The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that information in the brain is more often represented by explicit firing times of the neurons rather than mean firing rates, it is imperative to develop novel hardware that can accelerate sparse and spike‐timing‐based encoding. Here a medium‐scale integrated circuit composed of two cascaded three‐stage inverters and one XOR logic gate fabricated using a total of 21 memtransistors based on photosensitive 2D monolayer MoS2 for spike‐timing‐based encoding of visual information, is introduced. It is shown that different illumination intensities can be encoded into sparse spiking with time‐to‐first‐spike representing the illumination information, that is, higher intensities invoke earlier spikes and vice versa. In addition, non‐volatile and analog programmability in the photoencoder is exploited for adaptive photoencoding that allows expedited spiking under scotopic (low‐light) and deferred spiking under photopic (bright‐light) conditions, respectively. Finally, low energy expenditure of less than 1 µJ by the 2D‐memtransistor‐based photoencoder highlights the benefits of in‐sensor and bioinspired design that can be transformative for the acceleration of SNNs.
Detecting a potential collision at night is a challenging task owing to the lack of discernible features that can be extracted from the available visual stimuli. To alert the driver or, alternatively, the maneuvering system of an autonomous vehicle, current technologies utilize resource draining and expensive solutions such as light detection and ranging (LiDAR) or image sensors coupled with extensive software running sophisticated algorithms. In contrast, insects perform the same task of collision detection with frugal neural resources. Even though the general architecture of separate sensing and processing modules is the same in insects and in image-sensor-based collision detectors, task-specific obstacle avoidance algorithms allow insects to reap substantial benefits in terms of size and energy. Here, we show that insect-inspired collision detection algorithms, when implemented in conjunction with in-sensor processing and enabled by innovative optoelectronic integrated circuits based on atomically thin and photosensitive memtransistor technology, can greatly simplify collision detection at night. The proposed collision detector eliminates the need for image capture and image processing yet demonstrates timely escape responses for cars on collision courses under various real-life scenarios at night. The collision detector also has a small footprint of ∼40 μm2 and consumes only a few hundred picojoules of energy. We strongly believe that the proposed collision detectors can augment existing sensors necessary for ensuring autonomous vehicular safety.
Intrinsically ferromagnetic and semiconducting two-dimensional (2D) H-phase vanadium disulfide (VS2) holds tremendous promise for future electronics, optoelectronics, spintronics and valleytronics applications. However, its thermodynamic instability and the formation of intermediate stoichiometric polymorphs during its growth have stymied any progress towards synthesis of high quality 2D VS2 films. In this article, we circumvent these challenges and accomplish large area growth of monolayer VS2 films using atmospheric pressure chemical vapor deposition (APCVD) technique. By incorporating excess sulfur during the growth process which suppresses the formation of intermediate compounds, good quality large-area VS2 film can be synthesized. Furthermore, the electronic and optoelectronic properties of VS2 were explored by fabricating photosensitive memtransistor devices, which reveal an n-type carrier transport along with a high responsivity to red, green, and blue wavelengths of light. In addition the device exhibited multiple non-volatile conductance states through electrical programming. To the best of our knowledge, this is the first comprehensive report on memtransistors built from large area grown H-phase VS2 that integrate compute, sense, and storage functionalities in a single device.
Collision detection under poor illumination and nightly conditions pose significant challenge for manned and unmanned vehicles, flying drones, and robots navigating complex terrestrial and extraterrestrial geographies. Existing night vision cameras offer solution based on sophisticated enhancement algorithms or additional thermal sensors necessitating extensive and expensive hardware, which make them power-hungry and untenable for deployment in remote and resource constrained locations. In contrast, nocturnal flying insects can avoid collision using very limited neural resources. Insect-inspired collision detectors based on silicon complementary metal oxide semiconductor technology and field programmable gate arrays also exist, however, the physical separation between sensing and compute and absence of spike-based information processing capability increases their area and energy overhead. Here, we introduce an insect-inspired, spike-based, and in-sensor collision detector using a reconfigurable optoelectronic integrated circuit constructed based on atomically-thin and light-sensitive memtransistors. We imitate the escape response of lobula giant movement detector (LGMD) neuron found in many insect species and demonstrate timely collision detection for various real-life scenarios at night involving cars on collision course. Our collision detector has a small effective footprint of 40 µm2 and consumes miniscule energy of few hundreds pico-Joules.
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