  The manual design of analog circuits is a tedious task of parameter tuning
that requires hours of work by human experts. In this work, we make a
significant step towards a fully automatic design method that is based on deep
learning. The method selects the components and their configuration, as well as
their numerical parameters. By contrast, the current literature methods are
limited to the parameter fitting part only. A two-stage network is used, which
first generates a chain of circuit components and then predicts their
parameters. A hypernetwork scheme is used in which a weight generating network,
which is conditioned on the circuit's power spectrum, produces the parameters
of a primal RNN network that places the components. A differential simulator is
used for refining the numerical values of the components. We show that our
model provides an efficient design solution, and is superior to alternative
solutions.

Machine Learning;Signal Processing;Machine Learning