DANN: Diffractive Acoustic Neural Network for in-sensor computing system target at multi-biomarker diagnosis

Analog machine learning hardware platforms, such as those using wave physics, present potential for edge artificial intelligence (AI) applications due to in-sensor computing architecture, offering superior energy efficiency compared to digital circuits. While the diffractive neural network has been implemented in optical systems, its deployment on integrated acoustic systems has not been achieved due to the challenges associated with hardware optimization. In this paper, we propose the Diffractive Acoustic Neural Network (DANN), a novel approach that applies diffractive neural network algorithms to surface acoustic wave (SAW) systems for in-sensor multibiomarker diagnosis. To address optimization challenges, we introduce a novel training methodology that combines Finite Element Analysis (FEA) with gradient descent. We validate our method on Major Depressive Disorder (MDD) and prostate cancer, achieving accuracies of $74.07 \%$ and $86.0 \%$, respectively, nearly reaching the accuracy levels of clinical diagnoses. By comparing the co-training method with the traditional gradient descent training method and direct training on the FEA model, the co-training method demonstrates its advantages in balancing training efficiency and accuracy. Furthermore, a comparison of power consumption is conducted between the traditional method and the in-sensor computing system, indicating $66 \%$ energy savings attributed to its high level of integration.