STUDY FOR GENERALIZED MODEL FOR SEMICONDUCTOR RADIATION RESPONSE PREDICTION.

Abstract

Investigations included the further study of grown junction diodes and transistors, and the initial study of the transistor element of a monolithic chip integrated circuit. The ionizing radiation response of the diode was calculated using the known values of diode recovery time and junction area. Calculated and observed photocurrents are within a factor of two. The common-emitter response of the grown-junction transistor was calculated. The openbase and small-signal transistor response was obtained from electrical parameter measurement and the collector photocurrent with source resistance of zero. Discrepancies between calculated and observed waveforms are attributed to variations in common-emitter current gain and carrier motion by drift in the collector region. Ionizing radiation effects in an integrated circuit transistor element were studied analytically and experimentally. The lumped model demonstrates the motion of carriers generated in the collector region to the base or substrate regions. The common-emitter response is used to show the influence of the substrate photocurrent on the total observed collector current. Neutron-induced lifetime degradation was directly introduced in the transistor lumped model to give the expected form of transistor gain reduction. The results compare favorably to the Messenger-Spratt prediction. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 15, 1965
Accession Number
AD0625341

Entities

People

  • James P. Raymond
  • William W. Chang

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Accumulators
  • Bipolar Junction Transistors
  • Circuits
  • Integrated Circuits
  • Ionizing Radiation
  • Radiation
  • Radiation Effects
  • Semiconductor Devices
  • Semiconductors
  • Substrates
  • Transistors

Fields of Study

  • Physics

Readers

  • Electronics Engineering
  • Nuclear and Radiation Engineering.
  • Semiconductor Device Technology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Microelectronics