An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior.

Animal behavior involves complex interactions between physiology and psychology. However, most AI systems neglect psychological factors in decision-making due to a limited understanding of the physiological-psychological connection at the neuronal level. Recent advancements in brain imaging and genetics have uncovered specific neural circuits that regulate behaviors like feeding. By developing neuro-mimetic circuits that incorporate both physiology and psychology, a new emotional-AI paradigm can be established that bridges the gap between humans and machines. This study presents a bio-inspired gustatory circuit that mimics adaptive feeding behavior in humans, considering both physiological states (hunger) and psychological states (appetite). Graphene-based chemitransistors serve as artificial gustatory taste receptors, forming an electronic tongue, while 1L-MoS2 memtransistors construct an electronic-gustatory-cortex comprising a hunger neuron, appetite neuron, and feeding circuit. This work proposes a novel paradigm for emotional neuromorphic systems with broad implications for human health. The concept of gustatory emotional intelligence can extend to other sensory systems, benefiting future humanoid AI.

A Peripheral-Free True Random Number Generator Based on Integrated Circuits Enabled by Atomically Thin Two-Dimensional Materials.

A true random number generator (TRNG) is essential to ensure information security for Internet of Things (IoT) edge devices. While pseudorandom number generators (PRNGs) have been instrumental, their deterministic nature limits their application in security-sensitive scenarios. In contrast, hardware-based TRNGs derived from physically unpredictable processes offer greater reliability. This study demonstrates a peripheral-free TRNG utilizing two cascaded three-stage inverters (TSIs) in conjunction with an XOR gate composed of monolayer molybdenum disulfide (MoS2) field-effect transistors (FETs) by exploiting the stochastic charge trapping and detrapping phenomena at and/or near the MoS2/dielectric interface. The entropy source passes the NIST SP800-90B tests with a minimum normalized entropy of 0.8780, while the generated bits pass the NIST SP800-22 randomness tests without any postprocessing. Moreover, the keys generated using these random bits are uncorrelated with near-ideal entropy, bit uniformity, and Hamming distances, exhibiting resilience against machine learning (ML) attacks, temperature variations, and supply bias fluctuations with a frugal energy expenditure of 30 pJ/bit. This approach offers an advantageous alternative to conventional silicon, memristive, and nanomaterial-based TRNGs as it obviates the need for extensive peripherals while harnessing the potential of atomically thin 2D materials in developing low-power TRNGs.

Graphene Strain-Effect Transistor with Colossal ON/OFF Current Ratio Enabled by Reversible Nanocrack Formation in Metal Electrodes on Piezoelectric Substrates.

Extraordinarily high carrier mobility in graphene has led to many remarkable discoveries in physics and at the same time invoked great interest in graphene-based electronic devices and sensors. However, the poor ON/OFF current ratio observed in graphene field-effect transistors has stymied its use in many applications. Here, we introduce a graphene strain-effect transistor (GSET) with a colossal ON/OFF current ratio in excess of 107 by exploiting strain-induced reversible nanocrack formation in the source/drain metal contacts with the help of a piezoelectric gate stack. GSETs also exhibit steep switching with a subthreshold swing (SS) < 1 mV/decade averaged over ∼6 orders of magnitude change in the source-to-drain current for both electron and hole branch amidst a finite hysteresis window. We also demonstrate high device yield and strain endurance for GSETs. We believe that GSETs can significantly expand the application space for graphene-based technologies beyond what is currently envisioned.

Dielectric Modulated Nanotube Tunnel Field-Effect Transistor as a Label Free Biosensor: Proposal and Investigation.

Dielectric modulated (DM) field-effect transistors (FET) have gained significant popularity for label-free detection of biomolecules. However, the inherent short channel effects limit their sensitivity, scalability and energy-efficiency. Therefore, to realize the true potential of the DMFET based biosensors, in this work, we propose a highly scalable, extremely sensitive and energy-efficient DM nanotube tunnel FET (NT-TFET) biosensor for label-free detection of biomolecules by modifying the structure of the conventional NT-TFET. The modified architecture facilitates the realization of a nanocavity at the source-channel tunneling junction and also provides stability to the immobilized biomolecules. We have performed an extensive analysis of the performance of the proposed DM NT-TFET biosensor in the presence of different representative target biomolecules characterized by different dielectric constants, and/or ionized charge densities using calibrated TCAD simulations. Our results indicate that the proposed DM NT-TFET exhibits an extremely high threshold voltage sensitivity ( SVth ) of 0.44, a high selectivity exceeding four orders of magnitude, ON-state current sensitivity ( SION ) of more than five orders of magnitude and could be a promising alternative to the conventional FET based dielectric modulated biosensors. Moreover, the sensitivity of the proposed DM NT-TFET could be further improved by utilizing alternate source materials with lower bandgap or by probing the transient response of the drain current and exploiting the difference in the settling time for different biomolecules with different dielectric constant ( κ ).

Insect-Inspired, Spike-Based, in-Sensor, and Night-Time Collision Detector Based on Atomically Thin and Light-Sensitive Memtransistors.

