Highly parallel and ultra-low-power probabilistic reasoning with programmable gaussian-like memory transistors.

Probabilistic inference in data-driven models is promising for predicting outputs and associated confidence levels, alleviating risks arising from overconfidence. However, implementing complex computations with minimal devices still remains challenging. Here, utilizing a heterojunction of p- and n-type semiconductors coupled with separate floating-gate configuration, a Gaussian-like memory transistor is proposed, where a programmable Gaussian-like current-voltage response is achieved within a single device. A separate floating-gate structure allows for exquisite control of the Gaussian-like current output to a significant extent through simple programming, with an over 10000 s retention performance and mechanical flexibility. This enables physical evaluation of complex distribution functions with the simplified circuit design and higher parallelism. Successful implementation for localization and obstacle avoidance tasks is demonstrated using Gaussian-like curves produced from Gaussian-like memory transistor. With its ultralow-power consumption, simplified design, and programmable Gaussian-like outputs, our 3-terminal Gaussian-like memory transistor holds potential as a hardware platform for probabilistic inference computing.

Advanced Electrochemical Reamer (EC-Reamer) for Root Canal Treatment.

OBJECTIVE: According to the American Association of Endodontists (AAE), 22 million endodontic procedures have been performed annually. Root canal treatment is needed to prevent infection and restore function when a tooth is severely infected or decayed. This procedure is the only way to preserve the natural tooth and avoid artificial replacement (implant, denture, etc.). The current study aims to develop an electrochemical reamer (EC-Reamer or EC-R) that can help to disinfect the canal system and thus improve the success rate of root canal treatment. METHODS: The COMSOL Multiphysics software was utilized to simulate the experimental setup and confirm the current flow in the electrolyte. The benchtop experimental approach follows a specific electrochemical protocol, (i) open circuit potential to monitor the electrochemical stabilization and (ii) potentiostatic scan at -9.0 V as the treatment stage. Identification of feasible reference electrode (RE) and insulation material for the exploratory benchtop studies considered platinum (Pt) and gold (Au) wire as the REs and hot melt adhesive (HMA) and liquid tape as the insulation materials. The antimicrobial effects of EC-R were analysed using Enterococcus faecalis (E. faecalis). One-way ANOVA with the Tukey post hoc test and a significance level of P<0.05 is used to compare the groups with an experimental duration of 60 seconds. RESULTS: The findings showed that magnitude and current fluctuations created by Pt wire are promising when compared to Au wire, while Pt-HMA pair is chosen considering Pt's good electrochemical inertness and HMA's easy handling, availability, and non-hazardous features. The use of potentiostatic duration of 1 s and 3 s resulted in >99.99% E. faecalis reduction. Duration at 5 s and above resulted in a total bacterial kill. Statistical analysis confirmed a significant difference among the groups tested with commercial and custom-built potentiostats. CONCLUSION: The outcome provided preliminary data for developing an EC-R prototype to enhance the antimicrobial effect during root canal treatment potentially. (EEJ-2022-01-04).

Reconfigurable MoS2 Memtransistors for Continuous Learning in Spiking Neural Networks.

Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS2 grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response. Inspired by biological systems, gate pulses are used to modulate potentiation and depression, resulting in diverse learning curves and simplified spike-timing-dependent plasticity that facilitate unsupervised learning in simulated spiking neural networks. This capability also enables continuous learning, which is a previously underexplored cognitive concept in neuromorphic computing. Overall, this work demonstrates that the reconfigurability of memtransistors provides unique hardware accelerator opportunities for energy efficient artificial intelligence and machine learning.

Bayesian reasoning machine on a magneto-tunneling junction network.

The recent trend in adapting ultra-energy-efficient (but error-prone) nanomagnetic devices to non-Boolean computing and information processing (e.g. stochastic/probabilistic computing, neuromorphic, belief networks, etc) has resulted in rapid strides in new computing modalities. Of particular interest are Bayesian networks (BN) which may see revolutionary advances when adapted to a specific type of nanomagnetic devices. Here, we develop a novel nanomagnet-based computing substrate for BN that allows high-speed sampling from an arbitrary Bayesian graph. We show that magneto-tunneling junctions (MTJs) can be used for electrically programmable 'sub-nanosecond' probability sample generation by co-optimizing voltage-controlled magnetic anisotropy and spin transfer torque. We also discuss that just by engineering local magnetostriction in the soft layers of MTJs, one can stochastically couple them for programmable conditional sample generation as well. This obviates the need for extensive energy-inefficient hardware like OP-AMPS, gates, shift-registers, etc to generate the correlations. Based on the above findings, we present an architectural design and computation flow of the MTJ network to map an arbitrary Bayesian graph where we develop circuits to program and induce switching and interactions among MTJs. Our discussed framework can lead to a new generation of stochastic computing hardware for various other computing models, such as stochastic programming and Bayesian deep learning. This can spawn a novel genre of ultra-energy-efficient, extremely powerful computing paradigms, which is a transformational advance.