Fact-checked by Grok 2 weeks ago

Impact ionization

Impact ionization is a fundamental carrier multiplication process in semiconductors, in which a high-energy or , accelerated by a strong , gains sufficient to collide with a bound and excite it across the bandgap, thereby generating an additional electron-hole pair. This threshold energy for ionization is typically approximately 1.5 times the semiconductor's bandgap energy, ensuring and momentum during the collision. The mechanism involves non-equilibrium transport under high electric fields, often exceeding 10^5 V/cm, where carriers undergo inelastic scattering events that favor pair creation over phonon emission. The probability of impact ionization is quantified by field-dependent coefficients, such as the electron ionization coefficient α_n, which follows empirical forms like α_n = A exp(-B/E) (where E is the electric field), reflecting the exponential rise in rate as carriers surpass the threshold. In materials like silicon, α_n reaches values around 10^6 cm^{-1} at fields of several hundred kV/cm, while wide-bandgap semiconductors like GaN exhibit lower coefficients, enabling higher breakdown voltages. This process is critical for device physics, underpinning in p-n junctions, where iterative leads to rapid and potential device failure if uncontrolled. Conversely, it is deliberately exploited in avalanche photodiodes (APDs) and single-photon detectors for internal gain, enhancing sensitivity in optical communications and particle detection. In and high-speed transistors, impact influences hot carrier effects and reliability, driving research into mitigation strategies like band-structure engineering.

Fundamentals

Definition

Impact ionization is the process by which an energetic , either an or a , in a with a band structure loses through a collision with a bound electron, thereby exciting that electron across the bandgap to create an additional electron-hole pair, resulting in carrier multiplication. This phenomenon occurs primarily in semiconductors and insulators, where the accelerated carrier, often termed a "hot" carrier, acquires sufficient from an applied to enable the event. The process demands specific prerequisites: a exhibiting a distinct and conduction , such as in semiconductors, and a high to impart the necessary to free carriers, typically on the order of those found in high-field device regions. Without these conditions, carriers lack the energy for such collisions, preventing ionization. Impact ionization differs fundamentally from generation, which produces electron-hole pairs through random thermal excitations across the bandgap without requiring high-energy carrier collisions or external fields, relying instead on temperature-driven processes in . In contrast to , where photons directly supply the energy to excite electrons from the valence to the conduction , impact ionization is induced solely by the transfer during carrier-carrier interactions under electric acceleration. For example, in a , a accelerated to an exceeding the can collide with a , promoting it to the conduction and generating a new electron-hole pair. This microscopic mechanism enables macroscopic effects, such as in junctions.

Physical Mechanism

Impact ionization occurs when a primary , typically an or , is accelerated by a strong within a , attaining greater than the necessary to initiate the process. This acceleration happens through repeated scattering events with phonons and impurities, but in high fields, the carrier gains sufficient to become "." Once this surpasses the required —roughly on the order of the —the carrier is capable of participating in an ionizing collision. The core of the mechanism is an between the hot primary and a bound in the lattice, governed by the interaction due to electron-electron repulsion. In the binary collision approximation, the primary electron transfers a portion of its to the , exciting it across the bandgap into the conduction band and thereby generating a secondary electron- pair. This energy transfer is inelastic, meaning the total of the system decreases by at least the bandgap energy, with of both and enforced during the two-body interaction; necessitates that the of the primary exceeds the bandgap to account for the of the three resulting particles. The excess beyond this is distributed among the primary electron (now with reduced velocity), the secondary electron, and the newly created . Following pair creation, all three —the original primary and the secondary pair—experience the and can gain , potentially undergoing further collisions to produce additional pairs in a cascading manner. A representative energy diagram illustrates this process: the initial hot appears high above the conduction minimum with substantial , while the post-collision states show the valence with a , the conduction populated by two electrons (one primary with diminished and one secondary near the edge), highlighting the effect. This microscopic process underpins but is distinct from macroscopic phenomena like .

