Neuromorphic sensors with in-sensor computing capabilities represent a transformative approach to sensing and processing, inspired by biological neural systems. These devices integrate sensing and computation at the physical level, reducing latency and energy consumption compared to traditional systems that separate sensing and processing. By leveraging materials such as 2D semiconductors, organic polymers, and memristive oxides, neuromorphic sensors mimic the efficiency of biological systems in vision, auditory, and tactile domains.

Vision sensors, often termed retinomorphic sensors, replicate the functionality of the human retina. Traditional image sensors capture raw pixel data, which is then processed externally, consuming significant power. In contrast, retinomorphic sensors perform early-stage processing directly at the sensor level. For example, dynamic vision sensors (DVS) respond only to changes in light intensity, similar to biological retinas, drastically reducing data throughput. Materials like graphene and transition metal dichalcogenides (TMDCs) enable photodetection with high responsivity and tunable bandgaps, facilitating efficient spike-based encoding. These sensors are particularly useful in robotics for real-time object tracking and autonomous navigation, where low latency is critical.

Auditory neuromorphic sensors mimic the cochlea’s function by converting sound waves into spiking signals. Traditional microphones capture continuous waveforms, requiring extensive digital processing. Neuromorphic auditory sensors, however, employ resonant structures or piezoelectric materials to encode sound frequencies directly into spikes. For instance, zinc oxide nanowires exhibit piezoelectric properties that convert mechanical vibrations into electrical spikes, enabling efficient edge processing. Such sensors are valuable in IoT applications, such as voice-activated devices, where power efficiency and rapid response are essential.

Tactile neuromorphic sensors replicate the mechanoreceptors in human skin, detecting pressure, texture, and temperature. Flexible and stretchable materials, including organic semiconductors and conductive polymers, enable these sensors to conform to irregular surfaces. Piezoresistive and capacitive elements convert mechanical stimuli into electrical signals, while in-sensor processing reduces the need for external computation. Applications include robotic prosthetics and human-machine interfaces, where real-time feedback and low power consumption are crucial.

Spike-based encoding is a hallmark of neuromorphic sensors, emulating the way biological neurons communicate. Instead of continuous analog signals, these sensors generate discrete spikes only when input thresholds are exceeded. This event-driven operation minimizes energy use and data redundancy. Memristive materials, such as hafnium oxide and tantalum oxide, enable spike-timing-dependent plasticity (STDP), allowing sensors to adapt their responses based on input patterns. This capability is particularly useful in edge computing scenarios, where sensors must operate autonomously with minimal external intervention.

Edge processing is a critical advantage of neuromorphic sensors. By performing computation locally, these devices reduce the need for data transmission to centralized processors, lowering latency and energy consumption. For example, a vision sensor with embedded convolutional neural networks (CNNs) can detect objects directly at the sensor level, transmitting only relevant information. This approach is beneficial in IoT networks, where bandwidth and power constraints are significant challenges.

Materials play a pivotal role in advancing neuromorphic sensors. Two-dimensional materials like graphene and MoS2 offer high carrier mobility and tunable optoelectronic properties, making them ideal for vision and tactile sensors. Organic semiconductors, such as PEDOT:PSS, provide flexibility and biocompatibility, enabling wearable and implantable applications. Memristive oxides, including TiO2 and WO3, facilitate analog switching and synaptic plasticity, essential for learning and adaptation in-sensor.

Despite their promise, neuromorphic sensors face several challenges. Power efficiency remains a concern, particularly in always-on applications. While spike-based encoding reduces energy use, maintaining low leakage currents in memristive devices is critical. Noise sensitivity is another issue, as stochastic variations in material properties can degrade signal fidelity. Improving material uniformity and device fabrication techniques is essential to address these limitations.

Applications of neuromorphic sensors span robotics, IoT, healthcare, and aerospace. In robotics, these sensors enable real-time perception and decision-making, enhancing autonomy. IoT devices benefit from reduced power consumption and bandwidth requirements, enabling scalable deployments. In healthcare, neuromorphic sensors facilitate advanced prosthetics and diagnostic tools, while aerospace applications include lightweight, low-power sensing for satellites and drones.

The future of neuromorphic sensors lies in overcoming material and integration challenges while expanding their functionality. Advances in heterostructure engineering, such as van der Waals stacking of 2D materials, could yield sensors with unprecedented performance. Additionally, AI-driven optimization of device architectures may further enhance energy efficiency and noise immunity. As these technologies mature, neuromorphic sensors will play an increasingly vital role in the next generation of intelligent systems.