Three-Dimensional Simulation of Charge-Trap Memory Programming—Part II: Variability

This paper investigates the statistical variability sources affecting the program operation of nanoscale charge-trap memories. Using the 3-D TCAD model presented in Part I of this work, featuring a Monte Carlo simulation approach to deal with discrete traps in the storage layer, atomistic doping in the substrate, and granular electron injection from the substrate to the storage layer, we consider the effect of three main variability sources impacting charge-trap memory programming: 1) the statistical process ruling electron injection and trapping; 2) the fluctuation in the number and position of the trapping sites; and 3) the statistical distribution of the threshold-voltage shift induced by stored electrons in presence of percolative substrate conduction. We show that the first variability source plays the dominant role in determining the statistical dispersion of cell threshold voltage during the program operation.

[1]  Haitao Liu,et al.  3D Simulation Study of Cell-Cell Interference in Advanced NAND Flash Memory , 2009, 2009 IEEE Workshop on Microelectronics and Electron Devices.

[2]  Y. J. Chen,et al.  Study of electron and hole injection statistics of BE-SONOS NAND Flash , 2010, 2010 IEEE International Memory Workshop.

[3]  Subhash Saini,et al.  Hierarchical approach to "atomistic" 3-D MOSFET simulation , 1999, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[4]  Christian Monzio Compagnoni,et al.  Three-Dimensional Simulation of Charge-Trap Memory Programming—Part I: Average Behavior , 2011, IEEE Transactions on Electron Devices.

[5]  A. Ghetti,et al.  3D Monte Carlo simulation of the programming dynamics and their statistical variability in nanoscale charge-trap memories , 2010, 2010 International Electron Devices Meeting.

[6]  A. Asenov,et al.  Simulation Study of Individual and Combined Sources of Intrinsic Parameter Fluctuations in Conventional Nano-MOSFETs , 2006, IEEE Transactions on Electron Devices.

[7]  A. Visconti,et al.  Ultimate Accuracy for the nand Flash Program Algorithm Due to the Electron Injection Statistics , 2008, IEEE Transactions on Electron Devices.

[8]  Tetsuo Endoh,et al.  Fast and accurate programming method for multi-level NAND EEPROMs , 1995, 1995 Symposium on VLSI Technology. Digest of Technical Papers.

[9]  C.M. Compagnoni,et al.  Analytical Model for the Electron-Injection Statistics During Programming of Nanoscale nand Flash Memories , 2008, IEEE Transactions on Electron Devices.

[10]  Nobuyuki Sano,et al.  On discrete random dopant modeling in drift-diffusion simulations: physical meaning of 'atomistic' dopants , 2002, Microelectron. Reliab..

[11]  D. Schmitt-Landsiedel,et al.  Novel model for cell - system interaction (MCSI) in NAND Flash , 2008, 2008 IEEE International Electron Devices Meeting.

[12]  A. Asenov,et al.  Statistical aspects of reliability in bulk MOSFETs with multiple defect states and random discrete dopants , 2008, Microelectron. Reliab..

[13]  Christian Monzio Compagnoni,et al.  Comprehensive Investigation of Statistical Effects in Nitride Memories—Part I: Physics-Based Modeling , 2010, IEEE Transactions on Electron Devices.

[14]  Carmine Miccoli,et al.  Impact of Control-Gate and Floating-Gate Design on the Electron-Injection Spread of Decananometer nand Flash Memories , 2010, IEEE Electron Device Letters.

[15]  Krishna Parat,et al.  25nm 64Gb MLC NAND technology and scaling challenges invited paper , 2010, 2010 International Electron Devices Meeting.

[16]  Andrew R. Brown,et al.  Simulation of intrinsic parameter fluctuations in decananometer and nanometer-scale MOSFETs , 2003 .

[17]  A. Visconti,et al.  Comprehensive Analysis of Random Telegraph Noise Instability and Its Scaling in Deca–Nanometer Flash Memories , 2009, IEEE Transactions on Electron Devices.

[18]  A. Lacaita,et al.  First evidence for injection statistics accuracy limitations in NAND Flash constant-current Fowler-Nordheim programming , 2007, 2007 IEEE International Electron Devices Meeting.

[19]  A Maconi,et al.  Comprehensive Investigation of Statistical Effects in Nitride Memories—Part II: Scaling Analysis and Impact on Device Performance , 2010, IEEE Transactions on Electron Devices.

[20]  H. Wong,et al.  Three-dimensional "atomistic" simulation of discrete random dopant distribution effects in sub-0.1 /spl mu/m MOSFET's , 1993, Proceedings of IEEE International Electron Devices Meeting.