Key Parameters

Threshold Energy

The threshold energy, denoted as E_{th}, represents the minimum kinetic energy a charge carrier must acquire to trigger impact ionization in a semiconductor, enabling the generation of an electron-hole pair through collision. This energy is generally greater than the material's bandgap energy E_g to account for momentum conservation and energy dissipation via phonon emission during the process. Typically, E_{th} \approx 1.5 \times E_g, as the impacting carrier loses a portion of its energy to lattice vibrations (phonons), ensuring the final states satisfy energy and momentum requirements. The value of E_{th} varies significantly with the material, primarily due to differences in bandgap and band structure. In (Si), an indirect bandgap material with E_g \approx 1.12 eV, the threshold for electrons is approximately 1.8 eV, while for holes it is higher, around 2.3 eV. In gallium arsenide (GaAs), a direct bandgap with E_g \approx 1.42 eV, the electron threshold is about 1.7 eV. Wider-bandgap materials like (SiC), with E_g \approx 3.2 eV for 4H-SiC, exhibit higher thresholds, typically approximately 1.5 times the bandgap (around 4.8 eV or more), reflecting the increased energy barrier for pair creation. Several factors influence E_{th}, including the semiconductor's band structure, carrier effective masses, and scattering mechanisms. Direct bandgap materials like GaAs allow for lower thresholds compared to indirect ones like , as the former facilitate easier matching without assistance. Lighter effective masses enable carriers to reach higher energies under , potentially lowering the effective threshold, while frequent reduces the net available for . These effects collectively determine the precise E_{th} and its within the . Experimentally, E_{th} is determined by observing the onset of carrier multiplication in p-n diodes or similar structures under applied , where the multiplication gain begins to exceed unity. Measurements involve varying the field strength and analyzing the current amplification, often using photomultiplication techniques to isolate the ionization threshold. A key concept in describing the threshold behavior is the Chynoweth condition, which models the ionization probability as rising sharply once the carrier energy exceeds E_{th}, transitioning from negligible to significant rates in high-field environments. This "soft" threshold reflects the probabilistic nature of the process, where the rate follows an exponential dependence on energy above E_{th}.

Ionization Coefficients

Ionization coefficients, denoted as \alpha for electrons and \beta for holes, quantify the average number of electron-hole pairs generated per unit distance traveled by a in the direction of the applied , provided the carrier energy exceeds the for impact . These coefficients become significant only above the , where carriers gain sufficient from the field to initiate ionization. An empirical expression for the field dependence of these coefficients, known as Chynoweth's law, is given by \alpha(E) = A \exp\left(-\frac{B}{E}\right), where E is the strength, and A and B are material-dependent constants reflecting the probability of events. For , representative parameters for electron-initiated ionization are A \approx 7 \times 10^5 cm^{-1} and B \approx 1.2 \times 10^6 V/cm, while hole-initiated parameters differ, typically with a higher B value around $2 \times 10^6 V/cm. This form captures the exponential increase in ionization probability with , as higher fields accelerate carriers more effectively between events. In , the coefficients exhibit , with \alpha > \beta across relevant field ranges, attributed to the and conduction band structures that favor electron-initiated processes due to differences in effective masses and . For instance, at fields of $3 \times 10^5 V/cm, \alpha can exceed \beta by an or more, influencing the directionality of multiplication in devices. These coefficients are experimentally determined from measurements of the carrier multiplication factor M in reverse-biased p-n junctions, defined as M = I_\text{total} / I_\text{primary}, where I_\text{total} is the total current and I_\text{primary} is the primary generation current (e.g., from or optical sources). By varying the bias to achieve high fields in the and solving the ionization integral iteratively—accounting for junction profiles via measurements—the local \alpha and \beta are extracted as functions of E. This approach ensures the coefficients reflect bulk material properties rather than edge effects. The coefficients display a pronounced dependence, decreasing with rising due to increased , which shortens mean free paths and reduces the likelihood of reaching thresholds. simulations confirm this effect, showing \alpha and \beta dropping by factors of 2–5 over 300–500 K in at fixed fields around $4 \times 10^5 V/cm, as enhanced optical and acoustic interactions dissipate more efficiently. At high electric fields exceeding $10^5 V/cm, typical in avalanche regimes, the coefficients reach $10^3–$10^4 cm^{-1}, resulting in exponential carrier multiplication over micrometer-scale distances and enabling gain factors of $10^2 or higher before breakdown.

