[
    {
        "id": "authors:z6jtz-dq243",
        "collection": "authors",
        "collection_id": "z6jtz-dq243",
        "cite_using_url": "https://authors.library.caltech.edu/records/z6jtz-dq243",
        "type": "article",
        "title": "Computationally Efficient Design of an LNA Input Matching Network Using Automatic Differentiation",
        "author": [
            {
                "family_name": "Shila",
                "given_name": "Kiran A.",
                "orcid": "0000-0003-4652-7038",
                "clpid": "Shila-Kiran-Arik"
            }
        ],
        "abstract": "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.",
        "doi": "10.1109/jmw.2025.3568779",
        "issn": "2692-8388",
        "publisher": "IEEE",
        "publication": "IEEE Journal of Microwaves",
        "publication_date": "2025-07",
        "series_number": "4",
        "volume": "5",
        "issue": "4",
        "pages": "972-982"
    }
]