Embedded emerging memory technologies for neuromorphic computing: temperature instability and reliability

For the first time, the impact of temperature instability of resistive memory switching on potential neuromorphic computing applications has been extensively studied using eNVM-R and eNVM-M technologies developed on Intel 22FFL process. The reliability risk assessment shows that the effects of ambient temperature (e.g. resistance or conductance shifting with varying temperature) can lead to potential degradation of the neural network accuracy. Our results provide additional insight into device-level physical models and circuit-level design guidance for potential AI hardware applications.

[1]  H.-S. Philip Wong,et al.  In-memory computing with resistive switching devices , 2018, Nature Electronics.

[2]  T. Ghani,et al.  MRAM as Embedded Non-Volatile Memory Solution for 22FFL FinFET Technology , 2018, 2018 IEEE International Electron Devices Meeting (IEDM).

[3]  Pulkit Jain,et al.  13.3 A 7Mb STT-MRAM in 22FFL FinFET Technology with 4ns Read Sensing Time at 0.9V Using Write-Verify-Write Scheme and Offset-Cancellation Sensing Technique , 2019, 2019 IEEE International Solid- State Circuits Conference - (ISSCC).

[4]  H. Nhalil,et al.  Switching of multi-state magnetic structures via domain wall propagation triggered by spin-orbit torques , 2019, Scientific Reports.

[5]  P. Asenov,et al.  Engineering amorphous-crystalline interfaces in TiO2−x/TiO2−y-based bilayer structures for enhanced resistive switching and synaptic properties , 2016 .

[6]  Qi Liu,et al.  First Demonstration of OxRRAM Integration on 14nm FinFet Platform and Scaling Potential Analysis towards Sub-10nm Node , 2020, International Electron Devices Meeting.

[7]  Fei Zhou,et al.  Intrinsic SiOx-based unipolar resistive switching memory. II. Thermal effects on charge transport and characterization of multilevel programing , 2014 .

[8]  P. Bai,et al.  Non-Volatile RRAM Embedded into 22FFL FinFET Technology , 2019, 2019 Symposium on VLSI Technology.

[9]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.

[10]  Xiufeng Han,et al.  Temperature dependence of giant tunnel magnetoresistance in epitaxial Fe/MgO/Fe magnetic tunnel junctions , 2008 .

[11]  Zhitang Song,et al.  Scandium doping brings speed improvement in Sb2Te alloy for phase change random access memory application , 2018, Scientific Reports.

[12]  Yao-Feng Chang,et al.  Physical and chemical mechanisms in oxide-based resistance random access memory , 2015, Nanoscale Research Letters.

[13]  Jinho Ahn,et al.  Lattice Distortion in In3SbTe2 Phase Change Material with Substitutional Bi , 2015, Scientific Reports.

[14]  Fei Zhou,et al.  Dynamic conductance characteristics in HfOx-based resistive random access memory , 2017 .

[15]  Heiner Giefers,et al.  Mixed-precision in-memory computing , 2017, Nature Electronics.

[16]  Yi Li,et al.  Neuronal dynamics in HfOx/AlOy-based homeothermic synaptic memristors with low-power and homogeneous resistive switching. , 2018, Nanoscale.

[17]  Pulkit Jain,et al.  13.2 A 3.6Mb 10.1Mb/mm2 Embedded Non-Volatile ReRAM Macro in 22nm FinFET Technology with Adaptive Forming/Set/Reset Schemes Yielding Down to 0.5V with Sensing Time of 5ns at 0.7V , 2019, 2019 IEEE International Solid- State Circuits Conference - (ISSCC).