Franz Schuler

Analytical percolation model for predicting anomalous charge loss in flash memories

Data retention in flash memories is limited by anomalous charge loss. In this work, this phenomenon is modeled with a percolation concept. An analytical model is constructed that relates the charge-loss distribution of moving bits in flash memories with the geometric distribution of oxide traps. The oxide is characterized by a single parameter, the trap density. Combined with a trap-to-trap direct tunneling model, the physical parameters of the electron traps involved in the leakage mechanism are determined. Flash memory failure rate predictions for different oxide qualities, thicknesses and tunnel-oxide voltages are calculated.

Analytical model for failure rate prediction due to anomalous charge loss of flash memories

Anomalous charge loss in flash memories is modeled with a percolation concept. An analytical model is constructed that relates the charge loss distribution of moving bits in flash memories with the geometric distribution of oxide traps, thus linking the phenomenological description of moving bits to physical conduction models. This model allows flash memory failure rate predictions for different oxide qualities and thicknesses.

Statistical model for stress-induced leakage current and pre-breakdown current jumps in ultra-thin oxide layers

We present a statistical, unified picture of Stress-Induced Leakage Current (SILC) generation, pre-breakdown current steps and breakdown in 2.4 nm oxide layers during a constant voltage stress. Pre-breakdown current steps were investigated through gate voltage ramp measurements and modeled by means of a percolation model with variable trap-trap distance. During oxide stress, first single-trap conduction paths are formed, followed by two-trap conduction paths which are identified as pre-breakdown current steps in small devices. Finally, a highly conducting path is formed which triggers breakdown.

Failure Rate Prediction and Accelerated Detection of Anomalous Charge Loss in Flash Memories by Using an Analytical Transient Physics-Based Charge Loss Model

We introduce an analytical physics-based model for the transient simulation of anomalous charge loss in flash memories. This model is applied to determine the bit failure rate and the time-to-failure due to anomalous charge loss. This model can also be used to introduce an accelerated method for the detection of bits suffering from anomalous charge loss.

Physical charge transport models for anomalous leakage current in floating gate-based nonvolatile memory cells

A model for anomalous charge loss in nonvolatile memories is presented based on the physical description of charge transport through the tunnel oxide. This model considers multiphonon-assistance as well as arbitrary three-dimensional (3-D) distributions of oxide defects (electron traps). After identifying the trap-trap distance as the most important parameter, the 3-D model can be simplified to a tunneling model, which describes one tunneling step only. The consistency of this simplified one-step tunneling model with the percolation model for anomalous charge loss description is shown. A further simplification is achieved by deducing an analytical description of the transient behavior based on the one-step tunneling model.

Time Dependent Anomalous Charge Loss Modeling in Flash Memories and an Accelerated Testing Procedure

Data retention is the most critical issue of nonvolatile memories (NVM). Because of the decreasing tunnel oxide thickness, this data retention is determined by a limited population of bits with larger than expected charge loss. This anomalous charge loss has generally been ascribed to the high-field stress during the (tunnel) erase operation. Although several models have been suggested Il-5], up till now there is no commonly accepted charge loss model available. In this paper the direct tunneling model (DT) is proposed to describe the transient behavior of anomalous charge loss, to model accelerated testing by drain disturb, and to derive a simplified analytical method for failure rate prediction.