Hi all, I've made a Jupyter notebook with some examples of

pygmid

module made by prof. Murmann during his presentation. The document is on spanish but the code should be understandable by anyone. I'm sharing this because it has some functions that should be of interest at the end of the notebook. •

gmid_basic_workflow(fet_model, mos_params)

tooks a dictionary with the initial parameters (gmid, gm, L, vsb, vds) and it returns a pandas DataFrame with relevant figures of merit derived from the LUT and the parameters. •

plot_mos_characteristics(df, plot_info)

takes the same df returned by previous function, and it plots the parameters in a way that is easy to analyze tradeoffs between them. It takes a dictionary that indicates which df rows plot. • The attached image is the plot of some figures of merit of the differential pair shown on the last meeting for gm/ID on range 5 to 20. Some lines overlap (W with CGG) https://github.com/ChipUSM/usm-vlsi-tools/blob/main/shared_xserver/gmid-test.ipynb Hi @Boris Murmann. With the figures extracted from the tables. Do we have equations to estimate the specs of the differential pair? I would like to do that before spice generation and simulation

What do you mean by specs of the differential pair?

The function on the notebook gets the figures of merit of a transistor device. I don't know if that information allows me to compute an estimate of specs of a higher level device, like an OTA with certain load and bandwith, psrr requirements

Stay tuned for the next meetup.