RRAM-Based Neuromorphic Computing Systems
387
RRAM-based Synaptic Devices and System Level Simulations
Different types of RRAM devices have been introduced in Chap. 10, i.e., cation and
anion devices. Their promising performances as synaptic devices have been widely
investigated and demonstrated on different neuromorphic computing platforms. The
underlying mechanism of different RRAM structures might lead to a huge difference
in macroscopic behavior of the device. Through structural engineering and rigorous
optimization of device programming schemes, RRAM devices have demonstrated
highly stochastic memory behavior to significantly more deterministic features. To
accommodate the different synaptic behaviors of these devices, various learning
rules have also been implemented. In this section, synaptic properties of different
RRAM devices under various learning rules with several system-level simulations
are discussed.
2 Anion-Based Synaptic Devices
The underlying mechanism of anion devices is based on the oxygen vacancy defects
modulation within the oxide layer under external electric field. The fundamental
structure of an anion device consists of an oxide switching layer coupled with an
inert electrode on one side and oxygen reservoir system on the other side, which can
be in the form of reactive electrode (Ti, Hf, Ta, etc.) or oxygen-deficient oxide layer.
Anion devices have been reported to have high scalability of sub-10 nm [7–9], excellent reliability (endurance as high as 10
12 and retention of more than 10 years) [10–
12], multibit per cell capability, and low energy consumption. Anion devices initially
emerged as one of the most promising candidates in non-volatile memory technology
as both embedded memory and standalone memory for high density storage applications. In recent years, these devices have also attracted interest from neuromorphic
computing and engineering community due to their desired characteristics. They have
then been extensively studied and implemented as synaptic device for various NN
applications, mainly taking advantage of their high scalability and analog memory
characteristic. Anion-based devices can be categorized into two major classes based
on the switching nature of the device, i.e., localized (filamentary) and non-localized
(non-filamentary) switching class. The difference between these two device classes
is mainly on the active area involved during switching operation, with the former
involves significantly smaller area than the latter.
a. Filamentary Devices
In general, the filamentary anion devices have an abrupt SET process, i.e., transition
from low to high conductance state, while having a gradual RESET process, i.e.,
transition from high to low conductance state. The gradual RESET process holds
the main advantage of anion device over its cation counterpart as a synaptic device
to achieve gradual depression. This is because achieving a gradual SET process
during potentiation can be performed by controlling the compliance current level
387
RRAM-based Synaptic Devices and System Level Simulations
Different types of RRAM devices have been introduced in Chap. 10, i.e., cation and
anion devices. Their promising performances as synaptic devices have been widely
investigated and demonstrated on different neuromorphic computing platforms. The
underlying mechanism of different RRAM structures might lead to a huge difference
in macroscopic behavior of the device. Through structural engineering and rigorous
optimization of device programming schemes, RRAM devices have demonstrated
highly stochastic memory behavior to significantly more deterministic features. To
accommodate the different synaptic behaviors of these devices, various learning
rules have also been implemented. In this section, synaptic properties of different
RRAM devices under various learning rules with several system-level simulations
are discussed.
2 Anion-Based Synaptic Devices
The underlying mechanism of anion devices is based on the oxygen vacancy defects
modulation within the oxide layer under external electric field. The fundamental
structure of an anion device consists of an oxide switching layer coupled with an
inert electrode on one side and oxygen reservoir system on the other side, which can
be in the form of reactive electrode (Ti, Hf, Ta, etc.) or oxygen-deficient oxide layer.
Anion devices have been reported to have high scalability of sub-10 nm [7–9], excellent reliability (endurance as high as 10
12 and retention of more than 10 years) [10–
12], multibit per cell capability, and low energy consumption. Anion devices initially
emerged as one of the most promising candidates in non-volatile memory technology
as both embedded memory and standalone memory for high density storage applications. In recent years, these devices have also attracted interest from neuromorphic
computing and engineering community due to their desired characteristics. They have
then been extensively studied and implemented as synaptic device for various NN
applications, mainly taking advantage of their high scalability and analog memory
characteristic. Anion-based devices can be categorized into two major classes based
on the switching nature of the device, i.e., localized (filamentary) and non-localized
(non-filamentary) switching class. The difference between these two device classes
is mainly on the active area involved during switching operation, with the former
involves significantly smaller area than the latter.
a. Filamentary Devices
In general, the filamentary anion devices have an abrupt SET process, i.e., transition
from low to high conductance state, while having a gradual RESET process, i.e.,
transition from high to low conductance state. The gradual RESET process holds
the main advantage of anion device over its cation counterpart as a synaptic device
to achieve gradual depression. This is because achieving a gradual SET process
during potentiation can be performed by controlling the compliance current level