Deep-Learning-Based Inverse-Designed Millimeter-Wave Passives and Power Amplifiers


引用 2|浏览9
This work describes deep-learning-enabled inverse design of multi-port electromagnetic (EM) structures co-designed with circuits that can enable the synthesis of novel high-frequency on-chip passives and circuits with designer scattering parameters in a rapid and automated fashion. The design of EM structures for high-frequency circuits typically starts from a pre-selected topology of unit functional elements that are subsequently optimized for the desired scattering parameters through time-consuming parameter sweeps, ad hoc optimization algorithms, or prior expert experience. In the space of all possible manufacturable EM structures, there is no reason to believe that these (and therefore, the co-designed circuits) will be close to being "globally" optimal. Inverse design attempts to take a top-down approach to synthesis of EM structures and circuits by efficiently searching in a vastly larger design space of nearly arbitrary distributed structures for the desirable scattering parameters. To allow search of this design space, we need to eliminate time-and resource-intensive EM simulations in iterative search algorithms. To this end, we demonstrate a deep-learning-based forward model that captures accurately the scattering parameters of any arbitrary planar EM structure on chip and demonstration of rapid synthesis of millimeter-wave (mmWave) EM structures utilizing the aforementioned model. As a proof of concept, we synthesize a broadband, low-loss but seemingly arbitrary-looking 2-D output matching network for a 30-100-GHz mmWave power amplifier (PA). The PA achieves a power added efficiency (PAE) of 16%-24.7%, and a saturation power of 16.7-19.5 dBm across P-sat,P-3 dB bandwidth of 30-94 GHz (103.2%). The PA supports both single-carrier and concurrent modulation at multi-gigabit per second. The design approaches can open up a new design space and allow rapid synthesis of complex RF-to-terahertz (THz) circuits and systems, reducing the need for prior experience, and time-intensive manual optimization typical for high-frequency circuits.
Scattering parameters,Millimeter wave communication,Predictive models,Integrated circuit modeling,Dielectrics,Convolutional neural networks,Topology,5G,6G,broadband,CMOS,deep learning,inverse design,millimeter wave (mmWave),power amplifier (PA),SiGe,silicon,transmitter
AI 理解论文
Chat Paper