DOI: 10.5061/dryad.jh9w0vtpv
title: Spectral kernel machines with electrically tunable photodetectors

description: Spectral machine vision collects spectral and spatial information as dense
 3D hypercubes and digitally processes them into scene recognition, which
 causes a data bottleneck, limiting power efficiency, frame rate, and
 spectral-spatial resolution. This work introduces a device architecture
 called spectral kernel machines (SKM) to overcome these bottlenecks. SKM
 directly compresses spectral analysis through the output photocurrent and
 learns from example objects to identify and classify new samples in a
 'sniff-and-seek' mode. We experimentally demonstrated SKM with
 electrically tunable bipolar black phosphorous (bP)-MoS2 photodiodes in
 the near/mid-infrared band and silicon photoconductors in the visible
 band, performing versatile intelligent tasks from chemometrics to
 semiconductor metrology. This architecture consumes significantly lower
 power and is more than an order of magnitude faster than existing
 solutions for hyperspectral image analysis, defining an intelligent
 imaging and sensing paradigm with intriguing possibilities.
description: # Spectral kernel machines with electrically tunable photodetectors
 Dataset DOI:
 [10.5061/dryad.jh9w0vtpv](https://doi.org/10.5061/dryad.jh9w0vtpv) ##
 Description of the data and file structure Data for the main figures of
 the paper entitled "Spectral kernel machines with electrically
 tunable photodetectors". The photocurrent values were measured from a
 silicon photoconductor for the visible and near infrared applications
 (Fig. 2, 3), and from a black phosphorus-MoS2 photodiode for the
 mid-infrared applications (Fig. 4). We used both Arduino and Keysight
 B2901A SMU to measure the readout. The simulation results were produced
 with python codes (Fig. 5). ### Files and variables #### File: Fig_2D.txt
 **Description:** The LED spectra for the spectral encoding. #####
 Variables * Wavelength(nm) * normalized_power_band1 *
 normalized_power_band2 * normalized_power_band3 * normalized_power_band4 *
 normalized_power_band5 * normalized_power_band6 #### File: Fig_2E.txt
 **Description:** The weighted spectrum Q(λ) for highlighting the leaf
 regions. ##### Variables * wavelength (nm) * weighted power #### File:
 Fig_2F.txt **Description:** Q(λ) for highlighting the bird regions. #####
 Variables * wavelength (nm) * weighted power #### File: Fig_2G.txt
 **Description:** photocurrent highlighting the leaf regions. #####
 Variables * 2D photocurrent distribution in unit of nA #### File:
 Fig_2H.txt **Description:** sign of photocurrent in Fig. 2G. #####
 Variables * 2D distribution of boolean variables #### File: Fig_2I.txt
 **Description:** photocurrent highlighting the bird regions. #####
 Variables * 2D photocurrent distribution in unit of nA #### File:
 Fig_2J.txt **Description:** sign of photocurrent in Fig. 2I. #####
 Variables * 2D distribution of boolean variables. #### File: Fig_3B.txt
 **Description:** Photocurrent values for wafer identification. Each pillar
 corresponds to one testing point illustrated in (A) with the same color.
 Regions with 90.5 nm oxide are supposed to show positive current. #####
 Variables * Sample point label * Photocurrent (nA) * Sample point label *
 Photocurrent (nA) #### File: Fig_3C.txt
 **Description:** Photocurrent-based inference of oxide thicknesses. #####
 Variables * Actual oxide thickness (nm) * Expected total photocurrent for
 ideal prediction (nA) * Actual readout photocurrent from test group (nA) *
 Actual oxide thickness (nm) * Actual readout photocurrent from training
 group (nA) #### File: Fig_3F.txt **Description:** SKM results for leaf
 hydration level classification. The photocurrent values are plotted
 against the relative water concentrations of the test leaves never seen
 before. ##### Variables * Relative water concentration * Photocurrent (nA)
 * Relative water concentration * Photocurrent (nA) #### File: Fig_4C.txt
 **Description:** The responsivity calibrated at λ = 1.32 μm. #####
 Variables: * Gate voltage (V) * Responsivity (mA/W) #### File: Fig_4D.txt
 **Description:** The detectivity calibrated with FTIR. ##### Variables *
 Wavelength (um) * Detectivity (Jones) #### File: Fig_4F.txt
 **Description:** The total photocurrent for the C~8~F~18~ sample and
 C~2~H~6~OS sample. ##### Variables * Time (s) * Photocurrent (nA) * Time
 (s) * Photocurrent (nA) #### File: Fig_4H.txt **Description:** The total
 photocurrents, trained at 1 pA every percentage, versus the ground truth
 water concentration in acetone. ##### Variables * Actual concentration (%)
 * Measured concentration (%) after training * Actual concentration (%) *
 Measured concentration (%) before training #### File: Fig_5A.txt
 **Description:** Crop identification with the Salinas Valley hyperspectral
 dataset. ##### Variables * 2D distribution of the output photocurrent
 (arbitrary units). #### File: Fig_5B.mat **Description:** Example SKM
 identification results from a hyperspectral city scene dataset LIB-HSI.
 ##### Variables * 2D distribution of the output photocurrent (arbitrary
 units). #### File: Fig_5C.mat **Description:** Example tumor diagnosis
 results from a hyperspectral human brain dataset. ##### Variables * 2D
 distribution of the output photocurrent (arbitrary units). #### File:
 Fig_5D.txt **Description:** The sensitivity of the classification results
 in (A)-(C), with a linear-kernel support vector machine run in a digital
 processor, a transformer run in a digital processor, and an in-sensor SKM.
 ##### Variables * Position labels * Sensitivity for linear SVM with
 digital processor. * Position labels * Results for transformer with
 digital processor. * Position labels * Results with in-sensor SKM. ####
 File: Fig_5E.txt **Description:** The specificity comparisons. #####
 Variables * Position labels * Specificity for linear SVM with digital
 processor. * Position labels * Results for transformer with digital
 processor. * Position labels * Results with in-sensor SKM. #### File:
 Fig_5F.txt **Description:** Power consumption for capturing and processing
 one hypercube of each dataset. ##### Variables * Position labels * Power
 consumption for linear SVM with digital processor (J/hypercube) * Position
 labels * Results for transformer with digital processor (J/hypercube) *
 Position labels * Results with in-sensor SKM (J/hypercube) #### File:
 Fig_5G.txt **Description:** The latency for capturing and processing one
 hypercube. ##### Variables * Position labels * Latency for linear SVM with
 digital processor (seconds/hypercube) * Position labels * Results for
 transformer with digital processor (seconds/hypercube) * Position labels *
 Results with in-sensor SKM (seconds/hypercube) ## Code/software A MATLAB
 or related Python package is necessary to read the data in Fig. 5B and 5C
 in .mat format. All other data are stored as .txt files.