Hydrogen Depassivation Lithography has become established as the method for atomic-precision patterning for 2D dopant-based devices, such as qubits and analog quantum simulators. Research thus far in this area has mostly been focused on patterning of n-type dopants, such as P and As. In this work, we describe the process for fabrication of bipolar dopant-based devices, such as p-n junctions and n-p-n bipolar junction transistors, which may have a number of advantages such as improved gain-bandwidth product and low-noise operation. The P-doped parts of the device are created first, and the B-doped parts are created subsequently, requiring atomically-precise alignment to the P-doped parts. To achieve the necessary patterning precision, we have developed various advances to conventional STMs, including corrections for piezo creep and hysteresis, automatic lattice alignment, and spectroscopic imaging methods to give strong contrast of surface and buried dopants. The overall process described here has become known as Atomically Precise Advanced Manufacturing (APAM), which offers far better patterning precision than conventional techniques such as e-beam lithography.
Current lithographic techniques are limited to a resolution of a few nm with poor relative precision. Scanning Tunneling Microscope (STM) based lithography[1], removes H from H-passivated Si 2x1 (100) by a mode distinct from usual imaging. This technique is generally called Hydrogen Depassivation Lithography (HDL) and since it scans a beam of electrons around on a surface exposing a resist, it is a form of E-beam Lithography. The HDL approach is not effective with standard resists and, at present, has only a limited number of pattern transfer methods. The two primary ones are patterning 2D delta doped Si devices for solid state quantum devices and selective Atomic Layer Deposition metal oxides that can be used as hard etch masks. However, electron stimulated desorption of atoms and molecules is a fairly generic process and its use can be anticipated on a wide variety of substrates. Sub-nm resolution (0.768 nm) has been demonstrated and used for numerous research purposes, such as dopant positioning for quantum devices[2]. While sub-nm resolution is easily obtainable with standard Ultra-High Vacuum (UHV) STMs, the repeatability and accuracy of the patterning has limited its applications. In this paper we report on progress to dramatically scale HDL’s throughput while maintaining sub-nm resolution.
In this work, we present the development of an infrared scanning near-field optical microscope (IR-SNOM) for thermal imaging. As an example, we explore thermal imaging of quantum cascade lasers (QCLs). QCLs are attractive infrared (IR) sources for chemical detection due to their tunability and wide emission range spanning from mid-wavelength to longwavelength infrared radiation (MWIR and LWIR). However, they require high performance cooling systems and have limited use at low power in continuous wave (CW) operation due to the potential for thermal failure of the device. Thermal imaging can help identify mechanisms and points of failure during laser operation. Because the size of the features of QCLs (~1 μm) are much smaller than the wavelength of the emitted thermal radiation, IR-SNOM is an ideal technique to image the spatial thermal profile of QCLs during operation to guide design improvement.
Scanning Tunneling Microscopy (STM) can easily image hydrogen-passivated silicon(001) with atomic resolution, but images often contain artifacts such as double tips, blunt tips or unstable tips. Nevertheless, a trained human eye can recognize surface details such as dimer rows, step edges, depassivated silicon, etc.
A Neural Network could classify the surface better than the human eye. One advantage of a deep learning algorithm is that it can analyze, in parallel, information from multiple channels such as topography, tunneling current, forward and reverse scans. Our scanning software also collects Tunnel Barrier Height information that contributes extra information about the electronic properties of the surface at each point.
This identification will be integrated into ZyVector – our STM controller product -- to provide more accurate depiction of the surface than is available in the STM image, and to automate Hydrogen Depassivation Lithography (HDL) patterning.
KEYWORDS: Electron beam lithography, Manufacturing, Silicon, Chemical species, Scanning tunneling microscopy, Error control coding, Information technology, Fabrication, Lithography
Hydrogen Depassivation Lithography (HDL) is a version of electron beam lithography that uses scanning tunneling microscope (STM) instrumentation to expose a self–developing resist that is a monolayer of H chemisorbed to a Si (100) 2x1 H-passivated surface. Developed in the 1990s it has been largely a laboratory tool used in research for nanofabrication. The technique is capable of atomic resolution, the ability to remove single H atoms from the Si surface and has much higher precision than the best conventional e-beam lithography can possibly achieve exposing polymeric resists. However, its most promising attribute is that it can be used as a digital fabrication tool and is the first of a class of nanofabrication techniques that can be considered digital atomic scale fabrication technologies. Digital Atomic Scale Fabrication can be shown to have similar advantages over analog fabrication techniques that digital information technology has over analog information technology.
KEYWORDS: Thermography, Quantum cascade lasers, Near field scanning optical microscopy, Spatial resolution, Temperature metrology, Infrared imaging, Infrared radiation, Modulation, Near field
The fundamental optical diffraction in infrared microscopes limits their spatial resolution to about ~5μm and hinders the detailed observation of heat generation and dissipation behaviors in micrometer-sized optoelectronic and semiconductor devices, thus impeding the understanding of basic material properties, electrical shorts and structural defects at a micron and sub-micron scale. We report the recent development of a scanning near-field optical microscopy (SNOM) method for thermal imaging with subwavelength spatial resolution. The system implements infrared fiber-optic probes with subwavelength apertures at the apex of a tip for coupling to thermal radiation. Topographic imaging and tip-to-sample distance control are enabled by the implementation of a macroscopic aluminum tuning fork of centimeter size to support IR thermal macro-probes. The SNOM-on-a-fork system is developed as a capability primarily for the thermal profiling of MWIR quantum cascade lasers (QCLs) during pulsed and continuous wave (CW) operation, targeting QCL design optimization. Time-resolved thermal measurements with high spatial resolution will enable better understanding of thermal effects that can have a significant impact on a laser's optical performance and reliability, and furthermore, will serve as a tool to diagnose failure mechanisms.
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