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.

A Sparse and Spike-Timing-Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks.

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.

Noise immune dielectric modulated dual trench transparent gate engineered MOSFET as a label free biosensor: proposal and investigation.

We propose and investigate a biosensor based on a transparent dielectric-modulated dual-trench gate-engineered metal-oxide-semiconductor field-effect transistor (DM DT GE-MOSFET) for label-free detection of biomolecules with enhanced sensitivity and efficiency. Various sensing parameters such as the I ON/I OFF ratio and the threshold voltage shift are evaluated as metrics to validate the proposed sensing device. Additionally, S Vth (the V th sensitivity) is also analyzed, considering both positively and negatively charged biomolecules. In addition, radiofrequency (RF) sensing parameters such as the transconductance gain and the cutoff frequency are taken into account to provide further insight into the sensitivity of the proposed device. Furthermore, the linearity, distortion, and noise immunity of the device are evaluated to confirm the overall performance of the biosensor at high (GHz) frequency. The results indicate that the proposed biosensor exhibits a S Vth value of 0.68 for positively charged biomolecules at a very low drain bias of 0.2 V. The proposed device can thus be used as an alternative to conventional FET-based biosensors.

Structural hierarchies define toughness and defect-tolerance despite simple and mechanically inferior brittle building blocks.

Mineralized biological materials such as bone, sea sponges or diatoms provide load-bearing and armor functions and universally feature structural hierarchies from nano to macro. Here we report a systematic investigation of the effect of hierarchical structures on toughness and defect-tolerance based on a single and mechanically inferior brittle base material, silica, using a bottom-up approach rooted in atomistic modeling. Our analysis reveals drastic changes in the material crack-propagation resistance (R-curve) solely due to the introduction of hierarchical structures that also result in a vastly increased toughness and defect-tolerance, enabling stable crack propagation over an extensive range of crack sizes. Over a range of up to four hierarchy levels, we find an exponential increase in the defect-tolerance approaching hundred micrometers without introducing additional mechanisms or materials. This presents a significant departure from the defect-tolerance of the base material, silica, which is brittle and highly sensitive even to extremely small nanometer-scale defects.

Atomistic study of crack-tip cleavage to dislocation emission transition in silicon single crystals.

At low temperatures silicon is a brittle material that shatters catastrophically, whereas at elevated temperatures, the behavior of silicon changes drastically over a narrow temperature range and suddenly becomes ductile. This brittle-to-ductile transition has been observed in experimental studies, yet its fundamental mechanisms remain unknown. Here we report an atomistic-level study of a fundamental event in this transition, the change from brittle cleavage fracture to dislocation emission at crack tips, using the first principles based reactive force field. By solely raising the temperature, we observe an abrupt change from brittle cracking to dislocation emission from a crack within a ≈10 K temperature interval.

Tearing graphene sheets from adhesive substrates produces tapered nanoribbons.

Graphene is a truly two-dimensional atomic crystal with exceptional electronic and mechanical properties. Whereas conventional bulk and thin-film materials have been studied extensively, the key mechanical properties of graphene, such as tearing and cracking, remain unknown, partly due to its two-dimensional nature and ultimate single-atom-layer thickness, which result in the breakdown of conventional material models. By combining first-principles ReaxFF molecular dynamics and experimental studies, a bottom-up investigation of the tearing of graphene sheets from adhesive substrates is reported, including the discovery of the formation of tapered graphene nanoribbons. Through a careful analysis of the underlying molecular rupture mechanisms, it is shown that the resulting nanoribbon geometry is controlled by both the graphene-substrate adhesion energy and by the number of torn graphene layers. By considering graphene as a model material for a broader class of two-dimensional atomic crystals, these results provide fundamental insights into the tearing and cracking mechanisms of highly confined nanomaterials.

Alpha-helical protein networks are self-protective and flaw-tolerant.

Alpha-helix based protein networks as they appear in intermediate filaments in the cell's cytoskeleton and the nuclear membrane robustly withstand large deformation of up to several hundred percent strain, despite the presence of structural imperfections or flaws. This performance is not achieved by most synthetic materials, which typically fail at much smaller deformation and show a great sensitivity to the existence of structural flaws. Here we report a series of molecular dynamics simulations with a simple coarse-grained multi-scale model of alpha-helical protein domains, explaining the structural and mechanistic basis for this observed behavior. We find that the characteristic properties of alpha-helix based protein networks are due to the particular nanomechanical properties of their protein constituents, enabling the formation of large dissipative yield regions around structural flaws, effectively protecting the protein network against catastrophic failure. We show that the key for these self protecting properties is a geometric transformation of the crack shape that significantly reduces the stress concentration at corners. Specifically, our analysis demonstrates that the failure strain of alpha-helix based protein networks is insensitive to the presence of structural flaws in the protein network, only marginally affecting their overall strength. Our findings may help to explain the ability of cells to undergo large deformation without catastrophic failure while providing significant mechanical resistance.