Abstract:Nanoscale (B28 nm) non-volatile multi-level conductive-bridging-random-access-memory (CBRAM) cells are developed by using a CuO solid-electrolyte, providing a V set of B0.96 V, a V reset of BÀ1.5 V, a B1 Â 10 2 memory margin, B3 Â 10 6 write/erase endurance cycles with 100 ms AC pulse, B6.63 years retention time at 85 1C, B100 ns writing speed, and multi-level (four-level) cell operation. Their nonvolatile memory cell performance characteristics are intensively determined by studying material properties such a… Show more
“…These issues mean that despite the evident long-standing success of conventional memories, the search for alternatives, so-called emerging memories, continues unabated 6,7 . Charge trap memory 8,9 , phase change memory 10,11 , ferroelectric RAM 12,13 , resistive RAM 14,15 , conductive bridge RAM 16,17 and magnetoresistive RAM 18,19 , collectively called storage-class memory (SCM) 20 are all examples of emerging memories which have been subject to vigorous research activity.…”
Whilst the different forms of conventional (charge-based) memories are well suited to their individual roles in computers and other electronic devices, flaws in their properties mean that intensive research into alternative, or emerging, memories continues. In particular, the goal of simultaneously achieving the contradictory requirements of non-volatility and fast, low-voltage (low-energy) switching has proved challenging. Here, we report an oxide-free, floating-gate memory cell based on III-V semiconductor heterostructures with a junctionless channel and non-destructive read of the stored data. Non-volatile data retention of at least 10
4
s in combination with switching at ≤2.6 V is achieved by use of the extraordinary 2.1 eV conduction band offsets of InAs/AlSb and a triple-barrier resonant tunnelling structure. The combination of low-voltage operation and small capacitance implies intrinsic switching energy per unit area that is 100 and 1000 times smaller than dynamic random access memory and Flash respectively. The device may thus be considered as a new emerging memory with considerable potential.
“…These issues mean that despite the evident long-standing success of conventional memories, the search for alternatives, so-called emerging memories, continues unabated 6,7 . Charge trap memory 8,9 , phase change memory 10,11 , ferroelectric RAM 12,13 , resistive RAM 14,15 , conductive bridge RAM 16,17 and magnetoresistive RAM 18,19 , collectively called storage-class memory (SCM) 20 are all examples of emerging memories which have been subject to vigorous research activity.…”
Whilst the different forms of conventional (charge-based) memories are well suited to their individual roles in computers and other electronic devices, flaws in their properties mean that intensive research into alternative, or emerging, memories continues. In particular, the goal of simultaneously achieving the contradictory requirements of non-volatility and fast, low-voltage (low-energy) switching has proved challenging. Here, we report an oxide-free, floating-gate memory cell based on III-V semiconductor heterostructures with a junctionless channel and non-destructive read of the stored data. Non-volatile data retention of at least 10
4
s in combination with switching at ≤2.6 V is achieved by use of the extraordinary 2.1 eV conduction band offsets of InAs/AlSb and a triple-barrier resonant tunnelling structure. The combination of low-voltage operation and small capacitance implies intrinsic switching energy per unit area that is 100 and 1000 times smaller than dynamic random access memory and Flash respectively. The device may thus be considered as a new emerging memory with considerable potential.
“…The I–V curve of the HRS between 0 V and V
set (0.95 V) was well fitted by the ionic conduction mechanism with a slope of 1.9 (defined by equation (3) and shown in Fig. 6a
6, 29 .Figure 6Current conduction mechanism for Ag-doped PEO polymer-electrolyte based CBRAM cell. For electro-forming at a positive applied bias on Pt cathode: ( a ) HRS between 0 and V
set (+0.95 V), ( b ) V
set (+0.95 V) and 0 V, ( c ) NDR, and ( d ) V
reset (−1.70 V) and 0 V. For electro-forming at a negative applied bias on Pt cathode: ( e ) HRS between 0 and V
set (−0.80 V), ( f ) V
set (−0.80 V) and 0 V, ( g ) NDR, and ( h ) V
reset (−2.10 V) and 0 V.