Semiconductor Applications

Avalanche Breakdown

Avalanche breakdown in semiconductors arises from a chain reaction driven by impact ionization, where thermally generated or injected charge carriers are accelerated by a high electric field, gaining sufficient energy to create additional electron-hole pairs upon collision with lattice atoms. This process multiplies the number of carriers exponentially, resulting in a rapid increase in reverse current through the device. The multiplication factor M, which quantifies the gain as the ratio of the total carrier current to the initial current, is expressed as M = \frac{1}{1 - \int_0^W \alpha(x) \, dx} for pure electron-initiated multiplication across a depletion region of width W, where \alpha(x) is the position-dependent electron impact ionization coefficient. The ionization coefficients \alpha and \beta (for holes) serve as the key drivers in the multiplication integral. Runaway current occurs when \int_0^W (\alpha - \beta) \, dx \approx 1, at which point the denominator approaches zero and M diverges, leading to uncontrolled carrier generation. The is the critical reverse bias at which this infinite multiplication is achieved, corresponding to an strength where impact ionization dominates. In p-n junctions, this critical field is approximately $3 \times 10^5 V/cm, beyond which the device can no longer sustain the applied voltage without surge. For a uniform field approximation in the , the multiplication factor simplifies to M = \frac{I}{I_0} = \frac{1}{1 - (\alpha - \beta)W}, where I is the multiplied , I_0 is the , and W is the depletion width; breakdown ensues as (\alpha - \beta)W nears . Avalanche breakdown manifests in two primary types: local and non-local. Local breakdown assumes a uniform across the multiplication region, well-described by the above models in longer-channel devices where carriers fully thermalize between collisions. In contrast, non-local breakdown predominates in short-channel structures, such as submicron transistors, where the channel length is comparable to the carrier , leading to "streaming" effects that alter the energy distribution and rates without local equilibrium. Additionally, breakdown often localizes into microplasmas—tiny hotspots of high carrier density—due to field non-uniformities or defects, forming current filaments that pulse intermittently and contribute to noisy current characteristics. The consequences of avalanche breakdown are severe, including thermal runaway from localized Joule heating generated by the high current density, which raises lattice temperature and further enhances ionization rates in a positive feedback loop, ultimately causing permanent device damage through melting or structural degradation. To mitigate premature or edge-related breakdown, design strategies such as guard rings—lightly doped annular regions surrounding the junction—and tailored doping profiles are implemented to smooth the electric field distribution, increasing the effective breakdown voltage by up to 50% in some silicon structures. This phenomenon was first systematically observed in the early 1950s through studies of p-n junctions, where K. G. McKay and colleagues demonstrated the avalanche mechanism via light emission and current multiplication in reverse-biased diodes. Avalanche breakdown is particularly relevant in distinguishing operating regimes from Zener (tunneling) breakdown, which prevails in heavily doped junctions at reverse voltages below approximately 6 V, while dominates above this threshold due to the field strength required for carrier multiplication.

