Computationally Efficient Design of an LNA Input Matching Network Using Automatic Differentiation

We present a method for the design of an LNA input matching network using automatic differentiation (AD), a technique made popular by machine learning. The input matching network consists of a non-uniform suspended stripline transformer, directly optimized with AD-provided gradients. Compared to the standard approach of finite-differences, AD provides orders of magnitude faster optimization time for gradient-based solvers. This dramatic speedup reduces the iteration time during design and enables the exploration of more complex geometries. The LNA designed with this approach improves over a previous two-section uniform-line design, achieving an average noise temperature of (11.53 $\pm$ 0.42) K over the frequency range of 0.7 GHz to 2 GHz at room temperature. We optimized the geometry in under 5 s, $40$x faster than optimizing with finite-differences.