…”
Section: Resultsmentioning
confidence: 83%
“…Breakage of the dendrite occurred at near the Pt cathode since the diameter of the secondary and tertiary dendrites near the Pt cathode was smaller than that of the primary Ag dendrite grown on the Pt anode. The I – V curve of NDR was correlated with the current conduction mechanism of tunneling 6, 31 , as defined by equation (5) and shown in Fig. 6c.
…”
Section: Resultsmentioning
confidence: 99%
“…CBRAM has been intensively studied as a promising nonvolatile memory cell for storage-class memory and for terabit-integration nonvolatile memory cells 1–3 , because it has a bipolar switching characteristic 4, 5 , is capable of multi-level-cell (MLC) operation 6 , has sufficient write/erase endurance cycles and retention time 7, 8 , and operates quickly 9, 10 . Its nonvolatile memory characteristics originates from ionic conduction through drift and electrochemical redox due to the reactive electrode causing metal ion filaments to form (electro-forming) and break (electro-breaking) in the solid electrolytes between the top and bottom electrode 7, 11–13 .…”
Section: Introductionmentioning
confidence: 99%
“…In addition, although morphologies have been reported for conductive metallic filaments in a solid electrolyte of a CBRAM cell, the mechanism behind electro-forming and electro-breaking of the conductive metallic filaments has not been explained through any morphology observation 6 , 14 , 24 , 25 . Thus, we intentionally designed a lateral CBRAM cell using Ag-doped PEO polymer electrolyte between inert Pt electrodes.…”
Ag-doped polymer (polyethylene oxide: PEO) conductive-bridging-random-access-memory (CBRAM) cell using inert Pt electrodes is a potential electro-forming free CBRAM cells in which electro-forming and electro-breaking of nanoscale (16~22-nm in diameter) conical or cylindrical Ag filaments occurs after a set or reset bias is applied. The dependency of the morphologies of the Ag filaments in the PEO polymer electrolyte indicates that the electro-formed Ag filaments bridging the Pt cathode and anode are generated by Ag+ ions drifting in the PEO polymer electrolyte toward the Pt anode and that Ag dendrites grow via a reduction process from the Pt anode, whereas electro-breaking of Ag filaments occurs through the oxidation of Ag atoms in the secondary dendrites and the drift of Ag+ ions toward the Pt cathode. The Ag doping concentration in the PEO polymer electrolyte determines the bipolar switching characteristics; i.e., the set voltage slightly decreases, while the reset voltage and memory margin greatly increases with the Ag doping concentration.
The learning and inference efficiencies of an artificial neural network represented by a cross‐point synaptic memristor array can be achieved using a selector, with high selectivity (Ion/Ioff) and sufficient death region, stacked vertically on a synaptic memristor. This can prevent a sneak current in the memristor array. A selector with multiple jar‐shaped conductive Cu filaments in the resistive switching layer is precisely fabricated by designing the Cu ion concentration depth profile of the CuGeSe layer as a filament source, TiN diffusion barrier layer, and Ge3Se7 switching layer. The selector performs super‐linear‐threshold‐switching with a selectivity of > 107, death region of −0.70–0.65 V, holding time of 300 ns, switching speed of 25 ns, and endurance cycle of > 106. In addition, the mechanism of switching is proven by the formation of conductive Cu filaments between the CuGeSe and Ge3Se7 layers under a positive bias on the top Pt electrode and an automatic rupture of the filaments after the holding time. Particularly, a spiking deep neural network using the designed one‐selector‐one‐memory cross‐point array improves the Modified National Institute of Standards and Technology classification accuracy by ≈3.8% by eliminating the sneak current in the cross‐point array during the inference process.
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