In this study, we present CIMulator, a simulation platform for crossbar arrays based on synaptic element types such as resistive random access memory (RRAM), ferroelectric field-effect transistor (FeFET), and phase change memory (PCM) devices. We have developed a custom-made synapse model for FeFET and adopted non-linear weight update expressions for RRAM and PCM to study the non-ideal behaviors and device variations extensively. To reduce the required distinguishable conductance levels of the devices, advanced methods such as the accumulated weight update method and novel architectures such as 1D1S were employed, resulting in great results with reduced efforts. However, this methodology may not be advantageous for all synaptic devices and at all times. Therefore, we present the most viable neural network solution based on device characteristics. Our results demonstrate that the proposed simulation platform can effectively model analog synaptic devices and provide insight into their non-ideal behaviors and device variations, contributing to the development of efficient and highperformance neuromorphic computing systems.

The versatility of hafnium oxide-based ferroelectric memories to function as a storage class memory, a synaptic device for neuromorphic implementation, and a device capable of high-density integration have made them attractive candidates for next-generation technology. Ferroelectric memories have been increasingly popular with the discovery of ferroelectricity in hafnium oxide at a shallow thickness and compatibility with complementary metal-oxide-semiconductor-compatible processes. The single transistor-based ferroelectric cell makes them ideal for non-volatile embedded memory solutions, bridging the gap between on-chip SRAM and external data storage (e.g. Flash). This paper begins with discovering ferroelectricity in Rochelle salt and discusses the neoteric progress up to successful integration with 3D memory.