Machine Learning Rebuild for Resistive Silicon Sensors

The evolution of particle detectors has driven the development of resistive silicon sensors, such as low-gain avalanche diodes (LGADs) and their alternating current-coupled versions (AC-LGADs). These devices allow precise measurement of time and position in high-energy environments, facilitating so-called '4D tracking'. However, the resistive structure introduces complex behavior: the charge generated by a particle is distributed among multiple electrodes, which generates shared signals that make spatial reconstruction difficult. Traditional methods based on matrix inversion become computationally expensive and sensitive to noise outside the diagonal. This is where machine learning offers a disruptive alternative, capable of extracting information from entire waveforms and regularizing the reconstruction without the need for simplified analytical models.

In this article we explore how artificial intelligence techniques are transforming event reconstruction into resistive silicon sensors. We address everything from recurrent neural networks with LSTM layers to transformer-based architectures, which can handle arbitrary pad geometries and mitigate distortions at the edges. This approach not only improves positional resolution—reaching approximately 10 micrometers in sensors with a 500 μm pitch—but also reduces the bandwidth required for data transmission, a critical aspect in high-event-rate experiments.

The technical challenges are manifold. The shared signal can be attenuated to levels close to electronic noise, and spatial interpolation requires correlated information from neighboring pads. Machine learning algorithms, trained on synthetic or real data, learn to exploit these correlations to reconstruct the position and arrival time of the particle with high fidelity. LSTM-based architectures, for example, process waveforms sequentially, capturing the temporal evolution of the load. Transformer models, on the other hand, by incorporating the coordinates of each pad as part of the input, become topology-independent and can scale to complex and asymmetric sensor configurations.

From a practical perspective, the implementation of these models in hardware is feasible. Prototypes have been developed for deployment on FPGAs using HLS (High-Level Synthesis), which allows real-time processing. In addition, compressing information by rasterizing waveforms and selecting time windows dramatically reduces the volume of data without losing accuracy. This is essential for particle physics experiments that generate terabytes per second, but it also finds application in sectors such as medical imaging, security, and industrial automation.

At Q2BSTUDIO, we understand that the integration of advanced technologies requires a multidisciplinary approach. Our expertise in custom applications and artificial intelligence allows us to design rebuild systems ranging from simulation to production deployment. We work with AWS and Azure cloud services to scale data processing, and we offer cybersecurity to protect critical infrastructures. In addition, we develop business intelligence services with power bi to visualize patterns in sensor data, and we create AI agents that automate decision-making in real time.

The potential of these sensors goes beyond fundamental physics. In industrial non-destructive inspection, for example, the ability to locate defects with micrometer accuracy is invaluable. In autonomous driving, resistive silicon-based time-of-flight detectors could improve awareness of the environment. And in medical imaging, 4D reconstruction would allow the movement of organs to be tracked during breathing. For all these applications, the combination of resistive sensors and machine learning opens up a new frontier.

One aspect that deserves attention is bandwidth optimization. In systems where each pad generates a full waveform of several hundred samples, the transmission of that data can saturate the links. Raster and window selection techniques, along with intelligent compression based on neural networks, make it possible to reduce the amount of information without sacrificing resolution. You can even train models that predict position from reduced amplitudes, eliminating the need to send out entire waveforms. This is especially relevant for embedded deployments with limited resources.

The adoption of transformer architectures in this domain represents a qualitative leap. By processing all pads simultaneously and weighting their contributions using attention mechanisms, these models can handle non-uniform configurations and correct edge distortions that affect peripheral sensors. In addition, as they are independent of the number of inlets, they facilitate the design of sensors with arbitrary geometries, optimizing the filling factor and collection efficiency.

At Q2BSTUDIO, we combine these advancements with our expertise in custom software to create acquisition and analysis platforms that integrate everything from Monte Carlo simulation to FPGA deployment. Our enterprise AI team develops custom models that are tailored to the specific characteristics of each sensor, and deploys them in cloud or edge infrastructures. In addition, we offer consulting services so that laboratories and companies can adopt these technologies without starting from scratch.

Looking to the future, the convergence of resistive sensors and machine learning promises to revolutionize particle detection and high-precision metrology. The ability to reconstruct trajectories in 4D with micrometer resolution and nanoseconds will open up new possibilities in physics experiments, but also in commercial applications that require extreme quality control. On this path, having a technology partner like Q2BSTUDIO, which masters both hardware and software, is a differential advantage.

We conclude that machine learning-based reconstruction is not just incremental improvement, but a paradigm shift. It overcomes the fundamental limitations of linear methods and offers a scalable path to smarter, more efficient sensors. We invite researchers and companies to explore these solutions with us, leveraging our ability to integrate AWS and Azure cloud services, cybersecurity, and business intelligence services into a single ecosystem. The future of resistive silicon detection is already here, and with artificial intelligence, its potential is limitless.