Carrier Multiplication Devices

Avalanche photodiodes (APDs) are devices that exploit impact ionization to achieve internal current gain for detecting low-light signals. These devices typically feature a p-i-n structure where the intrinsic (i) region serves as a high electric field multiplication zone, enabling carrier multiplication without reaching destructive breakdown. Photogenerated carriers in the absorption layer drift into the multiplication region, where the applied reverse bias accelerates them to energies sufficient for impact ionization, producing secondary electron-hole pairs and amplifying the by a factor M, often ranging from 100 to 1000 for enhanced sensitivity in photon-starved environments. The performance in APDs is characterized by the excess noise factor F, which quantifies the additional variance from the nature of impact ionization:
F = kM + (2 - 1/M)(1 - k),
where M is the mean and k = β/α is the ratio of to electron ionization coefficients. This factor arises because random multiplication chains lead to fluctuations beyond statistics, limiting the effective in noisy conditions. In materials like InGaAs, electron-initiated multiplication is preferred as it yields k < 1, resulting in lower F and reduced compared to hole-initiated processes.
APDs, first invented in 1952 by Jun-ichi Nishizawa, underwent significant development in the 1970s for , enabling high-speed optical receivers by providing gain to compensate for weak signals in fiber-optic systems. Another class of carrier multiplication devices is the impact ionization metal-oxide-semiconductor (I-MOS) transistor, which employs a gated p-i-n structure to modulate the high-field region for impact ionization. By controlling the gate voltage to tune the ionization probability, I-MOS devices achieve sub-60 mV/decade subthreshold swing, surpassing the Boltzmann limit of conventional MOSFETs while maintaining low off-state leakage. A related technology is single-photon avalanche diodes (SPADs), which operate in Geiger mode where a single carrier triggers a self-sustaining , providing high internal gain for single-photon detection. SPADs are widely used in applications such as fluorescence lifetime imaging, , and time-of-flight , offering timing resolution and low dark count rates in modern and InGaAs implementations as of 2025. These devices find applications in optical receivers for , where APDs boost signal-to-noise ratios in long-haul links, and in systems for precise ranging in autonomous vehicles and environmental sensing. The primary advantages of carrier multiplication devices include high sensitivity for weak signal detection, enabling single-photon or low-flux measurements. However, they suffer from temperature sensitivity, as ionization coefficients vary with , and timing from multiplication delays, which can degrade performance in high-speed or pulsed applications.

Theoretical Modeling

Empirical Models

Empirical models for impact ionization provide practical, data-fitted expressions for the ionization coefficient α, enabling efficient predictions in device engineering without requiring detailed quantum calculations. These models are derived from experimental measurements in p-n junctions and are particularly useful for uniform or slowly varying electric fields in semiconductors. The foundational Chynoweth model expresses the field-dependent ionization coefficient in the exponential form \alpha(E) = A \exp(-B/E), where A represents the high-field limit of the coefficient and B is related to the threshold energy for ionization, with parameters fitted to avalanche multiplication data in materials like silicon (Si) and gallium arsenide (GaAs). For Si, typical values are A \approx 3.8 \times 10^6 cm^{-1} and B \approx 1.75 \times 10^6 V/cm for electrons at room temperature, reflecting the empirical capture of the sharp rise in ionization as carriers gain sufficient kinetic energy from the field. Building on this, the Okuto-Crowell model incorporates the statistical of carrier energies to more accurately describe the average rate, given by \alpha = \int P(\varepsilon) f(\varepsilon) \, d\varepsilon, where P(\varepsilon) is the probability of at energy \varepsilon and f(\varepsilon) is the energy derived from considerations. This form accounts for the variance in energies due to , improving predictions over simple exponential fits, especially near conditions. Extensions incorporating effects address limitations in short devices, where newly generated carriers must travel a minimum —on the order of the times the number of scatterings needed to reach —before they can ionize, preventing overestimation of in submicron structures. In this approach, the effective ionization probability is zero within the region, with the model adjusting \alpha based on position and field history for more realistic simulations of thin regions. Validation of these models involves comparing simulated multiplication factors M = 1/(1 - \int \alpha \, dx) with experimental measurements, showing good agreement for voltages in long devices but deviations in short ones due to . For III-V semiconductors like (), fitted Chynoweth parameters yield higher B values (e.g., B \approx 3.7 \times 10^7 V/cm for electrons), attributable to the wide 3.4 eV bandgap requiring stronger fields for threshold energy attainment. These models assume a approximation, where \alpha depends solely on the instantaneous at the carrier's position, rendering them inaccurate for devices with rapid field variations or spatial non-uniformities that alter energy gain paths. Empirical formulations such as the Chynoweth model have been employed in technology (TCAD) simulations for device optimization since the 1960s, facilitating rapid assessment of avalanche performance in diodes and transistors.

