Passive, Capacitive, Interdigitated Comb Sensors: Towards a Data-Driven, Mechanistic Understanding of Surface Insulation Resistance
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Journal of Creativity (Online)
We present a high-level architecture for how artificial intelligences might advance and accumulate scientific and technological knowledge, inspired by emerging perspectives on how human intelligences advance and accumulate such knowledge. Agents advance knowledge by exercising a technoscientific method—an interacting combination of scientific and engineering methods. The technoscientific method maximizes a quantity we call “useful learning” via more-creative implausible utility (including the “aha!” moments of discovery), as well as via less-creative plausible utility. Society accumulates the knowledge advanced by agents so that other agents can incorporate and build on to make further advances. The proposed architecture is challenging but potentially complete: its execution might in principle enable artificial intelligences to advance and accumulate an equivalent of the full range of human scientific and technological knowledge.
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In this report we detail demonstration of temperature dependent effects on grayscale intensity imaged in Focused Ion Beam (FIB) microscope, as well as secondary electron (SE) dependence on temperature in the Auger Electron Spectroscopy (AES) and a Scanning Electron Microscope (SEM). In each instrument an intrinsic silicon sample is imaged at multiple temperatures over the course of each experiment. The grayscale intensity is shown to scale with sample temperature. Sample preparation procedures are discussed, along with hypothesized explanations for unsuccessful trials. Anticipated outcomes and future directions for these measurements are also detailed.
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