AI-Optimized Vanadium Oxide Multilayers for More Than 20-fold Enhancement in Bolometric Performance

Phase-transition materials such as vanadium dioxide (VO₂) inherently exhibit non-linear and hysteretic behavior, which limits their applicability in devices like infrared bolometric sensors that require linear and non-hysteretic responses. To circumvent this issue, nonstoichiometric VO_x has been widely used in infrared bolometers despite its degraded phase transitions and resultant lower temperature coefficient of resistance (TCR) compared to stoichiometric VO₂. Achieving both a high TCR and a linear, non-hysteretic response has therefore remained a major bottleneck in advancing microbolometer technology. In this study, we present a multilayer approach using machine-learning-optimized W_xV_1-xO_y thin films with varying doping ratios to address these challenges. By stacking layers with different W doping levels and employing genetic algorithm optimization, we achieve tailored linear/flat TCR profiles and significantly reduced hysteresis. These multilayer systems simultaneously achieve a high TCR and low electrical noise even under complementary metal-oxide semiconductor (CMOS)-compatible growth conditions, resulting in a universal bolometric performance 23.6 times greater than that of commercial materials. This work demonstrates a general methodology for achieving both a large and linear response to external stimuli, which can be widely adopted not only for microbolometers but also for other technologies.