ECRAM as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

“…[108][109][110][111] In addition, electrochemical random-access memory (ECRAM) based on ion intercalation has recently been reported as a promising synaptic cell, showing multi-states and incremental switching with near-ideal switching symmetry and linearity. [29,[112][113][114][115][116][117][118][119] The electrochemically driven ion intercalation process is more controllable than filament-related ion movements in RRAM; therefore, ECRAM also exhibits a much smaller stochasticity. In addition, by borrowing the battery concept, those devices successfully decouple the read and write operations and thus realize low programming energy and long retention time simultaneously.…”

Memristive and CMOS Devices for Neuromorphic Computing

“…Its operation relies on the intercalation/de-intercalation of ions in a channel layer to tune the device conductance. As reported in [108], the intercalation of Li + ions into the WO 3 layer by application of a positive voltage at the gate terminal leads the device to experience a conductance increase whereas the de-intercalation of Li + ions under negative bias leads the device to experience a conductance decrease. The linear conductance change is achievable in ECRAM thanks to the decoupling of read/write paths, which makes this device concept very attractive for synaptic applications, mainly for hardware implementation of synaptic weights in ANNs, where analog and symmetric weight updates play a crucial role.…”

“…In addition to the two-terminal devices, memristive concepts also include the class of three-terminal devices whose main examples are those depicted in Figure 12, namely (a) the ferroelectric field-effect transistor (FeFET) [107], (b) the electro-chemical random access memory (ECRAM) [108], and (c) the spin-orbit torque magnetic random access memory (SOT-MRAM) [109]. Other interesting three-terminal concepts that have been recently investigated for neuromorphic computing applications are the 2D semiconductor-based mem-transistors [110,111] and the domain-wall-based magnetic memories [112,113].…”

“…Also, it has been operated to replicate synapse [116] and neuron [117,118] functions, which, combined with 3D integration opportunity, makes it a strong candidate for neuromorphic computing applications. Figure 12b illustrates the device structure of the ECRAM consisting of an MOS transistor where a solid-state electrolyte based on inorganic materials, such as lithium phosphorous oxynitride (LiPON) [108,119], or organic materials, such as poly (3, 4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) [120], is used as the gate dielectric. Its operation relies on the intercalation/deintercalation of ions in a channel layer to tune the device conductance.…”

“…Its operation relies on the intercalation/deintercalation of ions in a channel layer to tune the device conductance. As reported in [108], the Figure 12a shows the structure of the FeFET consisting of an MOS transistor with an FE material, such as doped-HfO 2 [103], and perovskites [106], serving as the gate dielectric. Here, the application of external pulses at the gate terminal induces a non-volatile polarization switching within the FE dielectric, leading to a change of the transistor threshold, hence of the channel conductivity, which can be probed simply by reading the current at the drain terminal.…”