Advanced Simulations

Advanced simulations of impact ionization extend beyond empirical models by employing numerical methods to capture complex carrier dynamics in high-field regimes, enabling predictions in realistic device geometries and non-equilibrium conditions. techniques, particularly full-band ensemble simulations, stochastically solve the Boltzmann transport equation by tracking individual carrier trajectories, incorporating scattering events such as electron-phonon interactions and impact ionization, while resolving energy distributions across the full . These methods use ab initio-derived rates for accuracy, simulating high-energy transport in materials like β-Ga₂O₃ and GaAs, where they reveal in ionization coefficients due to band structure effects. Hydrodynamic models provide a deterministic approach, extending drift-diffusion equations with energy balance to account for hot carrier effects, solved via finite element methods in multi-dimensional device structures. The impact generation term is incorporated as G = \alpha n E + \beta p E, where \alpha and \beta are field- or temperature-dependent ionization coefficients for electrons and holes, respectively, n and p are densities, and E is the ; this term couples with and equations to model multiplication. These models are particularly suited for capturing non-local effects in sub-micron devices, such as spatial variations in influencing rates. Quantum approaches offer microscopic insights, with (DFT) used to compute threshold energies by evaluating band alignments and electron-electron interactions from first principles, often correcting local density approximations for accurate bandgap predictions essential to ionization onset. For non-equilibrium dynamics, time-dependent formulations within non-equilibrium frameworks model impact events as matrix-based evolutions, incorporating open boundary conditions to simulate carrier multiplication in avalanche photodiodes (APDs) while conserving energy and momentum. A representative application is the hydrodynamic model implemented in Sentaurus TCAD, which predicts gain by solving coupled transport equations with impact ionization, enabling optimization of multiplication layers in single-photon APDs to achieve high sensitivity without premature . Recent advances since 2020 include surrogates, such as deep neural networks trained on TCAD datasets to predict position-dependent ionization s in 2D devices, achieving over 97% accuracy in location and less than 6% error in coefficient values compared to full simulations. As of 2024, studies have further explored trap-assisted impact ionization in dichalcogenides (TMDs), revealing new mechanisms beyond conventional models. Despite these capabilities, advanced simulations face limitations: quantum methods like DFT and time-dependent Schrödinger incur high computational costs due to matrix scaling and fine k-space sampling, often restricting analyses to limited bands or dimensions; additionally, effective mass approximations in hydrodynamic models introduce errors in narrow-gap materials by neglecting full-band dispersion.

