# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
## 2 Emerging Nuclear Sensor Technology
### Neuromorphic Nuclear Sensors  
Neuromorphic devices are a class of hardware systems designed to replicate the functional principles of biological neural networks. Neuromorphic devices aim to emulate the brain's efficiency in processing, learning, and adapting to complex stimuli [48].  
A central element in many neuromorphic systems is the memristor, a device whose resistance can be modified by the history of electrical activity. Memristive devices are typically fabricated using thin films of metal oxides, such as hafnium oxide (HfO\({}_{2}\)), deposited via atomic layer deposition (ALD) [49]. In these devices, precise control over film thickness and stoichiometry is achieved by adjusting ALD parameters [50]. Electrical measurements on memristors reveal distinct switching behavior: the devices transition between high-resistance and low-resistance states when a voltage within the range of 0.8-1.2 V is applied [51]. In fact, these devices can switch reliably for over 10\({}^{4}\) cycles.  
Two-terminal memristors, phase-change materials (PCMs), and three-terminal devices such as electrochemical transistors and ferroelectric transistors are central to neuromorphic designs [52]. This review will touch only on these types of neuromorphic devices, but there are many other types, as seen in Figure 3.  
Phase-change materials (PCMs) such as Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\) (GST) provide an alternative mechanism for storing analog information, especially in optoelectronics [53]. Unlike memristors, which change resistance through ion migration or filament formation, PCMs rely on the reversible phase transition between amorphous and crystalline states [51; 54]. GST films are deposited using techniques such as molecular beam epitaxy (MBE) [55] or sputtering [56], followed by a carefully controlled thermal annealing process that induces the phase change [57]. Measurements using time-resolved electrical characterization show that the transition occurs within approximately 10 ns. The PCM devices are capable of exhibiting multiple discrete conductance levels, a property confirmed by constructing conductance histograms from current/voltage data [58].  
Spiking neural network (SNN) architectures are employed to process the output from these devices. In an SNN, the sensor output is represented by voltage spikes that occur when the input radiation exceeds a defined threshold [59]. The networks incorporate spike-timing-dependent plasticity (STDP) rules, a learning mechanism in which the temporal correlation between spikes adjusts the effective synaptic weights.  
The fabrication of neuromorphic sensor arrays involves advanced lithographic techniques. Electron-beam lithography is commonly used to define patterns for memristors and PCM cells with feature sizes on the order of nanometers [60]. Subsequent processing steps, such as plasma etching [61] and chemical mechanical polishing, are optimized to ensure uniformity across large device areas. Flexible substrates have been employed to demonstrate that neuromorphic sensor arrays can be integrated into conformable platforms [62].  
Figure 3: Short classification of emerging neuromorphic device technology types.  
This integration is particularly important for applications in portable radiation detection or wearable safety devices that can be used in emergency response scenarios.  
Neuromorphic devices have been exposed to gamma radiation from Co-60 sources, with cumulative doses reaching 5-10 kGy [63]. Electrical characterization performed before and after irradiation indicates that the threshold voltages and switching dynamics of both memristors and PCMs remain stable [64].