  This paper deals with the problem of identification of a Robin coefficient
(also known as impedance coefficient) in a parabolic PDE from terminal
observations of the temperature distributions. The problem is ill-posed in the
sense that small perturbation in the observation may lead to a large deviation
in the solution. Thus, in order to obtain stable approximations, we employ the
Tikhonov-regularization. We propose a weak source condition motivated by the
work of Engl and Zou (2000) and obtain a convergence rate of
$O(\d^\frac{1}{2})$, the main goal of this paper, where $\d$ is the noise level
of the observed data. The obtained rate is better than some of the previous
known rates. Moreover, the advantage of the proposed source condition is that
we are getting the above mentioned convergence rate without the need for
characterizing the range space of modelling operator, which is in contrast to
the general convergence theory of Tikhonov-regularization for non linear
operators, where one obtain the same order of convergence by characterizing the
range of the adjoint of the Fr{\'e}chet derivative of modelling operator, a
challenging task for many problems.
