arXiv subjects

G. Dash

Publications and source records attributed to G. Dash.

5 recordsLinked to original sources

arXiv2026

TCAD + Allpi$\text{x}^2$ Simulation study of MALTA2, a Depleted Monolithic Active Pixel Sensor for future tracking

In this work, a hybrid simulation framework combining TCAD and Allpi$\text{x}^2$ is presented to investigate the sensor properties of MALTA2, a depleted monolithic active pixel sensor designed for future tracking. The study starts from 3D modeling and transient simulations in TCAD, with generic doping profiles and simple well structures. The resulting doping profiles and electric field are extracted and fed into Allpi$\text{x}^2$ for high-statistics Monte Carlo simulations in both DUT-only and full-telescope mode. Simulations reveal a strong dependence of sensor performance, specifically the detection efficiency and cluster size, on the doping concentration of the N-type blanket at the sensor surface. The doping concentration is then optimized by comparing simulations with measurement data. The active depth of the depleted region of the MALTA2 sensor is estimated in both simulations and measurements using a grazing angle method, in which the sensor is positioned at various inclinations relative to the beam, covering angles from 0 to 60 degrees. Excellent agreement on active depth is obtained with the optimal doping concentration, showing a deviation of 2\% from the measured value at a threshold of 450\,$\text{e}^-$. Consequently, the framework offers a generic toolkit for sensor studies without requiring proprietary information.

physics.ins-det↗

arXiv2026

Charge collection parameterization of MALTA2, a depleted monolithic active pixel sensor

A fast simulation method is presented for a depleted monolithic active pixel sensor, which uses a data driven parameterization of the charge collection and propagation. This approach provides an efficient alternative to TCAD simulations, particularly for sensors whose proprietary process details - such as doping profiles or implant geometries - are unavailable. Data was obtained with a MALTA2 sensor fabricated in a 180 nm CMOS imaging technology on 30 {\mu}m epitaxial silicon using the MALTA beam telescope at CERN SPS. The model reproduces the measured inpixel efficiency with high accuracy and enables a realistic yet computationally lightweight analog pixel simulation. This method will be further employed in optimizing the digital sensor design for applications in high-rate particle tracking and high-granularity calorimetry.

physics.ins-det↗

arXiv2025

Study of MALTA2, a Depleted Monolithic Active Pixel Sensor, with grazing angles at CERN SPS 180 GeV/c hadron beam

MALTA2 is a Depleted Monolithic Active Pixel Sensor designed to meet the challenging requirements of future collider experiments, in particularly extreme radiation tolerance and high hit rate. The sensor is fabricated in a modified Tower 180 nm CMOS imaging technology to mitigate performance degradation caused by 100 MRad of Total Ionising Dose and greater than 10^{15} 1 MeV n_{eq}/cm^2 of Non-Ionising Energy Loss. MALTA2 samples have been tested during the CERN SPS test beam campaign in 2023-2024, before and after irradiation at a fluence of 1 $\times$ 10^{15} 1 MeV n_{eq}/cm^2. The sensors were positioned at various inclinations relative to the beam, covering grazing angles from 0 to 60 degrees. This contribution presents measurements of detection efficiency and cluster size as functions of these angles, along with an estimation of the active depth of the depleted region based on the test beam results.

hep-ex↗

arXiv2025

Timing characterization of MALTA and MALTA2 pixel detectors using Micro X-ray source

The MALTA monolithic active pixel detector is developed to address some of the challenges anticipated in future high-energy physics detectors. As part of its characterization, we conducted timing studies necessary to provide a figure of merit for this family of monolithic pixel detectors. MALTA has a metal layer in front-end electronics, and the conventional laser technique is not suitable for timing studies due to the reflection of the laser from the metallic surface. X-rays have been employed as a more effective alternative for penetration through these layers. The triggered X-ray set-up is designed to study timing measurements of monolithic detectors. The timing response of the X-ray set-up is characterized using an LGAD. The timing response of the MALTA and MALTA2 pixel detectors is studied, and the best response time of MALTA2 pixel detectors is measured at about 2.6 ns.

physics.ins-det↗

arXiv2024

System-level Impact of Non-Ideal Program-Time of Charge Trap Flash (CTF) on Deep Neural Network

Learning of deep neural networks (DNN) using Resistive Processing Unit (RPU) architecture is energy-efficient as it utilizes dedicated neuromorphic hardware and stochastic computation of weight updates for in-memory computing. Charge Trap Flash (CTF) devices can implement RPU-based weight updates in DNNs. However, prior work has shown that the weight updates (V_T) in CTF-based RPU are impacted by the non-ideal program time of CTF. The non-ideal program time is affected by two factors of CTF. Firstly, the effects of the number of input pulses (N) or pulse width (pw), and secondly, the gap between successive update pulses (t_gap) used for the stochastic computation of weight updates. Therefore, the impact of this non-ideal program time must be studied for neural network training simulations. In this study, Firstly, we propose a pulse-train design compensation technique to reduce the total error caused by non-ideal program time of CTF and stochastic variance of a network. Secondly, we simulate RPU-based DNN with non-ideal program time of CTF on MNIST and Fashion-MNIST datasets. We find that for larger N (~1000), learning performance approaches the ideal (software-level) training level and, therefore, is not much impacted by the choice of t_gap used to implement RPU-based weight updates. However, for lower N (<500), learning performance depends on T_gap of the pulses. Finally, we also performed an ablation study to isolate the causal factor of the improved learning performance. We conclude that the lower noise level in the weight updates is the most likely significant factor to improve the learning performance of DNN. Thus, our study attempts to compensate for the error caused by non-ideal program time and standardize the pulse length (N) and pulse gap (t_gap) specifications for CTF-based RPUs for accurate system-level on-chip training.

cs.NE↗