additional voltage level and level restoring circuitry. Furthermore, in the analogue domain, a depletion current mirror topology is presented with demonstrated small current error. Finally, the current mirror is used in the design of an OpAmp, achieving a simulated open-loop gain of 45 dB, CMRR of 58 dB, unity-gain frequency of 444 kHz and a phase margin of 71 degrees.

This paper presents a pulse amplitude modulated signal generator to address inference in Memristor based Neural Network Accelerators. As a part of this system, a novel 8-bit capacitive segmented Digital to Analog Converter (DAC) using amorphous Indium Galium Zinc Oxide (a-IGZO) thin-film transistor (TFT) technology has been designed. The DAC employs 50% segmentation with binary coded least significant bits (LSBs) and unary coded most significant bits (MSBs). This circuit has shown an ENOB of 7.3 bits at a sampling frequency of 100 kHz and an input frequency of 50 kHz. The worst case INL and DNL were recorded as 0.047 LSB and 0.34 LSB, respectively. With a power supply voltage of 5 V for the operational amplifier and 3V as the DAC reference voltage, the power consumption of the complete DAC was around 1.25 mW. This circuit can find potential applications in different flexible electronics systems.

This study explores the resistive switching phenomena present in 4 μm2 amorphous Indium-Gallium-Zinc Oxide (IGZO) memristors. Despite being extensively reported in the literature, not many studies detail the mechanisms that dominate conduction on the different states of IGZO-based devices. In this article, we demonstrate that resistive switching occurs due to the modulation of the Schottky barrier present at the bottom interface of the device. Furthermore, thermionic field emission and field emission regimes are identified as the dominant conduction mechanisms at the high resistive state of the device, while the bulk-limited ohmic conduction is found at the low resistive state. Due to the high complexity associated with creating compact models of resistive switching, a data-driven model is drafted taking systematic steps.

Solution-based memristors are emergent devices, due to their potential in electrical performance for neuromorphic computing combined with simple and cheap fabrication processes. However, to achieve practical application in crossbar design tens to hundreds of uniform memristors are required. Regarding this, the production step optimization should be considered as the main objective to achieve high performance devices. In this work, solution-based indium gallium zinc oxide (IGZO) memristor devices are produced using a combustion synthesis process. The performance of the device is optimized by using different annealing temperatures and active layer thicknesses to reach a higher reproducibility and stability. All IGZO memristors show a low operating voltage, good endurance, and retention up to 105 s under air conditions. The optimized devices can be programmed in a multi-level cell operation mode, with 8 different resistive states. Also, preliminary results reveal synaptic behavior by replicating the plasticity of a synaptic junction through potentiation and depression; this is a significant step towards low-cost processes and large-scale compatibility of neuromorphic computing systems. This journal is

Memristor crossbar arrays can compose the efficient hardware for artificial intelligent applications. However, the requirements for a linear and symmetric synaptic weight update and low cycle-to-cycle (C2C) and device-to-device variability as well as the sneak-path current issue have been delaying its further development. This study reports on a thin-film amorphous oxide-based 4×4 1-transistor 1-memristor (1T1M) crossbar. The a-IGZO crossbar is built on a flexible polyimide substrate, enabling IoT and wearable applications. In the novel framework, the thin-film transistor and memristor are fabricated at the same level, with the same processing steps and sharing the same materials for all layers. The 1T1M cells show linear and symmetrical plasticity characteristic with low C2C variability. The memristor performs like an analog dot product engine and vector–matrix multiplications in the 4×4 crossbars is demonstrated experimentally, in which the sneak-path current issue is successfully suppressed, resulting in a proof-of-concept for a cost-effective, flexible artificial neural networks hardware.