Effect of Carrier Transport Process on Tunneling Electroresistance in Ferroelectric Tunnel Junction

Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric‐Based Neuromorphic System

“…This is because these noise types originate from random processes generated by carrier trapping or fluctuations in barrier height, and their behavior is influenced by various defects unique to each process. [40][41][42][43][44][45][46][47][48][49][62][63][64][65] Furthermore, the 𝛾 value of FTJs is close to one regardless of the area. In addition, S IT /I T 2 increases with device downscaling.…”

“…Subsequently, numerous studies have reported the possibility of implementing neuromorphic systems using HfO 2 -FTJs. [22][23][24][25] To implement neuromorphic systems based on electrical devices such as HfO 2 -FTJ, reliability is a crucial challenge that limits the performance and accuracy of large-scale arrays. Therefore, several studies have analyzed the impact of device reliability, such as nonlinearity, asymmetry, dynamic range, endurance, retention, read/write disturbances, and variations in neuromorphic systems.…”

“…However, there is variation due to device intrinsic read noise, such as thermal noise, 1/f-like noise, and random telegraph noise (RTN), which is classified as dynamic noise because it changes over time. [40][41][42][43][44][45][46][47][48][49] Because different neuromorphic system structures have different sensitivities to each variation, careful consideration of the system architecture is required to evaluate the impact of these variations on neuromorphic systems. [29] Neuromorphic systems can be classified into two categories based on their training methods.…”

“…[10,14,18] In contrast, ferroelectric tunnel junctions (FTJs), which have the advantages of fast operation speed, low current density, high reliability, process simplicity, and high integration (4F 2 , F: Minimum feature size), are considered the strongest candidates for implementing synapses in neuromorphic computing systems. [20][21][22][23][24][25] This movement gained significant traction following the breakthrough discovery of ferroelectricity in hafnium oxide (HfO 2 ) thin films in 2011, which was fully compatible with conventional CMOS processes. Subsequently, numerous studies have reported the possibility of implementing neuromorphic systems using HfO 2 -FTJs.…”

Breaking the bottleneck of lead-free perovskite solar cells through dimensionality modulation

“…Inspired by perovskites, rudorffites are also considered potential candidates for photovoltaic application. 193,199,205,206 Rudorffites have the chemical formula of Ag x Bi y I x +3 y and their tunable compositions result in various symmetries, such as R 3̄ m for the Ag-rich compound and Fd 3̄ m for the Bi-rich counterpart. 191,207,208 However, despite the suitable direct bandgaps achieved in some compounds, 188,195,201,209 the current PCE of rudorffite solar cells 23,195 still falls short of the expectation (Table 4).…”