Defect-Aware Physics-Based Compact Model for Ferroelectric nvCap: From TCAD Calibration to Circuit Co-Design

TL;DR

This paper introduces a physics-based compact model for ferroelectric nvCap devices that accurately captures small-signal capacitance, variability, and degradation, enabling improved circuit design for non-volatile memory applications.

Contribution

It presents a novel multi-scale modeling approach combining experimental data, TCAD simulations, and circuit validation to accurately model ferroelectric capacitance behavior.

Findings

Achieved +/- 5 mV sense margin in memory read-out

Demonstrated impact of device endurance on circuit performance

Enabled design of selector-less 3D-stacked memory arrays

Abstract

Ferroelectric non-volatile capacitance-based memories enable non-destructive readout and low-power in-memory computing with 3D stacking potential. However, their limited memory window (1-10 fF/{\mu}m) requires material-device-circuit co-optimization. Existing compact models fail to capture the physics of small-signal capacitance, device variability, and cycling degradation, which are critical parameters for circuit design. In non-volatile capacitance devices, the small-signal capacitance difference of the polarization states is the key metric. The majority of the reported compact models do not incorporate any physical model of the capacitance as a function of voltage and polarization. We present a physics-based compact model that captures small-signal capacitance, interface and bulk defect contributions, and device variations through multi-scale modeling combining experimental data, TCAD simulations, and circuit validation. Based on this methodology, we show optimized memory read-out with +/- 5 mV sense margin and impact of device endurance at the circuit level. This work presents a comprehensive compact model which enables the design of selector-less arrays and 3D-stacked memories for compute-in-memory and storage memory.