Neuromorphic Synaptic Devices

PCM

Emerging resistive memory technologies are well-developed in the order of MRAM, PCM, and RRAM from a typical memory application perspective. However, in neuromorphic applications, the PCM led to the introduction of new analog synaptic weight elements by identifying and defining new important characteristics (e.g., linearity and symmetry) as well as conventional requirements for the memory functions (e.g., endurance and retention). The resistance of the PCM depends on the crystal structure of the chalcogenide materials such as Ge2Sb2Te5 (GST).[47] In general, it is relatively easy to transmit electrons in a crystalline state, whereas the electrical conductivity is lowered when the structure is transformed to an amorphous state. The two phases can be reversibly changed by first melting the solid-state chalcogenides into a glassy state and subsequently controlling the time required for the ions to be rearranged. To effectively generate heat, a confined electrode serving as a heater is normally used to maximize the current density by reducing the region in which current flows. Applying a pulse that drives a current induces Joule heating, and the phase near the electrode begins to melt, resulting in a mushroom-shaped switching area. Instant cutting off of the pulse satisfies the glassy state of the chalcogenide. It results in a significantly disordered amorphous state, showing a high resistance state (HRS), known as a reset process. Meanwhile, when sufficient time to relocate the ions to a thermodynamically stable position is provided during the molten state, the crystalline state can be formed to obtain a low resistance state (LRS), known as a set process. The analog behavior in the PCM was observed by subdividing and fine-tuning the intermediate pathways that changed from the HRS to LRS, or vice versa. It was possible to experimentally achieve a distinguishable 3-bit state corresponding to the synaptic weight precision by elaborately adjusting the switching current directly related to the volume of the phase transition.[48]
Two important stages are performed in the neuromorphic systems implemented with the cross-point PCM synaptic arrays.[49] In the inference phase, weights predefined from the software or external cloud servers, which is a training (or learning) process, are assigned to each PCM device and mapped to the array to extract the correct value according to input patterns after the VMM execution. The capability of the multiple weights in the PCM allows more numerous and complicated input patterns to be recognized accurately. The accuracy and robustness of the inference are thus related to the state-stability of each state. However, despite the exclusion of the disturbance contributed by accumulative stress induced by the repeated input voltage, the states in the PCM were drifted to the HRS over time due to structural relaxation of the amorphous phase,[48] making it difficult to ensure each state with a reasonable margin of error. To improve state-stability, an additional metallic liner was introduced to mitigate the drift. Consequently, nearly negligible drift and noise reduction were achieved.[50]
In addition to inference accelerators, where the system recognizes and categorizes provided information, there is a demand for the systems to respond in real-time to unknown trends. Because the power consumption is mostly hindered by data movement, the training should be performed within the hardware itself. In the training phase, the synaptic weight within the provided dynamic range of the multilevel states is updated and plays an important role in achieving high recognition accuracy.[49] The resistance in the PCM was freely modulated in both upward and downward directions, but the amplitude of the reset pulse must occasionally be higher than the previous step.[48] Identifying a specific-state first and changing the pulse conditions appropriately became an area overhead in the peripheral circuit and extra burden on its complexity, consuming more power and increasing latency. Therefore, the state should be updated only by the number of identical pulses having similar widths and magnitudes. In the PCM, however, different switching dynamics from crystalline to amorphous or vice versa caused an asymmetric response in the resistance states.[47] When the identical set pulse was applied to the initial HRS of the PCM, the partially crystallized portion expanded in direct proportion to the pulse number. In the situation in which this conductance increase was defined using potentiation, the intermediate states were controllable. Meanwhile, the identical reset pulse applied for depression, which refers to a decrease in conductance, caused a rapid drop in resistance from the LRS and reached the HRS promptly. Specifically, the degree of the change in conductance during the potentiation was initially high during the pulse event and thereafter reduced, resulting in a nonlinear response. This implies that the PCM devices, which have the states close to the LRS in the array, are not trained properly, thereby degrading the recognition accuracy of the system. Moreover, the amount of increase or decrease in any given state of the PCM should be similar because the state of the PCM does not consistently change in a similar direction in the systems. However, due to the weak linearity and symmetry of the PCM, training cannot be effectively conducted.
One of the approaches used to overcome the asymmetric response of the PCM was to only use a potentiation regime that exhibits analogous conductance by periodically resetting (or refreshing) all information to its original state.[51] For this technique, a pair of two PCM elements for positive conductance (G+) and negative conductance (G) comprise a single synaptic device to encode actual weight (w = G+ – G) and also represent its negative value. The weight was increased to a target value by a single step of applying the identical pulses. To lower the weight, depression was performed using a two-step method in which the both PCMs were reset to the initial state. Thereafter, one of the PCMs in the pair, which is responsible for the positive conductance, was only activated again by the pulses while the other PCM representing negative conductance maintained its state. A multilayer perceptron comprising 500 × 661 PCM arrays using the technique has been experimentally implemented.[49] A recognition accuracy of ≈82% was achieved for Modified National Institute of Standards and Technology (MNIST) dataset; however, it was lower than the expected level of 97% due to imperfect PCM device characteristics.