Identifying trade-offs in aggregation between geography, ind/occ, and temporal specificity

I find that I very often want the finest available disaggregation by time (one-, three- or five-year) and geography (e.g. state, county, Census tract) for some specified disaggregation of industry or occupation, usually three- or four- digit ind/occ/NAICS code. I spend a lot of time looking for, e.g., 1 year ACS values for four-digit NAICS industry codes at the Census Tract level. It seems that often these combination of high levels of disaggregation on three different variables do not exist, and identifying the trade-off surface can be quite difficult.

Am I correct that the finest level of ACS microdata that is ever available is for PUMAs?

Does IPUMS maintain copies of any of the ACS summary files at a finer level of disaggregation, for use in, e.g., small area estimation? If so, can you point me toward documentation for it?

If IPUMS does not maintain any of the summary file information, do you know if anyone has nonetheless combined such information with micro data that IPUMS does maintain? For this case (if no IPUMS ACS summary data available) I am very interested in identifying methodology papers on achieving such combinations via statistical matching, microsimulation, iterative biproportional scaling/RAS balancing/raking, and other methods.

You are correct that the smallest geography available in the ACS microdata is PUMA. Tabular (summary) data for smaller areas is available at factfinder.census.gov. IPUMS NHGIS also has tabular data for sub-PUMA areas. Links between PUMAs and smaller geographic areas can be found in the equivalency files available at this page: https://www.census.gov/programs-surveys/geography/guidance/geo-areas/pumas.html