Improving copy cost of a bit array by using big-endian Patricia Trees (or similar) I have an algorithm that on each iteration generates a copy of an object containing a bit array and modifies it (the modification is limited to just 1 element per copy)

While doing the time analysis I cannot neglect the copy cost as this bit array is encoding information about the input set, so for a very large number of elements I have to account for it..

I has been said that effective data structures exists, like big-endian Patricia trees, that can achieve a $O(log(n))$ when copying an instance, and I confirmed there are implementations using them like Data.IntSet from haskell that provides a very good TC for certain operations that I can use to refactor this part of the algorithm ($O(min(n,W))$ W=word size)

I have seen another post in Stack Exchange explaining that is possible to achieve a similar copy cost with AVL persitent trees.

I'm looking for confirmation about this or any other specialized structure that can achieve the reported time complexity for copy.
Yes, you can use standard techniques for persistent data structures to make a version of Patricia trees that has the properties you want.  See also What classes of data structures can be made persistent? and persistent-data-structure on this site.