Phosphorene, a monolayer or few-layer form of black phosphorus, has emerged as a promising candidate for neuromorphic computing applications due to its unique electronic and structural properties. Unlike conventional silicon-based electronics, neuromorphic devices aim to mimic the brain’s neural architecture, enabling energy-efficient, parallel processing. Phosphorene’s anisotropic carrier transport, tunable bandgap, and inherent defects make it particularly suitable for memristive switching and synaptic plasticity, two critical functionalities in neuromorphic systems. This article explores phosphorene’s potential in neuromorphic devices, comparing its performance with oxide-based and organic alternatives while addressing challenges such as variability and endurance.

Memristive switching is a fundamental mechanism for neuromorphic devices, enabling resistive memory that can emulate synaptic weight changes. Phosphorene exhibits non-volatile resistive switching due to its intrinsic defects, such as vacancies and edge states, which can trap charges and modulate conductivity. Studies have demonstrated that phosphorene-based memristors achieve switching ratios exceeding 10^3, comparable to oxide-based memristors like HfO2 or Ta2O5. However, phosphorene’s switching mechanism differs significantly. While oxide-based devices rely on filamentary conduction or oxygen vacancy migration, phosphorene’s switching is attributed to charge trapping in defect states or interfacial effects in heterostructures. This difference offers advantages in terms of energy consumption. Phosphorene memristors operate at lower voltages, typically below 1 V, reducing power dissipation compared to oxides, which often require higher forming voltages.

Synaptic plasticity, the ability of synapses to strengthen or weaken over time, is another critical feature for neuromorphic computing. Phosphorene’s anisotropic conductivity allows for precise control of synaptic weights, emulating both short-term and long-term plasticity. For instance, phosphorene-based synapses can replicate spike-timing-dependent plasticity (STDP), a biological learning rule, with high fidelity. The material’s sensitivity to external stimuli, such as electric fields or strain, further enhances its adaptability. In contrast, oxide-based synapses often suffer from abrupt conductance changes, making it challenging to achieve gradual weight updates. Organic materials, while flexible and biocompatible, typically exhibit slower switching speeds and poorer endurance compared to phosphorene.

Energy efficiency is a key advantage of phosphorene in neuromorphic applications. The material’s low switching energy, on the order of femtojoules per spike, is competitive with state-of-the-art oxide and organic devices. This efficiency stems from phosphorene’s thin atomic structure and low defect formation energy, which minimize energy losses during operation. Oxide-based devices, although robust, often require higher energy due to their thicker active layers and higher operating voltages. Organic materials, while energy-efficient in some cases, struggle with stability under prolonged cycling, limiting their practicality.

Despite these advantages, phosphorene faces several challenges that must be addressed for widespread adoption in neuromorphic computing. Variability in device performance is a significant issue, arising from inhomogeneous defect distributions and environmental sensitivity. Phosphorene degrades rapidly under ambient conditions due to oxidation, necessitating encapsulation or passivation strategies. Endurance is another concern; while phosphorene memristors can achieve thousands of switching cycles, they still lag behind oxide-based devices, which routinely endure millions of cycles. Improving material quality and developing better interface engineering techniques are essential to overcome these limitations.

Comparing phosphorene with oxide-based and organic neuromorphic materials reveals a nuanced landscape. Oxide-based devices, such as those using HfO2 or Ta2O5, offer excellent endurance and scalability, making them suitable for large-scale integration. However, their high operating voltages and filamentary switching mechanisms introduce reliability issues. Organic materials, including conjugated polymers and small molecules, provide flexibility and low-cost fabrication but suffer from inferior switching speeds and environmental instability. Phosphorene occupies a middle ground, combining the speed and efficiency of inorganic materials with the potential for tunability and integration into flexible substrates.

Research into phosphorene-based neuromorphic devices is still in its early stages, but progress is promising. Recent studies have demonstrated multi-level resistive switching, a prerequisite for analog computing, in phosphorene memristors. The material’s compatibility with van der Waals heterostructures also opens avenues for novel device architectures, such as all-2D neuromorphic circuits. Additionally, phosphorene’s strain-sensitive properties could enable mechanically reconfigurable synapses, adding another dimension to neuromorphic functionality.

In conclusion, phosphorene holds significant potential for neuromorphic computing, particularly in memristive switching and synaptic plasticity. Its energy efficiency, tunable properties, and compatibility with emerging technologies position it as a strong contender against oxide-based and organic materials. However, challenges related to variability, endurance, and environmental stability must be resolved to unlock its full potential. As research advances, phosphorene could play a pivotal role in the development of next-generation neuromorphic systems, bridging the gap between biological and artificial intelligence.