References

  1. [1]
  2. [2]
    Impact ionization thresholds in semiconductors - IOPscience
    A method of deriving accurate values for impact ionization threshold energies in semiconductors from realistic band structures is described.
  3. [3]
    5.1 Basics of Impact-Ionization - IuE
    Impact-ionization is a three-particle generation process. Carriers that gain high energies while traveling through high field regions undergo scattering events ...
  4. [4]
    Impact Ionization - an overview | ScienceDirect Topics
    Impact ionization is opposite to Auger recombination as it absorbs the energy of motion of another electron or hole to generate an electron–hole pair.
  5. [5]
    [PDF] IMPACT IONIZATION IN SILICON
    Impact ionization is an important charge generation mechanism. It occurs in many semiconductor devices and it either determines the useful characteristic of the ...
  6. [6]
    Impact ionization in semiconductors: Effects of high electric fields ...
    May 15, 1992 · We present a theory of impact ionization in semiconductors that expands an earlier theory of Kane and includes the effects of high electric ...
  7. [7]
  8. [8]
    2.3 Carrier Generation and Recombination - IuE
    Impact ionization is a pure generation process. ... The major difference is the cause of the effect. While it is purely the carrier concentration in the Auger ...
  9. [9]
    Impact Ionization and Avalanche Breakdown - ResearchGate
    The threshold energy is determined to be Eg≤Ei≤1.5Eg, and the mean free ... Impact ionization in narrow gap semiconductors. Article. Jan 1973; Phys ...
  10. [10]
    Excess Carriers in Semiconductors: Recombination Mechanisms
    ... threshold energy Et. The value of Et for impact ionization is roughly equal to 1.5Eg, where Eg is the energy band gap of the semiconductor. By substituting ...
  11. [11]
    Measurement of the ionization rates in diffused silicon p-n junctions
    Measurements on narrow junctions agree with measurements on wide junctions by assuming a threshold energy of 1.8 eV for electrons, in agreement with the results ...
  12. [12]
    The determination of impact ionization coefficients in (100) gallium ...
    Agreement among the results from all these structures is obtained with an electron threshold energy of 1.7 eV, and the corrected data are also in agreement with ...
  13. [13]
    Impact ionization coefficients of 4H silicon carbide - AIP Publishing
    Aug 23, 2004 · Impact ionization coefficients are important material properties for power devices, because the avalanche breakdown of a power device is caused ...
  14. [14]
    Threshold Energies for Electron-Hole Pair Production by Impact ...
    Mar 15, 1972 · The lowest thresholds for electron-initiated ionization without phonon assistance are 1.1, 0.8, 1.7, 2.6, and 0.2 eV relative to the conduction- ...
  15. [15]
    The band structure dependence of impact ionization by hot carriers ...
    Our measurements, made in GaAs, establish the crucial role of the ionization threshold energy, and its location in the Brillouin zone, in determining the ...
  16. [16]
    A discussion on various experimental methods of impact ionization ...
    Impact ionization coefficients play a critical role in semiconductors. In addition to silicon, silicon carbide and gallium nitride are ...
  17. [17]
    Ionization Rates for Electrons and Holes in Silicon | Phys. Rev.
    The ionization rates for holes and electrons in silicon have been determined over the following ranges of field: for holes, (2.5-6.0)× 1 0 5 volts c m − 1 ...
  18. [18]
    [PDF] MEASUREMENT OF THE IONIZATION RATES IN DIFFUSED ...
    Ionization rates are calculated using charge multiplication, approximating impurity profiles, and considering threshold energy, using an exponential function.
  19. [19]
    Temperature dependence of the electron impact ionization ...
    Abstract. We have used a Monte Carlo simulation to study the temperature dependence of the electron impact ionization coefficient in silicon.
  20. [20]
    Electrical properties of Silicon (Si)
    Basic Properties ; Breakdown field, ≈3·105V/cm ; Mobility electrons, ≤1400 cm2 V-1s ; Mobility holes, ≤450 cm2 V-1s ; Diffusion coefficient electrons, ≤36 cm2/s.
  21. [21]
    [PDF] Physics of Semiconductor Devices
    In this Third Edition of Physics of Semiconductor Devices, over 50% of the material has been revised or updated, and the material has been totally reorganized.
  22. [22]
    [PDF] Nonlocal impact ionization and avalanche multiplication - HAL
    Feb 25, 2011 · Avalanche breakdown, corresponding to a divergence in multiplication, occurs when the denominators of the right hand sides of these equations ...
  23. [23]
    Theory of microplasma fluctuations and noise in silicon diode in ...
    Mar 30, 2007 · The microplasma formation in avalanche breakdown requires a high rate of free charge generation by impact ionization in order to supply high ...
  24. [24]
    In situ thermal runaway of Si-based press-fit diodes monitored by ...
    Two main physical mechanisms for semiconductor breakdown can be described. The avalanche breakdown is generated by the high kinetic energy of the electrons at ...
