Real-Time SiPM Pulse Deconvolution for a High-granularity Dual-readout Calorimeter with Neutral Networks
The High-Granularity Dual-Readout Calorimeter (HG-DREAM) designed for FCC-ee aims to achieve unprecedented energy resolution through fine three-dimensional shower imaging, simultaneous measurement of Cherenkov and scintillation signals, and machine learning capabilities. A key enabling technology is longitudinal segmentation via timing measurements, where multiple energy deposits along optical fibers are distinguished by the arrival times of Cherenkov photons. We present quantized one-dimensional convolutional neural networks that predict pulse locations directly from 80-sample, 16 ns waveforms digitized at 200 ps, and evaluate their implementation in front-end electronics. Three architectures, a compact baseline and two variants using dilation and stride to reach the $\sim$ 20-sample receptive field set by the SiPM pulse width, achieve ROC AUC above 0.95 with four to eight filters per layer. Fixed-point quantization to a $<$16,8$>$ baseline costs less than $10^{-3}$ in AUC relative to the floating-point reference. Applied to pairwise deposits, the smallest model separates two pulses with better than 95\% probability at longitudinal separations of 20 cm and above. Synthesized at 400 MHz, this model uses 9663 LUT on a Xilinx XCVU13P FPGA and occupies $\sim$ 18500 $μ$$m^2$ in TSMC 28 nm CMOS with reconfigurable weights. Assuming a triggered readout architecture for FCC-ee, the $\sim$ 500 kHz Z-pole trigger rate will be the most stringent throughput requirement across all FCC-ee operating modes. The ASIC implementation of the model sustains 4.8 MHz, exceeding the 0.5 MHz requirement by an order of magnitude, while the reconfigurable-weight FPGA implementation fails to do so. Sustaining the 40 MHz FCC-ee Z-pole bunch-crossing frequency in the triggerless architecture is not feasible with the models developed and the hardware targeted in this work.