The diffuser and the diffractive layer used for the experimental demonstration were fabricated using a 3D printer (Pr 110, CADworks3D). The 3D printing material we used in the experiments has wavelength-dependent absorption. Therefore, additional neuron height-dependent amplitude modulations were applied to the incident light, which can be formulated as
where
is the extinction coefficient of the diffractive layer material, corresponding to the imaginary part of the complex-valued refractive index
, i.e.,
.
For the single-layer single-pixel diffractive model used for the experimental demonstration (Fig.
7), the diffractive layer consists of 120 × 120 diffractive neurons, each with a lateral size of 0.4 mm. The axial separation between any two consecutive planes was set to
d = 20 mm. To compensate for the nonideal wavefront generated by the THz emitter, a square input aperture with a size of 8 × 8 mm
2 was used as an entrance pupil to illuminate the input object, placed 20 mm away from it. The diffraction of this aperture was also included in the forward propagation model. The size of the input objects was designed as 20 × 20 mm
2 (50 × 50 pixels). After being distorted by the random diffuser and modulated by the diffractive layer, the spectral power at the center region (2.4 × 2.4 mm
2) of the output plane was measured to determine the class score.
To overcome potential mechanical misalignments during the experimental testing, the network was “vaccinated” with deliberate random displacements during the training stage
53 (link). Specifically, a random lateral displacement
was added to the diffractive layer, where
and
were randomly and independently sampled, i.e.,
where
and
are not necessarily equal to each other in each misalignment step.
A random axial displacement
was also added to the axial separations between any two consecutive planes. Accordingly, the axial distance between any two consecutive planes was set to
20 mm
, where
was randomly sampled as,
In our experiments, we also measured the power spectrum of the pulsed terahertz source with only the input and output apertures present, which served as an experimental reference spectrum,
. Based on this, the experimentally measured power spectrum at the output single-pixel aperture of a diffractive network can be written as:
The binary objects and apertures were all 3D-printed (Form 3B, Formlabs) and coated with aluminum foil to define the transmission areas. Apertures, objects, the diffuser, and the diffractive layer were assembled using a 3D-printed holder (Objet30 Pro, Stratasys). The setup of the THz-TDS system is illustrated in Fig.
7a. A Ti:Sapphire laser (Mira-HP, Coherent) generates optical pulses with a 135-fs pulse width and a 76-MHz repetition rate at a center wavelength of 800 nm, which pumps both a high-power plasmonic photoconductive terahertz source
57 (link) and a high-sensitivity plasmonic photoconductive terahertz detector
58 (link). The terahertz radiation generated by the terahertz source is collimated by a 90° off-axis parabolic mirror and illuminates the test object. After interacting with the object, the diffuser, and the diffractive neural network, the radiation is coherently detected by the terahertz detector (single-pixel). A transimpedance amplifier (DHPCA-100, Femto) converts the current signal to a voltage signal, which is then measured by a lock-in amplifier (MFLI, Zurich Instruments). By varying the optical delay between the terahertz radiation and the optical probe beam on the terahertz detector, the terahertz time-domain signal can be obtained. By taking the Fourier transform of the time-domain signal, the spectral intensity signal is revealed to calculate the class scores for each classification/inference. For each measurement, 10 time-domain traces are collected and averaged. This THz-TDS system provides a signal-to-noise ratio larger than 90 dB and a detection bandwidth larger than 4 THz.