3D printed neuromorphic sensing systems
Bibliographic record
Abstract
Thanks to the high energy efficiency, neuromorphic devices are spotlighted recently by mimicking the calculation principle of the human brain through the parallel computation and the memory function.Various bio-inspired 'in-memory computing' (IMC) devices were developed during the past decades, such as synaptic transistors for artificial synapses.By integrating with specific sensors, neuromorphic sensing systems are achievable with the bio-inspired signal perception function.A signal perception process is possible by a combination of stimuli sensing, signal conversion/transmission, and signal processing.However, most neuromorphic sensing systems were demonstrated without signal conversion/transmission functions.Therefore, those cannot fully mimic the function provides by the sensory neuron in the biological system.This thesis aims to design a neuromorphic sensing system with a complete function as biological sensory neurons.To reach such a target, 3D printed sensors, electrical oscillators, and synaptic transistors were developed as functions of artificial receptors, artificial neurons, and artificial synapses, respectively.Moreover, since the 3D printing technology has demonstrated a facile process due to fast prototyping, the proposed 3D neuromorphic sensing system was designed as a 3D integrated structure and fabricated by 3D printing technologies.A novel multi-axis robot 3D printing system was also utilized to increase the fabrication efficiency with the capability of printing on vertical and tilted surfaces seamlessly.Furthermore, the developed 3D neuromorphic system was easily adapted to the application of tactile sensing.A portable neuromorphic system was integrated with a tactile sensing system for the intelligent tactile sensing application of the humanoid robot.Finally, the bio-inspired reflex arc for the unconscious response was also demonstrated by training the neuromorphic tactile sensing system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".