Likelihood drops on adding random effect

Look at the values of the coefficients and standard deviations that
you are "converging" to.  Your intercept is -9.28, which, with a
binomial family, corresponds to probabilities below 1e-4.  With icfac
= fem the linear predictor is -9.28 - 4.76 = -14.04 corresponding to a
probability of 8e-07.

You are going to need to look at the data and the proportions of
positives for different levels of icfac to see what would make sense.
This problem will create a very ill-defined likelihood surface because
the fitted values will lose sensitivity to the parameters when the
probabilities are so extreme.  If you start extreme values you will
never be able to converge.

PS ?I also tried

start = list( fixef = c( -9.28405, ?2.81300, -4.75935, ?2.91080),
? ? ? ?ST = c(0, 8.29931, 2.56368e-06, ?0.00000))

and

start = list( fixef = list( -9.28405, ?2.81300, -4.75935, ?2.91080),
? ? ? ST = list(0, 8.29931, 2.56368e-06, ?0.00000))

to no avail.

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