Thermal effects in materials have emerged as a promising avenue for neuromorphic computing, offering unique mechanisms for emulating biological neural networks. Among the most studied phenomena are insulator-to-metal transitions (IMTs), exhibited by materials such as vanadium dioxide (VO₂) and phase-change alloys like Ge₂Sb₂Te₅ (GST). These materials undergo reversible changes in electrical resistance in response to temperature variations, enabling energy-efficient synaptic and neuronal functionalities. The interplay between thermal dynamics and electronic properties provides a rich framework for designing adaptive, brain-inspired computing systems.

Vanadium dioxide is a prototypical material for IMT-based neuromorphic devices. At around 68°C, VO₂ transitions from an insulating monoclinic phase to a metallic rutile phase, accompanied by a sharp drop in resistivity. This transition can be triggered not only by global heating but also by localized Joule heating or optical excitation, making it suitable for spiking neuron models. The hysteresis in VO₂'s resistance-temperature curve allows for history-dependent behavior, analogous to synaptic plasticity. Experimental studies have demonstrated that VO₂-based oscillators can replicate neuronal spiking and bursting patterns with nanosecond-scale switching times and energy consumption as low as a few femtojoules per spike. The abrupt resistance change also enables binary switching for artificial synapses, where thermal pulses modulate the conductance states.

Phase-change materials (PCMs) like GST offer complementary advantages for neuromorphic applications. Unlike VO₂, which relies on a structural phase transition, PCMs switch between amorphous and crystalline states through controlled heating and cooling. The amorphous phase exhibits high resistance, while the crystalline phase is conductive, allowing for multilevel conductance states by partial crystallization. This property is exploited for analog synaptic weight updates in training artificial neural networks. The energy efficiency of PCM devices stems from their non-volatility; once programmed, the state persists without additional power. Switching energies can reach sub-picojoule levels, with endurance exceeding 10¹² cycles in optimized devices. Thermal confinement techniques, such as embedding PCMs in nanoscale heater structures, further reduce energy dissipation and improve switching speed.

A critical challenge in thermal neuromorphic systems is mitigating thermal crosstalk, where unintended heat propagation interferes with adjacent devices. In densely integrated arrays, thermal diffusion can lead to erroneous switching or reduced precision in conductance modulation. Several strategies address this issue. One approach involves engineering thermal barriers using materials with low thermal conductivity, such as silicon dioxide or porous dielectrics, to isolate individual devices. Another method leverages pulse shaping, where the duration and amplitude of heating pulses are optimized to confine thermal effects spatially. For example, sub-nanosecond pulses with high peak power can trigger localized transitions without significant heat spread. Computational modeling has shown that thermal crosstalk can be reduced by over 80% through these design optimizations.

Environment-adaptive computing is a key application area for thermal neuromorphic materials. VO₂-based devices exhibit sensitivity to ambient temperature, enabling hardware that dynamically adjusts its operation based on environmental conditions. For instance, a neuromorphic sensor array could autonomously recalibrate its firing thresholds in response to temperature fluctuations, mimicking biological homeostasis. Phase-change materials, on the other hand, enable adaptive memory hierarchies where frequently accessed data is stored in low-resistance crystalline regions for fast retrieval, while less critical data resides in high-resistance amorphous domains. This intrinsic adaptability reduces the need for external control circuitry, simplifying system architecture.

Thermal management is another domain where these materials excel. Neuromorphic systems based on IMT materials can incorporate self-regulating thermal feedback loops. VO₂'s negative differential resistance region allows it to act as a thermal oscillator, maintaining stable operating temperatures without external cooling systems. Similarly, PCMs can be used as thermal switches, modulating heat flow in response to local activity. This capability is particularly valuable for edge computing devices operating in variable environments, where traditional cooling methods are impractical. Experimental implementations have demonstrated temperature stabilization within ±1°C using integrated VO₂ oscillators, significantly improving device reliability.

The scalability of thermal neuromorphic systems depends on material uniformity and fabrication precision. Advances in atomic layer deposition and nanolithography have enabled the production of VO₂ thin films with transition temperatures tunable within ±2°C across wafers. For PCMs, doping strategies with elements like nitrogen or carbon enhance thermal stability and reduce device-to-device variability. Large-scale integration has been demonstrated in crossbar arrays with 1,024 VO₂ neurons and 32,768 PCM synapses, showing feasibility for practical applications.

Future directions include exploring hybrid systems combining IMT materials and PCMs to leverage their respective strengths. For example, VO₂ could handle fast neuronal spiking, while PCMs store synaptic weights, creating a more biologically plausible architecture. Another avenue is the integration of these materials with photonic interconnects, using light for communication and heat for computation, thereby minimizing electronic noise and crosstalk. Research is also progressing toward room-temperature operation of IMT materials through strain engineering and alloying, expanding their applicability beyond specialized environments.

Thermal neuromorphic materials represent a convergence of solid-state physics, materials science, and computational engineering. Their ability to harness intrinsic material properties for brain-inspired computing offers a path toward energy-efficient, adaptive, and resilient hardware. As fabrication techniques mature and understanding of thermal transport at nanoscales deepens, these materials are poised to play a pivotal role in next-generation computing paradigms.