The unique properties of superconductivity can be exploited to provide the ultimate in electronic technology for systems such as ultra-precise analogue-to-digital and digital-to-analogue converters, precise DC and AC voltage standards, ultra high speed logic circuits and systems (both digital and hybrid analogue-digital systems), and very high throughput network routers and supercomputers which would have superior electrical performance at lower overall electrical power consumption compared to systems with comparable performance which are fabricated using conventional room temperature technologies. This potential for high performance electronics with reduced power consumption would have a positive impact on slowing the increase in the demand for electrical utility power by the information technology community on the overall electrical power grid. However, before this technology can be successfully brought to the commercial market place, there must be an aggressive investment of resources and funding to develop the required infrastructure needed to yield these high performance superconductor systems, which will be reliable and available at low cost. The author proposes that it will require a concerted effort by the superconductor and cryogenic communities to bring this technology to the commercial market place or make it available for widespread use in scientific instrumentation.

A neural network of massively interconnected digital neurons is presented for the total coloring problem in this paper. Given a graph G (V, E), the goal of this NP-complete problem is to find a color assignment on the vertices in V and the edges in E with the minimum number of colors such that no adjacent or incident pair of elements in V and E receives the same color. A graph coloring is a basic combinatorial optimization problem for a variety of practical applications. The neural network consists of (N+M) L neurons for the N-vertex-M-edge-L-color problem. Using digital neurons of binary outputs and range-limited non-negative integer inputs with a set of integer parameters, our digital neural network is greatly suitable for the implementation on digital circuits. The performance is evaluated through simulations in random graphs with the lower bounds on the number of colors. With a help of heuristic methods, the digital neural network of up to 530, 656 neurons always finds a solution in the NP-complete problem within a constant number of iteration steps on the synchronous parallel computation.

A digital neural network approach is presented for the multilayer channel routing problem with the objective of crosstalk minimization in this paper. As VLSI fabrication technology advances, the reduction of crosstalk between interconnection wires on a chip has gained important consideration in VLSI design, because of the closer interwire spacing and the circuit operation at higher frequencies. Our neural network is composed of N M L digital neurons with one-bit output and seven-bit input for the N-net-M-track-2L-layer problem using a set of integer parameters, which is greatly suitable for the implementaion on digital technology. The digital neural network directly seeks a routing solution of satisfying the routing constraint and the crosstalk constraint simultaneously. The heuristic methods are effectively introduced to improve the convergence property. The performance is evaluated through solving 10 benchmark problems including Deutsch difficult example in 2-10 layers. Among the existing neural networks, the digital neural network first achieves the lower bound solution in terms of the number of tracks in any instance. Through extensive simulation runs, it provides the best maximum crosstalks of nets for valid routing solutions of the benchmark problems in multilayer channels.