The development of neuromorphic computing materials has gained significant traction as the demand for energy-efficient, real-time processing at the edge intensifies. These materials enable on-device learning and inference, reducing reliance on cloud-based systems and addressing latency, privacy, and power constraints. Key requirements for such materials include robustness to environmental variations, compatibility with existing fabrication processes, and the ability to deliver low-latency responses. Applications span autonomous systems, embedded AI, and adaptive edge devices, where rapid decision-making is critical.

A primary class of materials for neuromorphic computing includes memristive oxides, such as hafnium oxide (HfO₂) and tantalum oxide (Ta₂O₅). These materials exhibit resistive switching behavior, mimicking synaptic plasticity in biological systems. Hafnium oxide, for instance, has demonstrated endurance exceeding 10¹² cycles and switching speeds below 10 ns, making it suitable for high-frequency neuromorphic operations. Its compatibility with CMOS fabrication allows seamless integration into existing semiconductor workflows. Environmental stability is another advantage, with HfO₂-based devices maintaining performance across a temperature range of -40°C to 125°C, a necessity for automotive and industrial applications.

Phase-change materials (PCMs) like germanium-antimony-tellurium (GST) alloys are also prominent due to their non-volatile memory characteristics. GST exhibits a rapid transition between amorphous and crystalline states, enabling analog resistance modulation for synaptic weights. Recent studies show that GST-based devices achieve programming energies as low as 10 pJ per operation, critical for energy-constrained edge devices. However, their sensitivity to thermal fluctuations necessitates careful thermal management in embedded systems.

Organic semiconductors offer flexibility and low-temperature processing, making them ideal for wearable and implantable neuromorphic systems. Conjugated polymers such as PEDOT:PSS demonstrate tunable conductivity through electrochemical doping, emulating synaptic behavior. These materials operate at voltages below 1 V, reducing power consumption. Their mechanical flexibility allows integration into unconventional form factors, though long-term stability under humidity and UV exposure remains a challenge. Advances in encapsulation techniques have improved operational lifetimes to over 10,000 hours in controlled environments.

Two-dimensional materials, particularly transition metal dichalcogenides (TMDCs) like MoS₂, provide atomically thin channels for low-power neuromorphic transistors. MoS₂-based devices exhibit high carrier mobility (>100 cm²/Vs) and strong electrostatic control, enabling sub-1V operation. Their inherent robustness to radiation and temperature variations makes them suitable for aerospace applications. Heterostructures combining TMDCs with hexagonal boron nitride (hBN) further enhance performance by reducing interface traps, leading to more stable synaptic responses.

Ferroelectric materials, including hafnium zirconium oxide (HfZrO₂), are gaining attention for their polarization-based memory and low-power switching. These materials exhibit endurance of over 10¹⁰ cycles and sub-nanosecond switching speeds. Their compatibility with CMOS back-end-of-line (BEOL) processing allows 3D integration, increasing synaptic density for complex neural networks. Ferroelectric field-effect transistors (FeFETs) have demonstrated inference accuracies exceeding 95% on standard machine learning benchmarks, rivaling conventional digital accelerators while consuming orders of magnitude less energy.

Spin-based materials, such as magnetic tunnel junctions (MTJs), leverage spintronic phenomena for neuromorphic computing. CoFeB/MgO-based MTJs exhibit stochastic switching behavior, useful for probabilistic computing and noise-resilient learning. These devices operate at room temperature with switching energies below 100 fJ, though their scalability remains limited compared to oxide-based alternatives. Research is ongoing to improve their endurance and integration density for large-scale neural networks.

For autonomous systems, robustness to environmental perturbations is non-negotiable. Materials like silicon carbide (SiC) and gallium nitride (GaN) provide inherent resilience to high temperatures, radiation, and mechanical stress. SiC-based neuromorphic circuits have been demonstrated in automotive applications, where they maintain functionality under harsh operating conditions. GaN’s high electron mobility (>2000 cm²/Vs) enables fast switching, crucial for real-time sensor processing in drones and robotics.

Fabrication compatibility is another critical consideration. Materials that require minimal deviation from standard semiconductor processes accelerate adoption. Hafnium oxide and ferroelectric HfZrO₂ are particularly advantageous, as they utilize existing deposition techniques like atomic layer deposition (ALD). Organic and 2D materials, while promising, often necessitate specialized handling, increasing production complexity. Hybrid approaches, such as integrating organic materials with silicon substrates, offer a middle ground, balancing performance with manufacturability.

Low-latency operation is essential for edge AI applications, where delays can compromise system reliability. Memristive and ferroelectric materials excel here, with response times in the nanosecond range. Phase-change materials, while slightly slower due to thermal dynamics, still meet the requirements of many real-time applications. Spin-based devices, though fast, face challenges in achieving deterministic switching at scale.

In embedded AI, energy efficiency is paramount. Materials that enable analog in-memory computing reduce data movement penalties, a major energy bottleneck. Memristive crossbar arrays, for instance, have demonstrated matrix-vector multiplication at energy efficiencies below 1 pJ per operation. This capability is transformative for always-on devices, such as smart sensors and IoT nodes, where battery life is a critical constraint.

The future of neuromorphic materials lies in co-designing devices, circuits, and algorithms to maximize synergies. Emerging trends include the use of multi-functional materials that combine memory, sensing, and computation in a single element. For example, materials with tunable optical properties could enable vision sensors that pre-process visual data at the pixel level, reducing bandwidth requirements. Similarly, materials with inherent noise resilience could simplify error correction in stochastic computing paradigms.

In summary, the advancement of materials for on-device learning and inference hinges on balancing performance, robustness, and manufacturability. Memristive oxides, phase-change materials, organics, 2D layers, ferroelectrics, and spin-based systems each offer unique advantages for different edge computing scenarios. As fabrication techniques mature and hybrid material systems evolve, the potential for intelligent, adaptive edge devices will expand, enabling a new generation of autonomous and embedded AI applications.