mpw-005 / HVNTM High Volt. N-tip

the HVNTM shapes of all of mpw-005

40 of 40 dies rendered.

549,793 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (9.6 MB in total, largest 244.7 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-005_HVNTM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-005_HVNTM_reticle.png
the stitched reticle, full resolution
5.3 MB c2142126e1b9a3bdf9d53edabde1e909b925253005349f9fba44cdf44b865a973c18368b c95f53e58a227004982a6b2e3db49ff37e5e88f585933660a42c34db03475fc5
mpw-005_HVNTM_reticle_overview.png
the same image scaled to fit 4096 px
366.7 kB dc495f375f7a9784454f3a032c15d33c475705387775762c8a0f5fc8577df6f44797cfad 221b445b9407bfbef4566ea22849c16af924e1075e8c2c3409e34ea0bc523ddc
mpw-005_HVNTM_tiles.json
where every tile belongs, and its checksum
13.6 kB 35219f742b9c08ffaa7e5c4c6745d4a7885bb714fd636095c288db856022733ce405af99 5af370d5e3684a7e28cff71c4255bcbd30ded27a100476d224b847045e3ae1a4
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
A2 mpw5_cache 7576×107767576, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B2 microwatt_mpw5 7576×107767576, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B3 armleo_gpio_mpw5 7576×1077615152, 10776 228.5 kB813ca8232c5f7331106a71be98f611349db0a8914a67219b17e34fd9e80674ce
B4 fabulous_efpga 7576×1077622728, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B5 i2c_controller 7576×1077630304, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B6 lbist-mbist 7576×1077637880, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 240.6 kB5d5e4f2138eba6afdcdac07a4a29f84543f581f864e843594654546cede2812d
C1 riscduino-qcore__q0_ 7576×107760, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C2 high_speed_vco 7576×107767576, 21552 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
C3 openfasoc-cryo-gen 7576×1077615152, 21552 229.8 kB4e56673dff86af3d063327f7c9f5ac61d22f5c67bb5f9ceb7efe0e8d34580eec
C4 microwave_signal_generator 7576×1077622728, 21552 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 241.1 kB1c704e5ebd646b08a6d9c1b8173d40cce71406cb270355bda4dd3311b3fa3b19
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 225.1 kB2b8c94e2429bdae3a948f184b0231f9c0a1b6559cd6d7ce3cf8e8285743c4abb
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
D2 azadi_iii 7576×107767576, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D3 tia_for_physic_experiments 7576×1077615152, 32328 221.7 kB5cb52fea0ea1bea0694382e09db070a14346ebec6052a4a68ca8737e87b66037
D4 qf105 7576×1077622728, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D6 junga_soc_mpw5 7576×1077637880, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D7 nand_flash_mpw-5 7976×1077645456, 32328 240.7 kB377ac2f824bcb52d52e75344a942a0fa79aa1884dec19bc3dcc7c3593ae52fc9
E1 kasirga-c0-mpw-5 7576×107760, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 236.8 kBd608a8271746557526d9bd17433fa3a26578f6a441477b4968f0ce043b05a37a
E3 wirelessenergyharvesting 7576×1077615152, 43104 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
E4 caravel_raster_engine 7576×1077622728, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E7 alu 7976×1077645456, 43104 240.6 kB5d5e4f2138eba6afdcdac07a4a29f84543f581f864e843594654546cede2812d
F1 4ft4 7576×111760, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F2 rng_chaos 7576×111767576, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F3 uetrv-ecore 7576×1117615152, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F4 systolic_array 7576×1117622728, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F5 reram_test 7576×1117630304, 53880 224.4 kB1aeb0ec3f8dbcd5ff7edb57acf8f22d75a7d5406203bbe15e7fbb12a897f1e9b
F6 secure_memory 7576×1117637880, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 244.7 kB6568eda406f0ddf8e8670bca395669760c37ec349951e361c2004d6ced55c03d

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About HVNTM on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask HVNTM

Mask layer
HVNTM — High Volt. N-tip
Process step
68 — HV N-tip mask formation
Drawn layer
125:20
Derived from drawn layers as
125:20 OR ((65:20 AND 93:44 AND 75:20) NOT 81:2)
Layer on the mask-layer GDS
39:0
Detail
drawn hvntm merged with a created part (ndiff AND hvi, outside areaid.ce); only the drawn part is rendered
Used in SKY130
yes

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.