Neuromorphic Neuronal Device

The cross-point array of densely arranged analog synapses used for producing VMM results for the inputs discussed in this article represents one of the layers of the neural networks. Identification of the outcome and communications between the arrays is typically conducted via the silicon-based CMOS neuron circuit by converting the weighted sum, in analog manner, to digital bits or spikes. It is discovered that a crucial function of the neuronal device is to turn on and off depending on the inputs, similar to the switch element. Fortunately, the selector serving as the two-terminal switch has been intensively studied and developed for constructing large memory arrays and stacking them in three dimensions.[107] Based on a particular Vth, the current difference of off-state (Roff) and on-state (Ron) of the selector occurred because of several orders of magnitude. This threshold-switching behavior has been demonstrated in Mott insulators such as VO2 and NbO2 that are driven using a metal–insulator transition (MIT) mechanism.[108] Various binary, ternary, and quaternary chalcogenide systems also exhibited the current response known as ovonic threshold switching (OTS) due to a lone-pair electrons in the chalcogen atoms.[109]

Threshold Switching for Integrate and Fire Neurons

Selector with Capacitor for Neuron

When specific conditions are met, the selector supplies high current temporarily, thereby serving as the CMOS circuits in the neuron designed for the fire function. The input spikes, which are related to the amplitude of the weighted sum, were consequently addressed, and charging and discharging at the capacitor occurred repeatedly. No output response was initially detected until the capacitor was completely charged. When the charged voltage of the selector reached the Vth, spike currents began to be generated.[110] The rate of output spike generation can increase and decrease by modulating either input pulse interval or device parameters of the threshold switch such as RoffRon, and Vth related to how sensitively charging and discharging is performed at the given input conditions.
The threshold-switching behavior can be also realized in the aforementioned CBRAM.[112] When the amount of the sources such as Cu (or Ag) ions constituting the filament or the repulsive force between each ion inside the filament was respectively limited or increased, the resultant instability of the filament promoted spontaneous dissolution. This resulted in the CBRAM returning to the off-state when the applied voltage was removed. Using the volatile memory with a Pt/Ag/Ag: SiOx/Ag/Pt structure,[111] the output spike generation adjusted by the spacing and amplitude of the input signals was demonstrated. In addition to the unit neuronal element, a prototype of fully integrated emerging devices based on neuromorphic systems showing the interactions between the nonvolatile RRAM-based synapses and the volatile RRAM-based neurons with capacitors were demonstrated experimentally. To perform an inference task on letter patterns, the synaptic weights were pre-encoded in the 1T–1R device with the Pd/HfO2/Ta structure as we discussed earlier. For each pixel of the pattern, different amplitudes of the input voltage were given and fed into the synapse array. The VMM results were concurrently filtered, activating corresponding neuron properties.  

Capacitor-Less Neuron Design Exploiting Selector

For accumulation that dynamically tracks history of the addressed input signals, the selector-based neuron seems to inherently require the capacitor. Attempts have been made to get rid of the capacitor, and the idea here is to deliberately make the selector devices vulnerable to the external stress using glassy materials for the volatile memory[113] or by strengthening the Joule heating mechanism on the VO2-based selector.[114] Even when a voltage below Vth was applied to the selector, the input pulses were stimulated to migrate the ions to form the filament in the volatile memory or induce the phase transition in the selector. This continued to steadily lower the Roff and eventually turned on the selector, which implied that a single selector could emulate both integrate and fire behaviors. The degree of sensitivity of the accumulation of the damage under stress in the selector with weak immunity determines the integration and timing of fire in this capacitor-less neuron design.
Meanwhile, the progressive evolution of the HRS and its reach to the LRS in the nonvolatile PCM[115] and FeFET[116] observed during the potentiation have achieved integration and fire functions. However, at the cost of the nonvolatility of the memories, the reset process to restore the initial state corresponding to the HRS for the next neuronal function should be processed periodically with additional circuitry. Accordingly, the MRAM has been proposed as an alternative.[117] The binary resistance of the MRAM was reversibly changed through spin-torque transfer. However, during normal operation, the LRS unexpectedly returned to the HRS due to a back-hopping oscillation, which was considered as one of the failure mechanisms. Therefore, the switching on and off was regularly observed at the specified pulse. Although the obtained frequency of the on/off switching was a stochastic and probabilistic, frequency was discovered to be proportional to the output current intensity. The 4-bit precision that can be distinguished by the MRAM-based neuron without a capacitor and reset circuit achieved an accuracy of 82% to be obtained for the CIFAR-10 image recognition.

Threshold Switching for Oscillation Neurons

The fired output can be represented in different ways. When the NbO2-based selector was connected to a load resistor, where the load resistance (Rload) is in between Roff and Ron of the selector (i.e., Roff > Rload > Ron), in a voltage divider configuration, an oscillation in voltage was monitored in real time.[118-121] Most of the voltage was initially applied to the selector because the Roff of the selector was greater than the Rload. As the charged voltage at the selector exceeded the Vth, the off-state of the selector was rapidly switched to the on-state. Because the Ron of the selector was now lowered, the voltage began to discharge until the voltage remaining on the selector reached a hold voltage (Vhold), which is the minimum driving force required to maintain the on-state, below which the on-state of the selector was promptly switched to the off-state. The reversible transition of the selector repeatedly induced the back-and-forth voltage charging and discharging, causing an oscillation with a specific frequency in the range of Vhold and Vth. Taking one step forward based on the single oscillation neuron with an off-ship discrete load resistor, an 1D 12 × 1 crossbar array that structurally resembles a column of the weight matrix, where one neuron is connected with multiple synapses in parallel for on-chip integration, has been demonstrated, as shown in Figure 5.[122] The single input pulse was delivered to only one of the RRAM-based synapses, and the remaining synapses were floating. The input pulse multiplied by the conductance at the selected RRAM was expected to be observed as a read-out current along the BL at the NbO2-based neuron, resulting in an oscillation with a slow frequency of 110 kHz. When more input vectors were loaded into the multiple rows of the synaptic array, larger amounts of the weights were summed along the BL, resulting in a larger read-out current corresponding to the equivalently reduced total resistance. Faster oscillation frequency was measured to be proportional to the analog column current. This compact neuron facilitated the number of synaptic columns shared by one neuron to be reduced, thereby outperforming the conventional silicon neuronal circuit in latency, area, and energy consumption.