arXiv subjects

Prasanna Venkatesan

Publications and source records attributed to Prasanna Venkatesan.

4 recordsLinked to original sources

arXiv2026

Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model

Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limitations with lower programming voltages. However, the data retention of 3D Fe-VNAND is hindered by the complex interaction between charge detrapping and ferroelectric depolarization. Developing optimized device designs requires exploring an extensive parameter space, but the high computational cost of conventional Technology Computer-Aided Design (TCAD) tools makes such wide-scale optimization impractical. To overcome these simulation barriers, we present a Physics-Informed Neural Operator (PINO)-based AI surrogate model designed for high-efficiency prediction of threshold voltage (Vth) shifts and retention behavior. By embedding fundamental physical principles into the learning architecture, our PINO framework achieves a speedup exceeding 10000x compared to TCAD while maintaining physical accuracy. The resulting surrogate provides a physics-consistent data engine for compact model parameter extraction and look-up-table (LUT) generation, directly supporting reliability-aware SPICE simulation of Fe-VNAND. This study demonstrates the model's effectiveness on a single FeFET configuration, serving as a pathway toward modeling the retention loss mechanisms.

cs.LG↗

arXiv2026

ALD Oxidant as A Tuning Knob for Memory Window Expansion in Ferroelectric FETs for Vertical NAND Applications

Dielectric inserts are widely used to expand the memory window (MW) in ferroelectric FETs (FeFETs) for vertical NAND applications, with prior efforts focused primarily on material selection and stack positioning. Here, we demonstrate that the ALD oxidant used for the Al2O3 interlayer serves as a process-level tuning knob for MW engineering. H2O-grown Al2O3 yields a significantly larger MW (7-8 V) compared to O3 (4 V) for both gate-injection (12/3) and tunnel dielectric (8/3/8) configurations. While the tunnel dielectric (8/3/8) stack maintains robust retention up to 1e4s at 125C despite the larger MW, the gate-injection (12/3) configuration exhibits pronounced retention degradation for the H2O case. The enhanced MW is attributed to higher interlayer leakage associated with H2O-based ALD. These results establish oxidant choice as a key process parameter for co-optimizing MW and retention in ferroelectric NAND technologies.

cond-mat.mtrl-sci↗

arXiv2026

The First Switch Effect in Ferroelectric Field-Effect Transistors

In this work, a ferroelectric field-effect transistor (FEFET) is systematically characterized and compared with an equivalent standard MOSFET with an equivalent oxide thickness. We show that these two devices, with a silicon channel, exhibit similar pristine state transfer characteristics but starkly different endurance characteristics. In contrast to the MOSFET, the FEFET shows a significant increase in sub-threshold swing in the first write pulse. Based on this, we reveal that this first write pulse (cycle 1) generates more than half of the total traps generated during the fatigue cycling in FEFETs. We call this the 'First Switch Effect'. Further, by polarizing a pristine FEFET step by step, we demonstrate a direct correlation between the switched polarization and interface trap density during the first switch. Through charge pumping measurements, we also observe that continued cycling generates traps more towards the bulk of the stack, away from the Si/SiO2 interface in FEFETs. We establish that: (1) the first switch effect leads to approximately 50% of the total trap density (Nit) near the Si/SiO2 interface until memory window closure; and (2) further bipolar cycling leads to trap generation both at and away from Si/SiO2 interface in FEFETs.

cond-mat.mtrl-sci↗

arXiv2011

A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars

Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model.

cs.NE↗