Enhanced Computational Study with Experimental Correlation on I-V Characteristics of Tantalum Oxide (TaOx) Memristor Devices in a 1T1R Configuration.

Memristors, non-volatile switching memory platform, has recently attracted significant interest, offering unique potential to enable the realization of human brain-like neuromorphic computing efficiency. Memristors also demonstrate excellent temperature tolerance, long-term durability, and high tunability with nanosecond pulses, making them highly attractive for neuromorphic computing applications. To better understand the material processing, microstructure, and property relationship of switching mechanisms in memristor devices, computational methodologies, and tools are developed to predict the I-V characteristics of memristor devices based on tantalum oxide (TaOx) resistive random-access memory (ReRAM) integrated with an n-channel metal-oxide-semiconductor (NMOS) transistor. A multiphysics model based on coupled partial differential equations for electrical and thermal transport phenomena is solved for the high- and low-resistance states during the formation, growth, and destruction of a conducting filament through SET and RESET stages. These stages effectively represent the migration of oxygen vacancies within an oxide exchange layer. A series of parametric studies and energy minimization calculations are conducted to determine probable ranges for key material and model parameters accounting for the experimental data. The computational model successfully predicted the measured I-V curves across various gate voltages applied to the NMOS transistor in the one transistor one resistance (1T1R) configuration.

Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning.

Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of analog hardware depends on RRAM switching properties including the number of discrete conductance states and conductance variability. Furthermore, the overall power consumption of the system inversely correlates with the RRAM devices conductance. To study material dependence of these properties, TaOx and HfOx RRAM devices in one-transistor one-RRAM configuration (1T1R) were fabricated using a custom 65 nm CMOS fabrication process. Analog switching performance was studied with a range of initial forming compliance current (200-500 µA) and analog switching tests with ultra-short pulse width (300 ps) was carried out. We report that by utilizing low current during electroforming and high compliance current during analog switching, a large number of RRAM conductance states can be achieved while maintaining low conductance state. While both TaOx and HfOx could be switched to more than 20 distinct states, TaOx devices exhibited 10× lower conductance, which reduces total power consumption for array-level operations. Furthermore, we adopted an analog, fully in-memory training algorithm for system-level training accuracy benchmarking and showed that implementing TaOx 1T1R cells could yield an accuracy of up to 96.4% compared to 97% for the floating-point arithmetic baseline, while implementing HfOx devices would yield a maximum accuracy of 90.5%. Our experimental work and benchmarking approach paves the path for future materials engineering in analog-AI hardware for a low-power environment training.

Disposable photonics for cost-effective clinical bioassays: application to COVID-19 antibody testing.

Decades of research have shown that biosensors using photonic circuits fabricated using CMOS processes can be highly sensitive, selective, and quantitative. Unfortunately, the cost of these sensors combined with the complexity of sample handling systems has limited the use of such sensors in clinical diagnostics. We present a new "disposable photonics" sensor platform in which rice-sized (1 × 4 mm) silicon nitride ring resonator sensor chips are paired with plastic micropillar fluidic cards for sample handling and optical detection. We demonstrate the utility of the platform in the context of detecting human antibodies to SARS-CoV-2, both in convalescent COVID-19 patients and for subjects undergoing vaccination. Given its ability to provide quantitative data on human samples in a simple, low-cost single-use format, we anticipate that this platform will find broad utility in clinical diagnostics for a broad range of assays.