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The final testing circuit and Mock-RRAM are varied and complex but come together to be able to produce a measured value of the high resistance and low resistance state. This is done by the Arduino autonomously digitizing the anode voltage, cathode voltage, and output voltage. A ramp voltage that sweeps from 1V to -1V is applied to the Mock-RRAM acting as an input. This ramp voltage is then later defined as the anode voltage which is just one of the two nodes connected to the Mock-RRAM. The cathode voltage is defined as the other node at which the voltage drops across the Mock-RRAM. Using these three voltages as input to the Arduino, the Mock-RRAM path resistance is able to be calculated at any given point in time. Conducting many test cycles and calculating the high and low resistance values allows for a graph to be developed of the chaotic variation of resistance state values over time. This chaotic variation can then be analyzed further in the future in order to better predict trends and characterize the RRAM cell that was tested.
Furthermore, the interchangeability of many devices on the PCB allows for the testing system to do more in the future. Ports have been added to the final PCB in order to connect a packaged RRAM cell. This will allow for the characterization of a real RRAM memory cell later down the line rather than a Mock-RRAM as emulation. Due to the autonomous testing, very few human intervention is needed which makes this board a great baseline for others in the future looking to add to it and collect larger pools of data.
• Completely rewritten with performance and usability in mind
• Cross-Platform vs. Linux Only
• Simple installation procedure vs. compilation and manual library configuration
• Self-contained, single file runtime
• Particle Swarm Optimization routine
NGExtract 2 works by plotting the Ids vs. Vds and Ids vs. Vgs curves of a simulation model and the measured, real-world data. The user can adjust model parameters and re-simulate to attempt to match the curves. The included Particle Swarm Optimization routine attempts to automate this process by iteratively attempting to improve a solution by measuring its sum-squared error against the real-world data that the user has provided.
Three models have been created to visualize and characterize the voltage response of a standing wave accelerating cavity system. These models are generalized to fit any cavity with known values of the quality factor, coupling factor, and resonant frequency but were applied to the Arizona State Universities Compact X-ray Free Electron Laser. To model these systems efficiently, baseband I and Q measurements were used to eliminate the modeling of high frequencies. The three models discussed in this paper include a single standing wave cavity, two cavities coupled through a 3dB quadrature hybrid, and a pulse compression system. The second model on two coupled cavities will demonstrate how detuning will impact two cavities with the same RF source split through a hybrid. The pulse compression model will be used to demonstrate the impact of feeding pulse compression into a standing wave cavity. The pulse compressor will demonstrate more than a 50\% increase of the voltage inside the cavity.