  25. [25]
    Guard Ring Design to Prevent Edge Breakdown in Double-Diffused ...
    Feb 16, 2023 · We show that when the spacing is longer than the critical value, the breakdown voltage can increase ~1.5 V higher than those of the APD devices ...
  26. [26]
    Avalanche Breakdown in Silicon | Phys. Rev.
    An avalanche theory of breakdown at room temperature is proposed for semiconductors based on the assumption of approximately equal ionization rates.Missing: 1950s | Show results with:1950s
  27. [27]
    Zener Effect vs Avalanche Effect in PN Junction Diode - Kynix
    Feb 25, 2022 · Zener with voltage lower than 5-6V is mainly due to Zener breakdown; Zener with voltage higher than 5-6V is mainly due to avalanche breakdown.
  28. [28]
    [PDF] Avalanche Photodiodes: A User's Guide
    The excess noise factor may be calculated using the model developed my McIntyre(3) which considers the statistical nature of avalanche multiplication. The ...
  29. [29]
    Electron-initiated low noise 1064 nm InGaAsP/InAlAs avalanche ...
    Jan 10, 2018 · Here we demonstrate an electron-initiated 1064 nm InGaAsP SAGCM APD with a p type InAlAs multiplier, a tailored InGaAs/InAlAs digital alloy ...
  30. [30]
    [PDF] Evolution of Low-Noise Avalanche Photodetectors
    From the mid 1970's to the present, optical communications. [1], imaging [2], [3], and single photon detection [4], [5] have been the primary driving forces ...
  31. [31]
    Impact ionization MOS (I-MOS)-Part I: device and circuit simulations
    One of the fundamental problems in the continued scaling of transistors is the 60 mV/dec room temperature limit in the subthreshold slope.
  32. [32]
    [PDF] Characterization of Advanced Avalanche Photodiodes for Water ...
    They have several applications including backscatter lidar, DIAL, and fiber-optic communication. Although they are widely used, few papers in the literature ...
  33. [33]
    Avalanche photodiode breaks performance record for LiDAR receivers
    May 27, 2020 · The team's avalanche photodiode is an ideal solution for compact, high-sensitivity LiDAR receivers. Many LiDAR applications, such as ...
  34. [34]
    [PDF] THE CASSINI COSMIC DUST ANALYZER - CalTech GPS
    The Cassini Cosmic Dust Analyzer (CDA) observes dust grains, measuring their mass, composition, charge, speed, and flight direction in interplanetary and ...
  35. [35]
    Avalanche Photodiodes – APD, single-photon detection, Geiger ...
    They have a higher signal-to-noise ratio (SNR) than PIN photodiodes, as well as fast time response, low dark current, and high sensitivity.
  36. [36]
    An Analysis of Temperature-Dependent Timing Jitter Factors in the ...
    Single-Photon Avalanche Photodiodes (SPADs) are increasingly utilized in high-temperature-operated, high-performance Light Detection and Ranging (LiDAR) ...Missing: disadvantages | Show results with:disadvantages
  37. [37]
    Dead space approximation for impact ionization in silicon
    Dec 9, 1996 · We show that the hard‐threshold dead space model is in good agreement with a more refined model taking into account soft‐threshold effects ...
  38. [38]
    Experimental determination of impact ionization coefficients of ...
    Aug 13, 2019 · In this study, we experimentally determined the impact ionization coefficients of GaN using homoepitaxially grown pn diodes with avalanche capability.
  39. [39]
    5.2 Modeling Approaches - IuE
    Like all high-energy mechanisms, impact-ionization is a non-local process (compare Section 4.3.1). This leads to problems in classical drift-diffusion ...
  40. [40]
    [PDF] TCAD Avalanche Models - KIP Wiki
    May 19, 2020 · ○ Chynoweth's experimental ionization rates ... ○ Compact model based on impact ionization data generated by Boltzmann solver HARM :.
  41. [41]
    [PDF] Impact Ionization in β-Ga2O3 - arXiv
    A hypothetical estimate using the computed Chynoweth parameters predicts avalanche breakdown field to be higher than the empirically predicted value of 8 MV/cm ...
  42. [42]
    Full-Band Monte Carlo simulations of GaAs p-i-n Avalanche ...
    We present a Full-Band Monte Carlo (FBMC) investigation of impact ionization in GaAs pin Avalanche Photodiodes (APDs).
  43. [43]
    3.7.5 Impact Ionization - IuE
    The ionization coefficients $\alpha_{n,\mathrm {bulk}}$ and $\alpha_{p,\mathrm {bulk}}$ are expressed by Chynoweth's law ... Si, 7.03e7, 1.231e8, 1.0, 1.528e8 ...Missing: silicon | Show results with:silicon
  44. [44]
    Hydrodynamic modeling of avalanche breakdown in a gate ...
    Jul 1, 2000 · Several research papers have shown the feasibility of the hydrodynamic transport model to investigate impact ionization in semiconductor ...
  45. [45]
    [PDF] TCAD Simulation of CMOS Single- Photon Avalanche Photodiode
    This aim of this project is to investigate the use of the state-of-the-art TCAD software tool Sentaurus™ to simulate Single-Photon Avalanche. Photodiodes (SPADs) ...
  46. [46]
    Impact Ionization Coefficient Prediction of a Lateral Power Device ...
    This paper proposes a deep neural network method to predict the impact ionization coefficient for 2D lateral power devices, achieving 97.67% accuracy for ...